The problem this solves
Your team already uses AI, individually, inconsistently, and without context. Answers about your products, pricing rules, or processes come out generic or wrong because the model has never seen your documentation. Sensitive material gets pasted into consumer tools outside your control, and the knowledge that should make AI useful sits unindexed in drives, wikis, and inboxes.
How we work
We define the assistant around real recurring questions, not a demo script: which team asks what, from which sources the answers should come, and what a wrong answer costs. That scoping decides everything downstream, model choice, retrieval design, and where the assistant lives.
Then we build it: retrieval over your documents and data with vector search so answers cite your actual material, a commercial LLM such as Anthropic Claude or OpenAI models behind it, and integration into the surface your team already uses, Slack, a web app, or directly inside HubSpot. We build in Python and Node on serverless cloud infrastructure, the same stack we run our own AI tooling on, with logging, access control, and guardrails as first-class parts of the build.
Before handover we evaluate against a test set drawn from real questions and review the failures together. You get a working assistant, the pipeline that keeps its knowledge current, and documentation for operating it.
Deliverables
- Use case and source-of-truth specification
- Retrieval pipeline over your documents and data
- Working assistant integrated into Slack, web, or HubSpot
- Guardrails: access control, logging, refusal behavior
- Evaluation set from real questions with a reviewed test report
- Operations documentation and knowledge update workflow