Overview
Language models wired into something that already exists, rather than a demo that impresses once and is never opened again. Chat over your own documents, retrieval that cites what it used, and agents scoped to a job small enough that you can tell when it got it wrong.
Covers
06- Chat interfaces
- Retrieval
- Agents
- API integration
- Evaluation
- Guardrails
Who it suits
03- Teams pasting the same things into a chat window all day
- Products that need answers out of their own content
- Anyone whose AI pilot never left the demo
01
What you get.
4 includedIncluded1 to 4 weeks
- A feature inside your product, not another tab
- Retrieval that cites what it used
- Running costs measured before you commit to them
- Somewhere to check what it actually answered
Built by Denney
02
How it runs.
1 to 4 weeksShare of the work, by the weight of each stage.
- 17% of the work
Define
One job worth automating, small enough that a wrong answer is obvious.
Days 1 to 2 - 50% of the work
Ground
Pointed at your own content, so answers cite something rather than invent it.
Weeks 1 to 2 - 33% of the work
Ship
Inside the product people already use, with the running cost measured first.
Final week
03
Built with.
6 tools- AIWorkflows
- PythonLanguage
- Next.jsFramework
- APIsIntegration
- PostgresDatabase
- SupabasePlatform
04
What to check.
Scope, up frontIn the product or beside it
Side by sideBuilt into the product
- Answers out of your own content
- Cites what it used
- Cost per answer measured up front
A chat window on the side
- Answers from whatever it read online
- Confident either way
- Cost discovered on the invoice
What this is not
3 excluded- A model trained from scratch
- A chatbot bolted onto the side of the site
- Any promise that it will never be wrong