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.
Chat interfacesRetrievalAgentsAPI integrationEvaluationGuardrails
Who it suits
- 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
What You Get
1 to 4 weeksA 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
02
How it runs
1 to 4 weeksThe Stages
3 stagesDefine
One job worth automating, small enough that a wrong answer is obvious.
Ground
Pointed at your own content, so answers cite something rather than invent it.
Ship
Inside the product people already use, with the running cost measured first.
Where Time Goes
1 to 4 weeksDefineDays 1 to 217%
GroundWeeks 1 to 250%
ShipFinal week33%
03
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
Scope, up front- A model trained from scratch
- A chatbot bolted onto the side of the site
- Any promise that it will never be wrong
04
Tools and examples
What it is built onBuilt With
6 toolsAIWorkflows
PythonLanguage
Next.jsFramework
APIsIntegration
PostgresDatabase
SupabasePlatform
