Between the three of us at Tailo, we have 36 years of experience building production software. We've worked at Netcompany, Accenture, Generali, and XTM. We've shipped systems that had to work reliably, at scale, for real users — not just in demos.
When AI started becoming practically useful, we brought the same engineering discipline to it that we'd applied to everything else. Here's what we've learned.
Reliability is non-negotiable
AI introduces a different kind of uncertainty. The same input can produce slightly different outputs. The model can be confidently wrong. The edge cases are harder to enumerate.
Our response to this isn't to accept unpredictability — it's to design around it. We build guardrails. We define what the assistant should do when it doesn't know the answer. We test against real scenarios from the client's workflow, not just synthetic ones.
Simplicity is still the right goal
One of the temptations with AI is to make things elaborate. The technology can do so much — why not build something that does everything?
Because the things that get used are the things that are simple enough to trust. We've watched teams abandon sophisticated AI tools in favour of a simpler one that did one thing well and never surprised them.
We scope every project to the minimum that solves the actual problem — the minimum that makes the team's life genuinely better, without adding cognitive overhead.
The handoff is part of the build
Good software engineering includes documentation, testing, and knowledge transfer. The same applies to AI assistants — with one addition: you also have to transfer the mental model.
Teams need to understand not just what the assistant does, but how to think about it. What kinds of questions does it handle well? When should they trust it immediately? When should they review carefully?
What changes with AI
The biggest shift is that AI systems improve over time — but only if someone is paying attention. An AI assistant can drift, improve, or degrade depending on how the workflow around it evolves.
This means the relationship with an AI assistant doesn't end at deployment. It requires ongoing attention: checking that it's still solving the right problem, refining prompts when the workflow changes, updating the knowledge base when new information becomes relevant.
We stay involved after launch because of this. Not indefinitely — but long enough that the assistant is genuinely embedded in how the team works.