LLM assistants that answer from your data.
Retrieval-grounded assistants, chatbots, and copilots wired into your systems, so answers are accurate, current, and auditable.
WHAT IT IS
The plain description.
We build assistants on top of your own knowledge: docs, tickets, product data, and CRM. Retrieval-augmented generation keeps answers grounded in your sources instead of the model's guesswork.
That means internal copilots for your team, support assistants for your customers, and search that actually understands your domain, with citations back to the source.
We keep it auditable: every answer can be traced, evaluated, and improved, and sensitive data stays in your environment.
WHEN YOU NEED IT
Signals that this fits.
Your team wastes hours searching across tools
An internal assistant answers from all of them at once.
Support volume is rising faster than headcount
You have rich docs and data no one can find
A generic chatbot hallucinates about your product
You need answers with citations for compliance
HOW WE DO IT
A short, honest sequence.
- 01
Scope and sources
We pick one clear task, list the sources it must read, and agree on what a good answer looks like.
- 02
Ingest and index
We build a retrieval pipeline over your content with chunking, embeddings, and access rules that match your permissions.
- 03
Guardrails and evaluation
We write a task-specific eval set, tune prompts and retrieval, and set refusal and citation rules before it ships.
- 04
Integrate
We wire the assistant into Slack, your site, or your app, with the auth and logging you already use.
- 05
Monitor and improve
We track quality, cost, and coverage in a live dashboard, then iterate on weak spots on a set cadence.
WHAT YOU GET
Deliverables at the end.
- A deployed assistant (web, Slack, or in-app)
- A retrieval index over your sources
- An evaluation harness and quality report
- Guardrail and prompt configuration
- Monitoring dashboard and docs
STACK
Tools we reach for.
Models
- OpenAI
- Anthropic
- open-weight LLMs
Retrieval
- pgvector
- Pinecone
- LlamaIndex
Orchestration
- LangGraph
- server-side APIs
Eval and monitoring
- Ragas
- custom evals
QUESTIONS
Answers, before you ask.
- No. We default to provider modes that do not retain or train on your data, and where you require it we run open-weight models inside your own cloud so nothing leaves your perimeter.
Next step
Book an intro call.
Fifteen minutes. We ask the sharp questions, tell you if we are a fit, and either scope a sprint or point you elsewhere.