The AI Reality Check
TechnBrains on AI-assisted discovery and measurable engineering work
Kazim Qazi is co-founder of TechnBrains, a software company that builds full products and supplies engineers to existing teams. The business stands out for bringing industry context into healthcare, construction, logistics, and real estate work before a client pays for the learning curve later.
The practical lesson is to measure AI by net effort, not output volume. TechnBrains uses AI on repetitive engineering tasks, but keeps human judgment in the work where context, empathy, and business understanding shape the result.
What’s the quick origin story of your brand, and what makes your product or positioning genuinely different from other options in your niche?
TechnBrains started with a simple idea: companies should not have to choose between outsourcing an entire product and hiring people who need months to understand the business.
Over time, we built around both needs. Today, we develop complete software products and also provide experienced engineers to strengthen existing teams.
What makes us different is the amount of industry context we bring into the build. In areas like healthcare, construction, logistics, and real estate, understanding the workflow, compliance requirements, integrations, and edge cases matters just as much as writing good code. We try to remove that learning curve early rather than making the client pay for it later through delays and rebuilds.
Where does most of your new business come from today, and where do you wish it came from?
A lot of our business still comes from a mix of referrals, existing relationships, and people finding us while actively researching a development partner online.
The leads I value most are the ones that arrive because someone has already seen how we think about a problem, whether through our content, case studies, industry research, or something another client has said about working with us.
Today, new business comes primarily through referrals, existing relationships, repeat engagements, organic search, and companies actively researching software development partners.
We are increasingly focused on attracting clients through demonstrated expertise rather than broad service discovery. That means case studies, technical research, industry-specific content, and evidence of what we have actually built.
The ideal prospect reaches out already understanding where we have relevant experience and wants to discuss the product or engineering problem, rather than starting with a comparison of hourly rates.
What’s one thing you’ve actually handed over to AI in the last year, and one thing you tried it on and went back to doing the human way?
Over the past year, I have led engineering teams working with AI agents and automation across development workflows, particularly where repetitive tasks were consuming time without requiring much judgment.
We have used AI to handle parts of code analysis, routine implementation, workflow automation, and other repeatable engineering tasks. The value has been in reducing manual effort and giving engineers more time to focus on architecture, product decisions, and complex problem-solving.
Where I would not hand control over to AI is anything that depends heavily on human context, personalization, or judgment. We have tested AI in areas where it could technically produce an acceptable output, but the result often lacked the nuance that comes from understanding the customer, the user, or the business behind the request.
That distinction has become important for us. AI is very effective at reducing repetitive work, but the human element still comes first when the quality of the outcome depends on context, empathy, or individual needs. We use AI to support the team, not to replace the judgment that makes the work relevant.
Have you noticed a change in how customers find you or what they already know before they reach out? Is anyone arriving through AI assistants like ChatGPT yet?
Yes. We have been tracking lead sources more closely, and referrals from AI assistants such as ChatGPT, Claude, and other generative search tools are becoming a more visible part of how prospects discover and evaluate us.
These leads also tend to arrive better informed. Prospects often come in with clearer requirements, preliminary technology choices, competitor research, budget expectations, and more specific technical questions than they did a few years ago.
Traditional search, referrals, and existing relationships still account for a significant share of our business, but AI-assisted discovery is no longer something we treat as theoretical. It is becoming a channel we actively monitor.
For us, that changes how we think about visibility. It is not only about ranking in search anymore. We also need to publish enough credible, technically useful information for AI systems to understand our expertise, projects, and industry experience accurately.
If you were advising someone in your industry on all of this for the next 12 months, what would you tell them to actually do, and what would you tell them to ignore?
I would focus on measurable AI adoption rather than broad AI adoption.
Choose development tasks with a clear baseline, measure the engineering hours required today, introduce AI, and then measure both the time saved and the additional review or rework created. Productivity should be judged on net effort, not output volume.
I would also invest in giving engineering teams better context, processes, and controls around AI agents. The quality of the surrounding workflow often matters more than the tool itself.
What I would ignore is the pressure to adopt every new model, coding assistant, or agent. The priority should be repeatable improvements in engineering efficiency, code quality, and delivery, not the number of AI tools in the stack.
Thank you to Kazim Qazi and the team at TechnBrains for sharing what actually worked with Leaders Perception readers.
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