AI where it measurably helps.
We put AI into real workflows — document processing, intelligent search, decision support — with honest scope: measured against the manual work it replaces, not the hype.
Sound familiar?
Manual document work eats the day
Invoices, forms and applications are read, typed and routed by hand. Volume scales, accuracy doesn't.
Customers wait for simple answers
The same questions arrive all day. Staff copy-paste replies from documents the system could read itself.
AI pilots that never ship
A demo impressed the board, but nothing survived contact with real data, privacy rules and daily operations.
Scope you can hold us to.
- Document processing — extraction, classification, routing
- AI assistants grounded in your own documents and data
- Intelligent search across internal knowledge bases
- Workflow automation with AI decision points
- Integration of AI into existing software — no rip-and-replace
Where this pays for itself.
Automated extraction with review queues only for low-confidence fields
An assistant that drafts grounded replies for staff to approve
One search across documents with cited answers
A process you can plan around.
One team from discovery to operation. You always know what is built, what is next, and what it costs.
- Use-case auditFind tasks where AI beats the manual baseline honestly
- Data reviewCheck document quality, privacy and access constraints
- PilotNarrow scope, real data, measured accuracy
- IntegrateWire into the tools your team already uses
- OperateMonitor drift, cost and escalation quality
Technology chosen for the problem.
- Frontier models via API — no infrastructure burden
- Answers grounded in your documents
- Structured extraction from forms and invoices
- Accuracy measured, not assumed
Fits into a bigger system.
Asked before signing.
Is our data used to train AI models?
No. We use API providers under commercial terms where your data is not retained for training, and keep sensitive documents inside your infrastructure where required.
What if the AI is wrong?
Every workflow includes confidence thresholds and human review for uncertain cases. Accuracy is measured during the pilot before any full rollout.
We don't have clean data. Is that a problem?
Most data is messy. The use-case audit includes a data review, and cleanup is scoped honestly as part of the work rather than discovered later.
Discuss an AI Use Case?
Tell us about the workflow, the product, or the problem behind ai development. You'll get an honest read on scope, approach and cost.
Discuss an AI Use Case