Key Functions with High Impact

Generative AI is revolutionizing sales by enabling dynamic pricing and personalized customer interactions, boosting conversion rates and customer satisfaction. AI chatbots are increasingly capable of handling tasks traditionally performed by inside sales reps, such as initial customer contact, basic inquiries, and lead qualification. This shift allows business to reallocate human resources to more complex and strategic roles, or eliminate those positions entirely. Post-sale, AI analyzes customer data to improve service and loyalty, making it a cornerstone of modern sales methodologies. This AI-centric approach transforms sales into a data-driven field, emphasizing efficiency and personalized customer experiences.

Similarly, in customer support, AI-driven chatbots and automated response systems are taking over routine support, effectively handling common issues such as account inquiries or basic troubleshooting. TechEmpower has been instrumental in developing chatbots like these, utilizing generative AI to sift through internal documents and user manuals, enabling them to provide precise answers to customer service questions. This level of automation not only improves response times and consistency in customer service but also allows human customer support agents to focus on more complicated and nuanced customer interactions.

At TechEmpower, we are using LLMs, RAG, fine tuning and other Generative AI techniques to  revolutionize a key part of day-to-day operations in healthcare. The standards in healthcare dictate that we achieve reliable results. Working closely with world-class medical experts, we have created an innovative solution that achieves accuracy and can be tailored to particular medical practices. The result significantly lightens the workload for healthcare professionals, allowing them to focus on decision making and patient care.

AI empowers businesses to craft more impactful marketing campaigns by utilizing data analytics for content personalization and market trend forecasting, thereby significantly enhancing campaign relevance and effectiveness. Instead of just counting clicks, AI can analyze a range of factors like user engagement duration, the relevance of ad placement in relation to the content being viewed, and historical purchasing behavior of the viewers. The shift towards AI-driven ad technologies enables brands to set and achieve highly specific engagement KPIs, moving away from generic strategies to more personalized, data-driven approaches that resonate with their target audience. At TechEmpower, we’ve used LLMs as part of marketing strategies where you can find and classify companies, personalize outreach campaigns and have personalized drip campaigns.

In the sphere of software engineering, AI is pivotal for corporate IT by automating coding, optimizing algorithms, and enhancing security to boost efficiency and minimize downtime. It plays a crucial role in product development too, where generative AI speeds up design processes, streamlines testing, and tailors user experiences effectively. This technological integration into software engineering not only enhances the productivity of development teams but also ensures that IT infrastructures are robust and reliable. By automating routine and complex tasks alike, AI allows engineers to focus on innovation and strategic tasks. Overall, generative AI is a transformative asset in the software engineering lifecycle, from conception to deployment. At TechEmpower, we’ve used generative AI across a wide range of capabilities for ourselves and our clients. This includes: Github Copilot, PR summarization, user story creation including test and edge cases, creating unit and behavior tests, query optimization, debugging, and more.

In the domain of Product Research and Development (R&D), generative AI acts as a catalyst for innovation, significantly accelerating the ideation and creation phases of product development. By processing and analyzing large datasets, AI can identify emerging trends, enabling companies to align their product strategies with future market demands. It also facilitates rapid prototyping, allowing for quicker iterations and thus shorter development cycles. In testing, AI can simulate a multitude of scenarios, predicting performance outcomes and potential failures before they occur, which reduces the risk and cost associated with physical prototyping. Overall, generative AI in product R&D not only streamlines the development process but also empowers companies to lead with cutting-edge, data-driven products.

Other Notable Functions

Generative AI is poised to revolutionize supply chain management by enhancing demand forecasting, enabling businesses to anticipate market changes and adjust inventory accordingly. It can also optimize logistics through route and delivery scheduling, leading to reduced operational costs and improved delivery times. In manufacturing, AI facilitates the transition to smart factories by implementing predictive maintenance, which minimizes downtime, and by optimizing production lines for increased efficiency and reduced waste. These advancements allow for a more resilient and responsive supply chain, as well as a manufacturing sector that can swiftly adapt to new challenges and opportunities, thereby driving substantial corporate impact.

In corporate finance, generative AI is a transformative force, enhancing decision-making and operational efficiency. AI’s prowess in detecting and preventing fraud provides an added layer of security, safeguarding assets and transactions. Moreover, it automates routine tasks such as transaction processing and report generation, freeing finance professionals to focus on higher-level strategy and analysis. By integrating AI, finance departments can achieve greater accuracy, efficiency, and risk management, significantly impacting the overall financial health and strategy of a corporation.

AI can significantly aid Human Resources (HR) departments in reducing costs through various means. It can be used to quickly scan and shortlist resumes, reducing the time and resources spent on the initial stages of the recruitment process. This not only speeds up hiring but also lowers the costs associated with lengthy recruitment cycles. AI-driven platforms can also streamline the onboarding process, providing new hires with personalized learning paths, thereby reducing the need for extensive HR personnel involvement and ensuring quicker employee ramp-up.

Incorporating AI into Corporate Legal departments can significantly reduce costs and enhance efficiency. AI-driven document review and analysis expedite the handling of large volumes of legal documents, contracts, and case files, saving considerable time and labor costs. Contract management is streamlined as AI systems monitor contract lifecycles, ensuring compliance and mitigating risks of costly oversights. Predictive analytics offered by AI can inform legal strategies, aiding in the decision-making process to avoid unwinnable cases and focus resources effectively. Additionally, AI facilitates automated legal research, stays abreast of the latest laws and regulations, and aids in compliance monitoring, preventing expensive legal violations.

While legal departments must be cautious in their use of AI, ensuring that it complements rather than replaces the nuanced judgment of experienced legal professionals, the benefits are substantial. AI-powered tools can handle routine inquiries and draft standard documents, freeing up legal staff for complex tasks. In litigation, AI greatly improves the efficiency of the e-discovery process. The overarching impact of AI in corporate legal settings is a more streamlined, cost-effective department, where resources are allocated strategically and the risk of legal missteps is minimized.

Conclusion

Generative AI is revolutionizing the way TechEmpower enables corporate innovation and efficiency across a multitude of sectors. By automating routine tasks, enhancing data analysis, and fostering personalized strategies, this technology is a strategic asset driving our clients towards a future marked by greater efficiency, cost-effectiveness, and innovation. We utilize generative AI to provide cutting-edge solutions across various domains, establishing TechEmpower as a leader in leveraging AI to deliver tangible benefits and drive progress for our clients.

And if you’re like most people, you’ve also probably struggled to fill those boxes in.  Everyone struggles with empty text boxes. Populating them can be hard work, especially when the content needs to be just right.  This isn’t just our opinion – our startup metrics prove it!  Even with the pop-up suggestions, we saw significant drop-off during user onboarding.  Drop-off on the first page of an application is bad news.  It means wasted advertising spend and lost goodwill.

The point is, empty textboxes aren’t just intimidating, they can significantly impact user engagement and conversion rates. On a different project, we’d just used a Large Language Model (LLM) – in this case OpenAI’s GPT – to provide users with pre-filled text boxes, with content based on choices they’d previously made.  Instead of an empty box, the user gets one filled with content to use as a jumping-off point.  If they like the content as-is, they can keep it; if not, they can change it.  Either way,  it’s a huge improvement over starting from scratch.

Applying that solution to our job matching site made perfect sense.  In fact, it makes sense for almost any application that has text boxes!  Leveraging LLMs to help users fill in text, whether it’s by providing starter content, samples, or highly personalized tips, makes their lives easier.  That’s why we’re planning on building most of our sites this way in the future.

Bottom line: with LLMs, empty text boxes are going away.

Profile Blurbs and Writing Prompts

Let’s look at our job matching site in more detail.  During signup, the professional is prompted to enter their profile into a form, with an upload box for a resume, fields for awards, skills and certifications, and then a textbox – 500 characters max – for their professional summary.

An empty textbox, demanding to be filled with a concise, compelling summary to impress potential employers is daunting. Fill it with the right words, and your dream job could be right around the corner. Get it wrong, and you’re just wasting fifty bucks a month.

For example, let’s consider Mark. He lives in Houston, he’s a dedicated math teacher, and a proud recipient of the Teach for America excellence award. He’s a right-brain guy, and not much of a writer, which makes an empty textbox a potential stumbling block. So instead, we fill it with a completely custom blurb, written just for him:

That blurb, and the following examples, were all generated from GPT in only a few seconds, at a cost of less than one penny. The information provided was all pulled from data he’s already entered – just Mark, Houston, Math Teacher, Teach for America.

And if this description doesn’t resonate with Mark, he can ask for a new one, while providing feedback.  Maybe those references to TFA sound like bragging, or he thinks “passion for numbers” sounds silly.  We could prompt Mark to enter descriptive keywords like “dedicated” and “engaging.”  This gives Mark more control over the process, without requiring him to write much, and gives the LLM more to work with.  Giving this feedback to GPT gives us a revised prompt – once again, in just a few seconds:

Thanks to his custom blurb, instead of closing his browser and feeling inadequate for the rest of the day, Mark completes his bio and becomes a happy customer.  He’s happy because the hardest thing he had to do all day was done for him – in a way that allowed him to maintain control of the output.  The LLM didn’t just write a blurb for Mark, it wrote the blurb that Mark would have written, if only he’d had the words.  Note how this version incorporates Mark’s feedback and descriptive keywords.  It’s this collaboration between the user and the LLM that drives good results.

In the pre-LLM era, an empty textbox was a tough challenge.  Now that AI is here to help, every textbox is an opportunity to help a customer through the challenge. Which means better conversions and happier customers. Happy customers mean happy CEOs and founders – and that’s what makes us happy.

One of the main powers of LLMs lies in their ability to generate text that not only makes sense but is also engaging and personalized. They learn from millions of pages of text, understanding patterns, context, and even nuances that make the generated blurbs feel almost human.

If having an LLM write the whole blurb seems like too much, we could also fill our empty textbox with a writing prompt:

Along with personalized tips that inspire creativity:

• Inject Professionalism with a Touch of Fun: Mark, as a dedicated math teacher, you likely have engaging stories from the classroom. Share an anecdote that showcases your innovative teaching methods. This adds a touch of personality and makes your bio more memorable.
• Be Genuine: Emphasize your genuine passion for teaching and your commitment to education. As a proud Houstonian and a teacher, highlight your connection to your community and your ongoing efforts to improve the world around you.
• State Your Career Goals: You’re passionate about guiding young minds in mathematics. Clearly mention that you’re seeking a role where you can continue influencing students’ lives and making a difference.
• Skills and Interests: Consider adding personal skills and interests.  Do you participate in professional development workshops, or perhaps enjoy problem-solving puzzles in your downtime? Including these details can provide a more holistic view of your professional persona.

Since LLMs can understand text as well as generate it, the tips could be interactive as well as personalized.  As Mark types, the text could be sent to GPT; if it determines that he’s added a teaching anecdote, a check mark would appear next to that tip.

Fill Every Textbox

Bio blurbs are a difficult sort of textbox to fill in.  But there are others almost as hard.  Think about text boxes that have stymied you in the past.  Look around your home or your office.  (If you’re remote, use your imagination.)  Do you see anyone frustrated by these empty textboxes? Off the top of my head, here are some examples, but the list is huge:

1. E-commerce product descriptions and FAQs
2. Real estate listings
3. Social media posts
4. Marketing emails
5. Job postings
6. Job application cover letters
7. Personal statements for college applications
8. Business proposals
9. Customer support emails
10. Press releases
11. Dating profiles

All of these text boxes are difficult to fill in, though not necessarily for the same reason.  Product descriptions and listings require tedious editing to make them engaging – especially after the fortieth one. Customer support emails require both accurate information and a professional, helpful tone.  An LLM can help fill in all these boxes, either directly, or by providing prompts, tips, and editing help.

Are you a founder or CEO or head of product? Go through your product right now and look for empty text boxes. You might be surprised how many there are.  Each one represents a great opportunity to make your users more productive and happier.  A solid LLM integration can transform the way they interact with your platform. It can empower them to express their ideas more effectively and confidently, no matter what they’re writing.

TechEmpower can help

In the era of LLMs and Generative AI, empty textboxes are a product mistake. Instead, you can enhance the user experience and produce a better result by enabling the user with a combination of the right questions and a starting point. The result is less drop off, i.e., better conversion rates.

But there’s a big difference between an LLM implementation and the right implementation for you.  To get to what’s right for you, you need a tech partner with a deep understanding of your business needs, software development experience, data engineering skills and AI expertise.  With over 25 years of experience in the software industry – and many successful AI integrations under our belt – TechEmpower can help you replace those empty textboxes with happy customers.

Real-World Examples

Here are a few examples of search improvements where we’ve leveraged LLMs for improved search capabilities:

Streamlining Internal Documentation Access

Our team addressed the all-too-common challenge of scattered internal documentation by creating a tool that allows for fuzzy search across documents, with the ability to pose natural language questions, receiving intuitive natural language answers that might not be directly present in the documentation. Utilizing AI, the tool interprets the queries, scans through a broad range of documents spread across different repositories, and delivers relevant answers. This enables internal users to swiftly find answers to queries such as “What is our internal IP address?” or “How can I modify my direct deposit account?” This has significantly reduced the time team members spend searching for information, enabling them to stay focused on their core tasks.

Enhancing Front-End Product Search

We also developed a front-end search tool that revolutionized the way users search for products. While traditional search systems are bound by the constraints of keywords, fields, and specific taxonomies, this AI-powered tool embraces the concept of fuzzy searching. Rather than matching exact terms and requiring specific field values as inputs, it interprets and understands user queries, pinpointing the desired types or categories. Even if certain categories aren’t predefined in the database, users still receive relevant product suggestions. For instance, if a user searches for “low carb options,” even though “low carb” might not be a category saved in the database, the LLM can still match and suggest “keto” products, understanding the user’s intent. This advanced approach greatly enhances the user experience, making product discovery more intuitive.

Content Recommendations in Streaming Services

While many streaming platforms suggest content based on viewing history, LLMs have the power to significantly elevate this experience. If a user searches for “movies similar to Inception with a twist ending,” the LLM can delve into its vast knowledge of movie themes, plots, and reviews, and generate a curated list of recommendations that might not be explicitly tagged in the platform’s database. This nuanced approach can cater to the most specific of viewer moods and preferences, ensuring a seamless entertainment experience.

Recipe Suggestions for Culinary Sites

For culinary platforms or recipe websites, user searches can be incredibly varied, given the diverse range of ingredients and dietary preferences. With an LLM-driven search tool, when a user inputs specific ingredients they have on hand or mentions a particular dietary restriction, the platform can craft unique recipes that aren’t just pulled from a database, but are generated in real-time. An example of this would be: “carrots, chicken, and bok-choy.” Because those ingredients didn’t match an existing saved recipe, the LLM generated:

Quick Carrot, Bok-Choy, and Chicken Sauté

Ingredients:

• 1 medium-sized carrot, julienned
• 1 bok-choy, roughly chopped
• 1 chicken breast, diced
• 2 tablespoons soy sauce
• 1 tablespoon vegetable oil
• 1 clove of garlic, minced
• Salt and pepper to taste

Instructions:

1. In a skillet, heat the vegetable oil over medium heat. Add the diced chicken, seasoning with salt and pepper. Cook until browned.
2. Add the minced garlic to the skillet, sautéing briefly until fragrant.
3. Introduce the julienned carrot and chopped bok-choy. Stir-fry for 3-4 minutes, or until the bok-choy is slightly wilted.
4. Pour in the soy sauce, stirring well to coat the chicken and veggies.
5. Serve hot with your choice of side, or enjoy as is for a light meal.

So let’s summarize what we’ve already found to be different about searching with LLMs:

What’s Different About Search with LLMs

Search methods powered by LLMs stand out as transformative tools, offering businesses an edge in their information retrieval and customer engagement processes. One of the most compelling features of LLM-driven search is its ability to perform “fuzzy” searches as opposed to the rigid keyword match approach of traditional systems. In layman’s terms, while conventional search mechanisms demand exact phrases or keywords to return relevant results, LLMs can understand and interpret the intent and context behind a query. This means users can pose questions or enter queries in a more natural, conversational manner without being limited to specific keywords. The results are not only more aligned with user intent but also often more comprehensive.

Moreover, LLMs come equipped with an extensive knowledge base derived from the vast amounts of data they’ve been trained on. This expansive, and ever-increasing knowledge base allows them to provide insights, answers, and context that may not even exist in a business’s specific dataset or repository. For businesses, this means tapping into a broader informational spectrum without the need for manual data entry or updates. When integrated into business search tools, LLMs can drastically reduce the gap between user queries and the most relevant, context-rich results. In essence, an LLM-powered search doesn’t just fetch data—it understands, interprets, and often augments it, providing businesses with a dynamic tool that continually adds value.

As we dive deeper into the capabilities of LLMs in search, it’s essential to have a clear understanding of the kind of data these models deal with. Broadly speaking, data can be categorized as either structured or unstructured.

Structured Data refers to information organized in a defined manner, making it easier to search and analyze. This data is typically arranged in rows and columns, akin to what you’d find in databases, spreadsheets, or CSV files. Such a format is convenient for traditional search methods, where specific fields can be queried directly.

Unstructured Data, as the name suggests, lacks a clear structure. This category encompasses a vast array of content, from emails and text documents to social media posts. Searching through this data isn’t as straightforward due to the absence of predefined fields or categories, which is exactly where many conventional search systems fall short.

Given their training on diverse datasets, LLMs excel in parsing and understanding unstructured data, providing contextually relevant results. Where traditional search methods might stumble, LLMs can traverse this complex landscape, delivering insights from unstructured data sources with the same, if not higher, efficiency as they do with structured ones. This proficiency ensures that businesses can unlock the full potential of all their data, irrespective of its format.

The other really interesting aspect of search with LLMs is the range of possibilities it enables on the generative side. Beyond merely retrieving relevant information or documents based on a query, LLMs can generate entirely new content or responses tailored to a user’s specific request. These enhanced capabilities transform the search experience from a passive retrieval process into an active, dynamic interaction. For instance, if a user seeks advice on a particular topic, instead of just presenting pre-existing articles or references, an LLM can synthesize its vast knowledge base to produce a coherent, contextually apt, and unique response on the spot. Such generative prowess can be especially invaluable for businesses aiming to offer real-time solutions, personalized advice, or innovative content suggestions, ensuring they remain a step ahead in delivering unparalleled user experiences.

LLM Search Techniques

The following are some terms you are likely to hear as you dive into search solutions using LLMs:

RAG (Retrieval-Augmented Generation): RAG queries proprietary data and provides those as part of the prompt to fuse relevant documents with the LLMs knowledge base. In other words, first, potential answer-containing documents are fetched. Subsequently, a generative model crafts a detailed answer using the retrieved data. This is table stakes for the kinds of solutions we are describing above.

Initial Query Refinement: Initial query is the starting point in any search process where a user provides an initial query to the system. The quality and specificity of this query can significantly impact the success of the search. Following the initial query, there is often a need to refine the query based on user feedback or additional contextual information to narrow down or better direct the search towards the desired information.

Multiple Passes: This involves making several passes over the data to iteratively refine the search results. Each pass may use the information gleaned from the previous ones to improve the accuracy and relevance of the results.

FLARE (Forward-Looking Active Retrieval augmented generation): FLARE uses the prediction of upcoming sentences to anticipate future content. This anticipated content then serves as a query, guiding the retrieval of pertinent documents. It’s a proactive approach, ensuring that the search mechanism stays a step ahead.

Our expertise lies in navigating through these various techniques in order to determine the best solution across a variety of situations.

Ensuring Accuracy: How to Test Results

Upon deploying AI-driven search tools, validating their accuracy is paramount. Here’s a suggested approach:

Ground Truth Creation: Design a test dataset with established answers or recognized documents to serve as a reference point.

Precision and Recall Metrics: Define a process to assess the precision (the relevance of retrieved documents) and the recall (how many of the relevant documents were fetched). Strive for a balanced outcome.

User Feedback: Enable users to grade the relevance of search outcomes, offering invaluable real-world insights.

A/B Testing: Run the AI-enhanced search solution concurrently with a conventional one, juxtaposing their real-time performances.

Ongoing Monitoring: Continuously gauge the system’s efficacy, making ongoing, iterative adjustments based on evolving data sets or user preferences.

How to Proceed

Our experience has shown us that these initiatives begin with a company (a) reviewing existing search solutions to see what users like and don’t like about them, or (b) looking at proprietary data that they would like to be able to search, or (c) finding places where users would like answers that could be fueled by a combination of an LLM and proprietary data. These all become potential targets that you can evaluate for feasibility/level of effort as well as short- or long-term value. The next step is determining your businesses’ priorities. We often work with clients both prior to, and during these discussions.

With the priorities determined (i.e., high value/low effort), the next step is a Proof of Concept (PoC). This is an early version of the solution that can be expanded over time. Implementations will tend to grow more expensive as you improve them by iterating on prompts, using more advanced techniques and adding testing. However, you often get a 70% solution pretty quickly through a Proof of Concept – and it’s often already better than the existing search. A PoC is a pragmatic step to evaluate the feasibility and results. It gives stakeholders an opportunity to assess the benefits and challenges before making additional investment on iterative improvements.

Assuming you do decide to improve the Proof of Concept, then typically you iterate using an agile process. We recommend that you utilize the steps outlined in the “Ensuring Accuracy” section to validate as you iterate so you can closely monitor the progress being made. Through this iterative process, you’ll be able to address quality issues and optimize results.

Wrapping Up

The era of simplistic keyword searches is rapidly changing over to the advanced capabilities of AI and LLM-driven search techniques – often starting with a parallel implementation of the two – see Google and Bing as prime examples. With the capability to do fuzzy searches across structured and unstructured data and generate different kinds of responses, the results speak for themselves. Even better, initial implementations are often affordable and relatively fast.

As with everything involving LLMs and Generative AI, this is an area that is advancing rapidly. RAG is well established, but methodologies like FLARE and query refinement offer exciting opportunities to make this increasingly powerful. Mechanisms for ensuring accuracy are also improving quickly.

It’s a good question. Lack of confidence in a dev team can be caused by any number of factors, including:

1. The dev team is, in fact, weak.
2. The team’s technical skills are solid, but they’re undermined by poor communication, especially around requirements and expectations.
3. Past failures, e.g., missed deadlines, bugs, or downtime make it impossible to reestablish trust. This can be true even if those failures had nothing to do with the current team.

But how do you know which? If you’re grappling with this issue, identifying the specific cause can be difficult, especially if you don’t have a software background. (This is where a technical review can be useful!) Don’t worry – we can help. In this post, we’ll show you how to identify common signs that a dev team isn’t performing as expected, even if you’re not that technical.

Before we review the symptoms, though, please bear this in mind: If your team shows these signs, it doesn’t necessarily mean they’re weak. It means that you – or someone you trust – need to dive in to figure out what’s going on.

The Founder-Developer Gap and A, B, C Players

The challenges that business leaders face when assessing development teams are a good example of the Founder-Developer Gap. The fact is, developers operate in a world that outsiders can’t easily understand. It’s hard to know if a developer is an A, B or C player, or a player at all. And in the software world, an A player is worth 10+ C players!

Unfortunately, there are a lot of C players out there. When we interview potential developers, we’re always amazed at how many can’t answer basic programming questions. It makes us wonder, how did these folks graduate from their CS program or their bootcamp? And how did they build their impressive resume? (Maybe with ChatGPT!)

The Symptoms

Here are the red flags we hear about most often. These are the worries that keep team leads up at night. Knowing how to spot these signs can help you keep your business on track.

1. Missed deadlines.
2. Last minute scope-cutting to avoid missing deadlines.
3. Delivery of code that has clearly not been tested.
4. Marking bugs as fixed that aren’t fixed.
5. Racking up massive overtime.
6. Lack of communication between developers.
7. Dev teams without a clear leader.
8. Rogue developers with their own agenda.
9. Private bits of code that are jealously protected by a single dev.
10. Shifting blame and finger pointing.
11. Fixing one bug breaks something else.
12. Developers seem unconcerned about bugs or system downtime.
13. Developers become annoyed at testers for finding bugs.
14. The same bugs/problems occur over and over again, and no one wants to find the source of the problem.
15. New features always require significant rewrites, and consequently a lot of time.
16. Developers can’t explain why changes will require more or less development time.
17. During team meetings, developers are quiet when bugs, features, changes are being discussed, only to come back with questions later.
18. Rapid turnover, especially of senior or “A” developers.
19. Developers aren’t aware of the progress of the current dev cycle, or even what’s in it.

And the #1 symptom relates to that old software engineering adage:

From a CEO’s perspective, this translates into:

If you’re seeing some of these symptoms, you may have a weak development team. But to repeat our previous warning, you might not! There may well be other problems keeping your team from being effective. To find the answer, you’ll need a deep-dive analysis. But it’s best to start with a phone call for a quick reality check – and we are happy to do that with you.

What Makes a Team Strong?

It’s useful to approach this problem from the opposite direction. What are some characteristics of a strong team?

A strong development team should have the following:

• A high service level and availability of their product/system.
• A high throughput of effective change.
• A low amount of unplanned work.
• A culture of change management.
• A culture of continual improvement.
• And a culture of root-cause analysis.

If your team shows these characteristics, then make sure they know they’re appreciated! And don’t be surprised if you see some weaknesses and some strengths. That’s to be expected.

Recovery is Extremely Hard

Software development is challenging. Asking the right questions during requirements gathering – which is essential – doesn’t come naturally to most devs. Edge cases are easy to miss, even for experienced programmers. Aggressive timelines and pressure from management create plenty of opportunities to introduce bugs.

Early failures by a development team can make it difficult – next to impossible, really – to recover trust. If your co-workers or your manager think you’re doing a bad job, it’s very hard to overcome that perception.

Bottom line: If you have concerns about your development team, read the list of symptoms carefully. If you find yourself nodding your head in agreement, you might have a weak team.  And it never hurts to get an outside opinion!

Most innovators don’t have a technical background, so it’s hard to evaluate the truth of the situation. We hear them explain that their current software development is expensive, deliveries are rarely on time, and random bugs appear. The explanation from software leadership is often unsatisfying or unclear. And unless they have a tech background, they can’t look under the hood themselves.

What does a business leader do in this situation? The reality is that it’s not a hard problem to address. The answer is to engage a trusted outside source for a Technical Review – a deep-dive assessment that provides a C-suite perspective. At TechEmpower, we’ve conducted more than 50 technical reviews for companies of all sizes, industries, and technical stacks.

Technical Review Defined

A technical review is a deep-dive assessment of your software, infrastructure, team and processes. It provides findings and recommendations intended to foster a mutual understanding between business and software leaders, shedding light on the current state of your technology and your team. It also provides concrete recommendations for improvements.

Common Signs You Need a Technical Review

We’ve already mentioned the most common signs that you might need a technical review:

• Slow or late delivery: “I’ve authorized two new developers, and we’re still behind.”
• Random or persistent bugs: “The menu bar has been fixed three times now!”
• Sleepless nights from strategic worries: “Am I building the right tech, the right way?”

But everyone’s situation is unique. Your reasons for needing a technical review will depend on your business goals. Think ahead to tomorrow, to the next six months, and to next year. Are you preparing to expand, take your business in a new direction, or take on new partners? Even if you’re happy with your tech team, a review can ensure your business is in a good place before you take your next big step.

Other common scenarios that we’ve seen:

• Scaling and new markets: Your company and product are established, and now you’re scaling or going after new markets. These are classic inflection points for a development  team. It’s important to ensure that they’re on a strong footing as significant changes occur. Otherwise, the downstream technical debt created can be overwhelming.
• Keeping up with the competition: You’re a market leader in a fast-growing field. Newer, nimbler competitors keep introducing new features before your team can catch up. A technical review can help your development team adopt new processes and strategies to maximize their speed and efficiency.
• Outgrowing your stack: Your tech stack was just right for your startup. But as you’ve grown, it’s become cumbersome and constraining. Your tech team is suggesting a costly, time-consuming overhaul. But is that the right strategy for your business? A technical review can answer that crucial question.
• Changes to the tech team: You need to make changes to your tech team to meet your business goals. But what does the right team look like? A technical review can help you decide what team will work best both with you and for you.
• Due diligence prep: You’re coming up on a funding round or looking at a potential exit. Your investors and buyers will be doing a technical review. Are you ready? Do your own review first so you can be confident and better positioned.

There are almost as many reasons to do a technical review as there are software-enabled businesses. If you’re a CEO, product leader, investor, or a founder looking for investors, a technical review conducted by a neutral, experienced third party will help you discover what’s really happening and what’s needed to make improvements.

How it Works – The Review that’s Right For You

We know what a review can do for you. But only you know what you really need. That’s why it’s important to discuss your specific situation early in the process, ideally before the review even begins. This allows us to shape our approach to your unique needs. Then, at the start of the review, we speak with other key stakeholders to understand where they’re at and what they need.

So, how your review might work depends a lot on your situation – there’s your standard tech person caveat! 🙂  But at a minimum, any good review should include:

• Straightforward Analysis: A straightforward report that grades aspects of your technology, processes, and team. It’s written for a business leader, not an engineer.
• Pragmatic Assessment: An in-depth evaluation by working engineers who understand what it takes to deliver in different development situations. This part of the report can go deep on how your software stacks up with respect to feature delivery, security, reliability, and scalability.
• Expert Recommendations: Concrete next steps that working engineers believe would be appropriate to consider.
• Findings Sessions: A review meeting to decode findings, gauge their business implications, and chalk out a roadmap for the future.

Let’s look at the process in more detail. Here’s what a typical review looks like, step by step.

Background Information Review

Our first step is to review existing background materials. Our goal is to build a decent understanding of your business, your product, and your technology.

This might include business-side materials like your product roadmap, business plans, and competitor analysis, as well as technical materials like requirements documentation, screen comps, and project/process systems like Jira and Confluence.

Discuss Pain Points

To borrow the famous quote, all successful software projects are alike; every struggling software project struggles in its own way. To help you, we need to understand your specific concerns.  What’s keeping you up at night? Understanding your pain points shows us where to focus our review. We keep these pain points at top of mind throughout the review.

Questions and Conversations

We provide the tech and product teams with a list of questions that cover a wide variety of topics including software architecture, hardware/software technologies in use/planned, hosting, external services and dependencies, deployment strategy, development process and tools, testing approach, data science, security, and other relevant topics.

We then review the answers with the technical and product teams.

An interesting side note: During our Q and As with dev teams, we often end up having conversations around issues that they haven’t even thought about. It’s not because they’re inexperienced or incapable – it’s because they’ve been so focussed on shipping features that there hasn’t been time for anything else. This is exactly the sort of thing that a technical review can help address.

Here are some questions that can trigger those kinds of conversations:

• What areas of the applications are the most difficult to work on? And what causes that?
• What amount of time is spent on bug fixes and maintenance versus development of new features?
• How would your product perform if your user base were to scale up by a factor of 10? By a factor of 100?
• How are you monitoring your systems and applications?  Who responds to alerts?

Architecture Review

We meet with your tech team to review the overall architecture of your systems and applications. Our goal is to get a big-picture understanding of your systems, including how complex they are. We also begin to assess how well the architecture fits with your business needs.

Targeted Code Review

In this step, our senior engineers review the source code that drives your business. Does the code follow best practices? Are there repeated bugs or security holes? Our goal is to make sure your team’s code is appropriate for the situation.

Why a targeted review? It’s not practical, or even worthwhile, to examine every line of code. We provide value by targeting our review towards key components across your application stack.

Process and Team Review

It’s easy for development teams, especially startup teams, to cut process corners in the name of shipping features. However, short-cuts on process can lead to problems downstream. It can also lead to a software team that looks ill-equipped.

To head these off, we start by reviewing your team’s overall methodologies, and then drill down to details like source control workflow, ticket management, and code review processes. This step helps us identify why development priorities get stuck or abandoned, determine if the right people are working on the right tasks, and identify development hurdles and roadblocks.

This step can include a review of each team member’s contributions, down to individual commits. If you’re concerned that some of your developers are not allocated wisely, a contribution review can help.

We also assess the makeup of your dev team. We check out the team’s makeup, skill sets, and past work to ensure they align with what your business needs.

Findings and recommendations

This is what you’ve been waiting for! We provide you with a report, written for a business audience, that includes:

• Executive Summary: We grade your architecture, source code, process, and teams, and provide a brief rationale for each grade.
• Detailed Findings: For each of the areas in the executive summary, we provide the details. Honestly, this often doesn’t get more than a skim from business leaders, but certainly gets a thorough read from the technical team.
• Recommendations: Specific recommendations based on your pain points, the findings and what we believe are pragmatic choices for your business.

Review Meeting

Once the business and technical teams have had a chance to review the findings report, we conduct a meeting, or series of meetings, to discuss the results and next steps.

Getting Buy-in from Leadership

Business leaders are often concerned about how their tech teams will react to an external technical review. Will they resent scrutiny from the outside? Will they refuse to cooperate or push back?

While this can be a challenging conversation, it’s not as hard to get technical leaders on-board as you might think. Let’s assume that you’ve already expressed your frustrations with technical leadership. In our experience, the tech teams generally welcome the review. They’re often frustrated by the same problems that trouble the business team – or different problems that the business team doesn’t even know about, but should. They are happy to share their concerns with us, and generally agree with our conclusions. After all, they want you to succeed too!

It’s important to get buy-in from product leadership as well, so we make sure they are also involved in the process. That said, we find that most of the time they are expressing the same concerns as business leaders and getting their buy-in is much easier.

All of that said, we highly recommend discussing your specific situation with us prior to getting too far into the conversation. It’s easier to secure buy-in if we know where everyone stands.

Why TechEmpower?

Choosing the right partner for a technical review is important. You need a team who’s well-versed in technology and capable of translating that know-how into pragmatic options. It’s also important that they have experience in technical evaluations and in building real-world systems. We can’t emphasize that last part enough! If you bring in a consultant who spends all their time doing technical reviews and hasn’t built a system since the dot-com boom, you’re probably wasting your time.

At TechEmpower, we’ve done over 50 technical reviews for budding startups, scaling enterprises, discerning investors, and ambitious companies seeking investment or acquisition.  Our professionals bring expertise, neutrality, and depth to the process, and we approach each review with a unique blend of technological prowess and leadership acumen.

Bottom line, if you’re a CEO or founder, ask yourself these questions: Am I getting my money’s worth from my development team?  Do I really know what’s going on under the hood?  If the answer isn’t an unqualified yes, then email us to discuss a technical review.

The answer is: it depends.

The role of a CTO varies as the company matures. Here’s a graphic from Socal CTO that illustrates the roles as they change over time:

In its earliest days, a startup’s top need is often to produce a product. Getting something to market and getting funding override any other concerns. That’s why the CTO’s attention is on programming for the earliest stage.  

But be careful, and mind the gap – the Founder-Developer Gap, that is! Hiring a hands-on lead developer might seem like the right move for an early stage startup. It’s understandable – a hands-on developer can produce a product. But hiring a lead developer, or even a VP of Engineering, can create a gap between the founders and the developers.  

Founder-Developer Gap

How big is your startup’s Founder-Developer Gap? Is it a tiny crack, or a widening chasm? And how can you tell?

One sign of a gap is questions – really, a lack of questions – coming from your developers. Check out our blog post 53 Questions Developers Should Ask Innovators. If you’re not hearing those questions from your developers, you’ve got the gap.

Often, developers don’t think to answer these questions. Instead, given a startup project, they’ll default to building everything in-house, using technologies that they’re already familiar with. This is a safe choice, of course – but is it the best choice? Is it the best path to profitability?

It might be. But you shouldn’t rely on the defaults. A CTO can help you find the right answers.

Closing the Gap: Hiring A Fractional Startup CTO

We’re not suggesting that early-stage startups should hire a full-time CTO. Instead, they should consider a Fractional CTO who can help close the gap. This is especially true when a founder has a strong vision but limited knowledge of the technology needed to make it a reality.

Bottom line – if you recognize this gap, then reach out to get a slice of a CTO who can help bridge the gap.

Can the Right Lead Developer Address This?

Theoretically, an outstanding lead developer can provide good answers to all these questions.  But our experience suggests it doesn’t usually work out that way. And even if you prompt a good lead developer to answer, you’ll likely still have a gap. This is because the lead developer will often be:

• Limited by their experience and breadth of knowledge
• More interested in diving in to build things
• Challenged to pull together a good team
• Motivated by the short-term because that’s the focus of development
• Tired at the end of a long day of programming and not interested in thinking longer term
• Uninterested in or unable to add much value to investor presentations, business proposals, and new business meetings

Again, it can be done. It’s just not a realistic expectation. You don’t want to be in the meeting when the investor asks why you didn’t use their favorite new technology – at least, not without a good answer! And you certainly don’t want to be the company that spends time and money building something you could have picked up off the shelf.

More on the Role of the Startup CTO

Eric Ries, a great resource, answers the question What Does a Startup CTO actually do?

• Platform selection and technical design
• Seeing the big picture (in graphic detail)
• Provide options
• Find the 80/20
• Grow technical leaders
• Own the development methodology

And here are some links that are less Startup CTO, and CTO more generally:

• Top Ten Goals CTOs Should Focus On in 2023
• 14 Essential Tech Blogs Every CTO Should Follow
• SoCal CTO
• What You Need to Know before You Start as CTO
• Mark Suster – Want to Know the Difference Between a CTO and a VP Engineering?
• Aspatore Inside the Minds Series, Leading CTOs
• Mark Minevich, The CTO Handbook
• Tom Berray and Raj Sampath,the Role of the CTO: Four Models for Success
• Werner Vogels – The Different CTO Roles
• Role of the CTO

If you know of more resources on this topic, we’d love to hear from you. Please write us at blog@techempower.com !

• Customer Acquisition Cost  (CAC)

How will your business reach prospects? How will you convert them? And how much will it cost to win them?

• Customer Lifetime Value (CLV)

How much money will your business generate from each converted customer?

These two questions/answers can help define the early proof points for your company. If you can demonstrate that your CAC and CLV numbers work as expected, then you’ve proven you can make money.  

Proving your Business Model Works – Build, Define, and Review

But how do you prove your numbers?  

Start by building just enough of your product to get early CAC and CLV signals (they won’t be perfect). Don’t worry about scaling just yet. If your numbers work out, then scaling becomes a question of capital. 

Next, define what you need from a metrics and reporting standpoint. Look at different customer acquisition channels, how they are converting, and the expected lifetime value of customers acquired through those channels. Apply costs to each channel. We need to make sure we have these numbers. Quite often the goal is to get them into a spreadsheet in a form that allows people to easily play with them.

Finally, review the numbers with your partners. Don’t overcomplicate things with reporting tools.  Just get your numbers into a Google sheet so you can work with them easily.

Startup Metrics with Dave McClure

Dave McClure has a great presentation on Startup Metrics where he points to some additional metrics that are useful to consider:

• A: Acquisition – Where / what channels do users come from?
• A: Activation – What % have a “happy” initial experience?
• R: Retention – Do they come back & re-visit over time?
• R: Referral – Do they like it enough to tell their friends?
• R: Revenue – Can you monetize any of this behavior?

The metrics, and how they relate, are captured in his slide:

Note the relationship between retention/referral efforts and lifetime value. This isn’t a simple, first-cut acquisition pipeline! Dave’s model provides a much richer understanding of what’s happening, but in a way that’s understandable and measurable.

Another thing that Dave has done well is to look at the value of different marketing channels:

There’s a lot of value in this presentation. Some key slides to check out:

The Startup Metrics Religion

• The Progress is not equal to features (Less is More)
• Focus on User Experience
• Measure Conversion; Compare 2+ Options
• Fast, Frequent Iteration + Feedback Loop
• Keep it Simple and Actionable

What’s my business model?

• Get Users (= Acquisition, Referral)
• Drive Usage (= Activation, Retention)
• Make Money (= Monetize)

Startup Challenges

• Management: Setting priorities, defining key metrics, reporting progress
• Product: Build the right features, getting product out quickly, testing for conversion/adoption
• Marketing: Accessing “web 2.0” channels (search, social, viral, new media), cost-efficient distribution

We often reference Dave’s work when talking to innovators. Be sure to check out the entire presentation.

Conclusion

Startup metrics are an invaluable tool for founders and innovators. Focus on building an MVP to gather startup metrics. Then use what you’ve learned in this blog post to decide if the numbers validate your business model, or if you need to rethink your approach.

If you know of more resources on this topic, or want to talk about anything startup or tech related, we’d love to hear from you. Please write us at blog@techempower.com !

The conversations are interesting and varied because they’re about new, exciting, different things. After all, that’s what tech innovation is all about. But there are some common elements too. One common element – and one that’s pretty surprising – is that often, no one has asked these innovators even the most basic technical questions.

Even when they have talked to multiple developers or development firms, we’re often the first to ask basic questions like “Who are your customers?” or “Are you developing for desktop, tablet, mobile, or all three?”

Of course, it’s more complicated than just checking boxes on a question list. The innovator/developer relationship needs to be a conversation. Still, if you’re a business leader and your developers haven’t asked you these questions, look for a Fractional CTO to help navigate the critical early stage of development.

Background Questions

Let’s start with some background questions about the business and product. Think of these as the big upfront questions a developer should ask to get an overall picture.

1. Who are the customers? What’s their specific need/pain? Can you provide specific examples of different types of customers, what they need, and what the system will do for them?
2. Tell me about the business. How are you funding this? What level of funding do you currently have? Who’s helping you with fundraising? Do you have legal (Founder Agreement, IP, etc.) in place?
3. What are your big milestones? Do you have any deals done or in progress that are tied to those milestones? Where are you today, and what’s happening right now?
4. What’s been done so far to validate the concept?
5. Who are the other stakeholders involved? Are there other founders, business leaders, partners, or administrators?
6. How will you be taking this to market? What channels will you use (e.g., Ads, Viral/Social, SEO)? Is anyone working with you on this?
7. What are your key Startup Metrics? How do you make your money? How do you measure success?
8. Who are your big competitors? What are some sites or companies in the same space? How will you differentiate from these?
9. What is different, special here? Where’s the mystery? Do you have a custom algorithm or other technology?
10. What special data, content, APIs, etc., will you leverage? What’s the state of the relationships that brings you that data? What’s the state of those systems?
11. Where do you stand on your brand? Do you have a name, a logo, and have you thought about brand positioning? What are some examples of similar brands?
12. Are there any specific hard dates or important time-sensitive opportunities?
13. What do you see as your biggest risks and challenges?
14. What are the key features in each major phase of your application? What functionality would make your company launch-ready?
15. What has been captured so far? Are there user stories? Mock-ups? Wireframes? Comps?
16. What problem is your product trying to solve?
17. If you launched tomorrow, how many users would you forecast? Six months from now? A year from now? How quickly will we need to scale the application?

Questions Developers May Have Forgot to Ask

Here are some additional questions that might have slipped your developers’ minds.

1. eCommerce

Does your startup run on a subscription model? How many kinds of subscriptions do you support? What are the rules for subscriptions? Do you support discounts? Free trials? Bundling? Coupons?

Often this ties to marketing support. For example, you might want to offer a discount to a given group to provide incentive.

2. Targets

Are you developing a native app and/or a web app? Are you targeting desktop, tablet, or mobile? Can you do a hybrid web/native application? Which devices will you test on specifically? Most new sites need to account for mobile delivery – but on the other hand, not every MVP needs both desktop and mobile versions.

3. Registration

Do you plan to support Google Sign-In, Facebook Connect, or similar 3rd-party authentication? If so, will you also have your own account system? Will you validate new members’ email addresses and/or phone numbers?

4. Artificial Intelligence

Does your application leverage AI in any way?  For customer service?  To personalize customer recommendations? How can we use AI to improve the customer experience?

5. Member Profiles

What data is included? Is there a step-by-step wizard? Can members upload their pictures? How much member profile information do you need before allowing a user to register?

6. Social Integration/Viral Outreach

Is your application tied into any social networks? How tight is that integration? Is it limited to login and Like buttons, or are you building a presence within the social networks themselves? What about other kinds of viral outreach?

7. Communication/Forums

Are there discussion forums? Commenting? Messaging? If you have boards or comments, do you support flagging? Moderation?

8. Social Interaction

Do users/members relate to one another? If so, how do they interact? Are users otherwise grouped by the system, maybe by background (employer, university) or preferences?

9. Internationalization/Localization

Do you anticipate an international audience? How important is support for multiple languages? For multiple character sets? How do we prioritize internationalization versus getting something to market?

10. Location/Geography

Is your application location-aware? Does it tap into geolocation services provided by the browser or rely on a third-party lookup table? How are you using geographic information? How does the application behave when location data is not available?

11. Gamification/Scoring

Does your application include any kind of scoring and/or gamification? Are there achievements and badges? Is there a leaderboard for users or teams?

12. Video and Audio

Are you hosting your own video, or can we use a third-party host like YouTube or Vimeo? Do you need to process user-contributed media? What about reporting and moderation?

13. Notifications

What notifications does your application need? Are they dismissable? Do they generate emails or push notifications?

14. Email / SMS

Does your application send out transactional emails or SMS messages? In mass? How are those mass messages crafted? How often is message content updated? Do you need to track views and bounces? What are your privacy rules?

15. AI Assisted Development

How can we use AI to speed up our SLDC?  How can we leverage AI to get our product to market faster?

16. Marketing Support

What does your application need to do to help with marketing? Do you have specific landing pages? What are your referral sources, and what tracking do you need around these sources? Do you rely on affiliates? Is there a need for A/B testing?

17. SEO Support

Will URLs and page content need to be properly formed for SEO? What back-end support for SEO is needed?

18. Content Management

How often will the application’s content need to change? Who will be doing the changes?  Will you need to add arbitrary new pages? Should content changes be scheduled? Are members contributing content or only system administrators?

19. Dates and Time Zones

Does the application need to support multiple time zones? Does it need to convert dates automatically?

20. Search

Does the application include search? What content is searchable? How advanced does it need to be?

21. Logging/Auditing

What key operations need to be logged for auditing? What needs to be logged for customer support?

22. Analytics/Metrics

What key startup metrics will you need to track? What metrics will you need for future funding rounds or operations?

23. Administration

What will you need to do from a back-end? Administer users? Send messages?

24. Reporting

What needs to be reported? Are CSV/Excel exports sufficient, or do you need something more? Reporting can be endless! Our advice: keep it small to start.

25. Accounting

Beyond reviewing transactions, what accounting support do you need? Do you need to track inventory? Fulfillment?

26. Customer Support

Do you need specific interfaces and support for customer service? Do you need a ticket system? What about an AI support assistant?

27. Security

What are the business / application’s specific security risks? Does it need to throttle potential malicious activity? This generally is a significant discussion itself! Our advice: be pragmatic!

28. Performance 

What is the expected request volume? What response time characteristics are required? How complex is the application? Complexity can directly impact performance.

29. Integration Points

What third-party systems will we need to integrate with? How far along are any integration efforts? What is the business relationship with the third parties? Who controls access to the third-party accounts, if any?

30. Existing Capabilities

What capabilities and personnel do you already have access to? Graphic design? UI/UX design? A Product Manager? How much availability do they have to work on this effort?

31. Hosting

What hosting requirements does your application have? Do you have any existing hosting relationships?

32. Platform

Are there pre-existing technical platform decisions that must be considered?

33. Team and Process

Are you using, or planning to use any software development methodologies? How big is the anticipated development team? How will it be structured?

34. Product Management

Do you have a clear vision of the initial application and a plan for sequencing changes after the initial launch? Do you have the internal staff to manage changes?

35. Compliance

What regulatory compliance do you need to support? GDPR? CCPA? HIPPA?

36. Expansion

What is your vision for the expansion of the application? What features should be in place at launch? Six months from now? A year from now?