Ship AI in software products — without scoring an hallucination own goal.
You're a SaaS or product team and want to ship AI features that don't end up on the next Hacker News thread. Exactly what I'm building Tavora for — and exactly the focus of my consulting for software teams.
What's different about AI features in software products
AI features in a product are not normal features. They're non-deterministic, occasionally hallucinate even after three months of tuning, cost money per call, and may behave differently after every model update. That's not the engineering your team is used to.
With Tavora I build a platform that lets AI agents run in production under control — eval-gated deployments, sandboxed execution, agent-level observability. From that daily work I know what breaks: unreviewed prompts in PRs, cost spikes from wrong tool calls, model updates turning tests red, production bugs nobody can repro.
My consulting for software teams carries that reality. We don't talk LLM theory — we talk CI/CD for prompts, eval suites that make sense, per-use-case cost control, GDPR-compliant architecture, and sensible UX patterns for AI features.
Where I typically help software teams
- 01
AI feature architecture review
A two-to-four-hour deep dive into your planned or live AI feature architecture. Where does it hallucinate today? Where will costs explode? What breaks on the next model update?
- 02
Build an eval suite
An eval pipeline your team can use in CI/CD. Model updates and prompt changes get measured against a test set before they ship.
- 03
Agent architecture and tool use
Advice on agent design: when is an agent worth it versus a simple pipeline call? How do you structure tool calls so they don't escalate?
- 04
Cost control and model selection
LLM costs in your stack are often 3–10× higher than they need to be. We walk through the stack and identify where you can move to cheaper models without losing quality.
- 05
GDPR and data processing
Which models can see which data? How do you structure data processing when you have EU customers and use US models? Practical answers, not legal hand-wringing.
- 06
UX patterns for AI features
Streaming, loading states, error handling, user corrections, confidence indicators — best practices that have emerged in the past two years.
Common questions from software teams
We already have an AI feature in production. Do we still need consulting?
Honestly — probably yes. Most production AI features have no eval suite, ad-hoc prompt engineering, and patchy cost control. A two-day architecture review closes those gaps before they hurt.
We have our own ML team. What do you add?
Traditional ML and LLM application work need different skills. If your ML team comes from classifiers and embeddings, it often has gaps in LLM application patterns, agent design, prompt engineering, cost control. I complement, not replace.
Can you help us build an AI team?
Yes. If you want to outgrow external consulting, I help build an internal LLM application team: role profiles, onboarding, architecture standards, sensible KPIs.
What does a typical engagement cost?
Architecture review starts at €3,500. Eval suite setup €8,000–15,000. Longer engagements run as a retainer from €1,900/month.
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Let's talk for 30 minutes.
I listen, ask questions, and tell you honestly whether and how I can help.