Practical. Measured. Built into the work.
AI automation and integration
AI built into the systems you already run: assistants that answer over your own documents, extraction from forms and invoices, and processes that no longer need a person to move them between tools.
What this covers
Four jobs a model can be measured on. Everything else is a demo.
Assistants over your own data
Answers from your documents, with the source attached.
Retrieval over your own contracts, manuals, tickets and policies, so an answer comes back with the passage it came from rather than a confident guess. Scoped to what the person asking is allowed to see, and built so an unanswerable question returns nothing rather than something invented.
Extraction and classification
Invoices, forms and email, turned into fields.
Documents in, structured records out: line items off an invoice, details off a form, a category and a priority off an inbound email. Accuracy is measured against a held-back sample before it goes near production, and anything under the threshold routes to a person instead of being guessed at.
Process automation
The steps between systems that a person is doing by hand.
The copy and paste between the CRM, the spreadsheet and the accounting package. Triggers, queues, retries and an audit trail, so the automation is something you can inspect when it does the wrong thing rather than a black box that quietly stops.
Machine learning and data
A model is only as good as the pipeline feeding it.
Forecasting, scoring and recommendations where there is genuinely enough history to support them, and a straight answer where there is not. Most of the work is the unglamorous half: getting the data out of the systems it is trapped in, cleaning it, and keeping it arriving.
Built with
- OpenAI
- Python
- Node.js
- Express
- MongoDB
- PostgreSQL
- Redis
- Docker
How a project runs
- 01Discover
- 02Define
- 03Design
- 04Develop
- 05Validate
- 06Deploy
- 07Evolve
Questions
What does AI-powered actually mean here?
A model doing one specific job and being measured on it: usually retrieval over your own content, extraction from documents, classification, or generation with a review step. It is a feature with an accuracy and a cost per request, not a badge on the marketing.
Does our data go to OpenAI?
Only what a request needs, and only if you want it to. API traffic is not used for training by default, and where that is still not acceptable we run open models on your own infrastructure instead. Which route a project takes is a decision we make with you at the start, not after.
What happens when it gets something wrong?
It will, so the design assumes it. Confidence thresholds, a human review step on anything consequential, the source shown next to the answer, and logging that lets you go back and see exactly what was asked and what came back.