AI
Applied AI
We take AI from demo to production: built into your workflows, connected to your data and measured with evaluations so its answers are reliable.
What's included
Everything you need, from start to finish.
Assessment of use cases with real return
Assistants and chat over your documentation (RAG)
Semantic search with embeddings and pgvector
Agents that automate multi-step tasks
Continuous quality and cost evaluations
Privacy, security and cost control
Technology
The usual stack for this service.
We choose based on your case; these are the tools we use most here.
PythonFastAPILLMs (Claude, OpenAI)pgvectorSupabaseQueues and workersRedis
How we use AI
It is the core of the service: we design the system, measure it with automated evaluations and improve it with real usage data.
Process
How we do it.
- 01 · Week 1DiscoverWe understand the business, users and constraints before writing code.
- 02 · Weeks 2–3DesignArchitecture, prototype and delivery plan. You know what you get and when.
- 03 · 2-week sprintsBuildShort deliveries with a working demo at the end of every sprint.
- 04 · OngoingScaleMonitoring, performance and new features while your product grows.
Questions
About this service.
Is our data used to train models?
We choose providers and settings that do not use your data to train models, and we document every decision.
How do we know the AI answers well?
We build a set of evaluations from real cases and run it before every change, just like software tests.
Related
You might also be interested in.
$ ilabs applied-ai --start
Shall we talk about your project?
Tell us what you need and we'll suggest the right approach and stack.