Generative AI grounded in your own data
We build assistants, internal copilots and search experiences on large language models, using retrieval-augmented generation so answers come from your documents, with sources, not from guesswork.
What you get
RAG search & assistants
Chat and search over your knowledge base, tickets, contracts or code, with citations.
Copilots for your team
Assistants inside the tools your staff already use, such as Slack, Teams or your admin panel.
Private deployments
Models on Amazon Bedrock or Azure OpenAI inside your own cloud, so data stays in your account.
Quality & cost control
Evaluation sets, answer-quality tracking, caching and model routing to keep bills sensible.
Step by step
- 1
Data review
Find the sources worth indexing and the ones that must stay out.
- 2
Build
Ingestion pipeline, vector store and the assistant itself.
- 3
Evaluate
Measure answer quality against real questions from your team.
- 4
Ship
Launch with feedback buttons, monitoring and cost alerts.
What changes for you
- Answers with sources
- Data stays in your cloud
- Predictable running costs
Tools we use
Questions about generative ai & llm apps
Will our data be used to train public models?
No. We use enterprise model endpoints on AWS or Azure that do not train on your data, deployed inside your own account.
How do you stop it making things up?
Retrieval with citations, strict prompts, evaluation sets and a clear "I don't know" when the answer is not in your data.
Tell us what you are building, or what is keeping you up at night.
A 30-minute call with an engineer, not a sales script. You will leave with practical next steps even if we never work together.