Generative AI development
Generation witha structure behind it
Generated text is only useful when it lands in a format the next system can consume. We build generation pipelines with schemas, validation, and a review step — documents, summaries, and structured extractions that hold their shape.
500+
Projects delivered
most under NDA
100+
Clients served
5 regions
5
Systems live
in production now
—
Clutch rating
awaiting verified score
What we build
The work itself,component by component
Structured generation
Schema-constrained output so downstream code gets fields, not prose it has to parse.
Document assembly
Long-form documents composed from templates, retrieved context, and validated sections.
Extraction pipelines
Unstructured input in, typed records out, with confidence scores and a review queue.
Summarisation at scale
Batch pipelines over large corpora with deduplication and cost control.
Human-in-the-loop review
A queue where a person accepts, edits, or rejects — and the edits feed the eval set.
Versioned prompts
Prompts live in the repository under review, with regression runs on every change.
Why it matters
What changesonce it is running
Three things a buyer of this work should be able to hold us to.
Parsable output — Schema validation means malformed generations fail loudly instead of corrupting a record.
Improvable — Reviewer edits accumulate into an evaluation set that measures whether changes actually help.
Predictable cost — Batching and caching keep per-document cost inside a number you can plan around.
Stack we use for this
- Python
- Pydantic
- OpenAI / Bedrock
- Celery
- PostgreSQL
- S3
How it runs
Four stages,and you can leave after any of them
Scope
We agree what the system does and what shipping means, in writing.
Build
Short cycles in your repository, running before it is finished.
Ship
Deployed into your cloud account, reachable by a real user.
Stay
Maintenance and roadmap by the same engineers who built it.
Proof
The systemsthat back this page
FAQ
Questionswe get asked
How do you keep output on-format?
Schema-constrained decoding plus validation. Anything that fails the schema is retried or routed to review.
Can a person review before it goes out?
Yes, and we recommend it for anything customer-facing until the eval set proves it out.
Does prompt tuning need an engineer every time?
No — prompts are versioned assets your team can edit, with regression runs guarding the change.
Generative AI development