Executive coaching
Retrieval-backed coaching assistant
A Slack-native assistant that answers from a curated coaching corpus rather than from whatever the base model remembers — vector search over embedded source material, with scheduled nudges pushed to users on a cadence.
Pipeline
- sync corpus
- embed
- vector search
- compose context
- generate
- deliver in Slack
01The brief
What made this hard.
The problem
A general-purpose model gives confident, generic coaching advice. The client's value was in their own methodology, so answers had to be grounded in their material and traceable back to it.
The approach
Source content is embedded into a vector index and retrieved per query, with the assistant orchestrated across multiple model providers so no single one is a point of failure. Content syncs from the client's existing operational tooling, so their team keeps editing where they already work. A scheduler drives recurring prompts without a human in the loop.
02In production
What it actually does, day to day.
- Answers grounded in the client's own corpus, not model recall
- Multiple model providers behind one interface
- Non-technical team edits content in the tool they already use
- Scheduled delivery runs unattended
Stack
03More systems
Others we have shipped.
04Start here
Tell us what you are building. We will tell you what it takes.
Four short steps, then a real conversation with the engineer who would build it. No sales call, no discovery deck.
Rather just talk?
Grab 45 minutes. You will be on with an engineer, not a salesperson.
- No NDA needed to have the first conversation
- You keep the architecture note either way
- We will tell you if we are the wrong fit