Service · 06
Custom AI Solutions
RAG systems, assistants, and LLM-powered features built into your product — grounded in your data, not a generic chatbot.
Problem
You want AI inside your product — a copilot, search over your docs, a feature that summarizes or drafts — but a naive ChatGPT wrapper hallucinates and leaks context. Production AI needs retrieval, evaluation, and the same engineering rigor as the rest of your app.
What’s included
- Retrieval-augmented generation (RAG) over your data
- Embeddings + vector search pipeline
- Assistant / chat UI integrated into your product
- Prompt + evaluation harness to measure quality
- Guardrails, PII handling, and cost/rate controls
- Streaming responses with citations back to the source
Tools & stack
Deliverables
- AI feature deployed in your product
- Evaluation suite + quality benchmarks
- Usage and cost dashboard
- Architecture and prompt documentation
FAQ
Which models do you use?
We default to the latest frontier models (Claude and GPT) and stay model-agnostic — we pick based on quality, latency, and cost for your use case, and keep you free to switch later.
Is my data used to train models?
No. We use APIs configured so your data isn't used for training, and we can self-host open models when data residency requires it.
04 — Start a project
An idea, an existing app, or something in between?
Tell us what you're trying to ship. We reply within one business day with a written take on scope and timeline — not a sales pitch.