Finance and back-office leads
Your team retypes invoices, contracts and forms into systems all day, and the error rate shows.
AI features that earn their cost
Retrieval over your documents, extraction from invoices and contracts, agents for repetitive back-office work. Every feature ships with an evaluation suite, so you know the accuracy number before your customers do.
What is included
- Use-case selection based on measurable value
- Retrieval pipelines over your real documents
- Structured extraction with confidence scores
- Evaluation suites run before every rollout
- Human review workflows for low-confidence cases
- Cost and latency budgets agreed up front
Who this is for
If one of these sounds like your week, the scope call will be a productive 45 minutes.
Product teams under AI pressure
The board wants an AI feature; you want one that survives contact with real data.
Companies sitting on documents
Years of PDFs, tickets and reports that nobody can search, let alone learn from.
How this engagement runs
Prove
A short paid pilot on your real data. If the accuracy number is not good enough, you find out for thousands, not hundreds of thousands.
Build
Production pipelines with evaluation gates, monitoring and fallback paths for the cases the model gets wrong.
Operate
Accuracy and cost dashboards, drift alerts, and retraining or prompt updates as your data changes.
- Claude
- OpenAI
- Vercel AI SDK
- Postgres pgvector
- Python
A typical engagement · Back-office automation
A document pipeline with human review built in
The typical build: documents arrive in every format, get extracted with confidence scores, and anything below threshold routes to a human review queue. An evaluation suite runs on every change, so accuracy is a number you watch, not a hope.
- Extraction with confidence scoring
- Human review queue for edge cases
- Evaluation suite and accuracy dashboard
Common questions
Which models do you use?
Whichever passes your evaluation at the best cost. We build so the model is swappable, and we show you the accuracy and cost numbers per option before you commit.
What about our data?
Your data stays in your accounts under your keys. We configure zero-retention API options where providers offer them, and it is written into the contract.
Is our use case actually a fit for AI?
Sometimes it is not, and we say so on the scope call. The pilot exists precisely to answer this question cheaply.
Stop comparing agencies. See a real plan for your build instead.
- 45 minutes with an engineer, not a salesperson
- Written scope and fixed quote in two working days
- NDA signed before the call if you want one
Book a call
No obligation. If we are not the right fit, we say so on the call.