Your data should answer questions, not create another system nobody trusts.
AI projects fail when the use case is vague, the data is messy, or the model is bolted onto a product with no feedback loop. Ardilis starts with the decision your user needs to make, then builds the data pipeline, retrieval layer, or LLM feature around that outcome.
Concrete outcomes you can keep using after launch.
An LLM integration with product boundaries
The model has clear inputs, outputs, fallback behavior, and a place inside the workflow where it helps.
A data pipeline your team can inspect
Raw data becomes structured, traceable, and ready for features instead of living in scattered files.
A RAG system with useful retrieval
Documents are chunked, indexed, retrieved, and cited so answers can be checked instead of blindly accepted.
A model plan that earns its cost
Fine-tuning is used only when retrieval, prompting, or workflow design cannot solve the problem cleanly.
An AI feature inside your product
The result is a working feature in the existing interface, not a disconnected demo in a notebook.
You're a good fit if...
You know AI could help, but not where
You have a product, process, or dataset and need the first useful use case defined before build starts.
Your first AI attempt failed
The chatbot gives weak answers, retrieval misses context, or nobody can explain why the output changed.
Your team needs reliable automation
You want AI to reduce manual work, but the output must be reviewable and safe enough for customers.
Four steps, clear outputs.
You get a staging link at every milestone. No surprises.
Discovery
We turn the problem, users, constraints, risks, and success criteria into a clear working scope.
Architecture & Design
The core screens, data model, system boundaries, and integration points are mapped before build work hardens them.
Build
The product is implemented in focused milestones, with each release reviewed against the agreed scope.
Handover
The work is cleaned up, documented, deployed, and prepared so you can own it without guesswork.
The stack is chosen for the work, not for decoration.
- Python
- FastAPI
- Supabase
- Claude API
- Docker
- Sentry
Not sure if this is what you need?
Book a 30-minute discovery call. We will clarify the problem, the minimum useful scope, and the next technical decision.