RAG and knowledge systems
Retrieval over your documents, tickets and databases with citations, permissions and freshness handled properly.
LLM development company
An LLM development company that ships production apps on Claude
We build LLM applications that hold up in production: retrieval over your own data, structured outputs your systems can trust, evaluations that catch regressions, and a gateway so the model stays a swappable component rather than a lock-in.
What we build with LLMs
Structured extraction
Typed, schema-validated outputs so an LLM result can be written into a database without a human retyping it.
Copilots inside your product
In-app assistants that act on your real data and APIs, with guardrails on anything destructive.
Evaluation and observability
Fixed evaluation sets, regression tracking, token cost dashboards and prompt versioning from day one.
How an LLM build runs
Define correctness
We collect real inputs and agree on what a good output looks like before writing a prompt.
Build the pipeline
Retrieval, prompting, tool calls and validation, wired through a model gateway so models can be swapped per task.
Evaluate and tune
Scored against the fixed set on every change, with cost and latency tracked alongside quality.
Ship and hand over
Deployed with monitoring and documentation. You own the repository, prompts and evaluations.
Pricing
A first LLM application ships as an MVP Production build at a $1,500 flat rate. Multi-surface LLM platforms and large-scale rollouts are scoped to a fixed price after a call.
See the full pricing tiers or browse the ventures we've built.
Common questions
- Which models do you develop on?
- Primarily Anthropic's Claude, routed through a gateway so each task can use whichever model wins on quality, latency and cost. The model stays swappable.
- Can you work with our private data?
- Yes. Retrieval runs over your own stores with your permission model enforced, and we scope data handling to what the feature actually needs.
- How do you stop an LLM from making things up?
- Retrieval with citations, schema-validated outputs, tool calls instead of recall for facts, and human approval gates on irreversible actions — all measured against a fixed evaluation set.
- Do we own the LLM application?
- Yes. On custom builds you own the codebase outright, including prompts, evaluations and infrastructure code.