AI Agents And Document AI That Reach Production
AI agents, document processing, OCR and knowledge assistants integrated into your existing business systems.
What is broken today
- AI pilots that impress in a demo and never reach production
- Documents typed into systems by humans, one field at a time
- Support teams answering the same forty questions daily
What you get
- Document and OCR pipelines with human-in-the-loop review
- Internal knowledge assistants grounded in your own data
- AI agents that take actions in your systems, with guardrails
- Evaluation and monitoring so quality is measured, not assumed
What actually breaks
The pilot worked. A model read a handful of invoices in a demo and everyone was impressed. Then it met the real post bag: scanned at an angle, stapled, a handwritten amendment in the margin, a supplier who changed their template without telling anyone. Accuracy that looked like a solved problem becomes a queue of corrections, and the team quietly goes back to typing.
How we build it
Nothing goes straight from model to system of record. Documents are classified first, then extracted against a per-type schema, and every field returns with a confidence value. Fields above threshold post automatically; anything below routes to a review screen where a human corrects the value, and the correction is stored as labelled data rather than discarded. Knowledge assistants are retrieval-grounded against your own documents with the source passage shown next to the answer, so an answer that cannot cite anything becomes a refusal instead of a guess.
What goes wrong on these projects
The evaluation set is the work nobody budgets for, and skipping it means quality is an anecdote. Second, edge cases are not rare in aggregate: individually unusual document formats add up to a meaningful share of volume, and the review queue has to be staffed for that rather than treated as a temporary state. Third, providers change models, and a prompt tuned to one version can behave differently on the next.
What changes after
Documents post themselves when the model is confident and get reviewed when it is not, with exceptions visible instead of hidden. Support questions are answered from current material with the source attached. Quality becomes a number you can watch rather than a feeling.
Before you ask
Where does our data go?
Into infrastructure you control. Foxquart configures model providers for zero retention where that option is available, so your documents and internal knowledge are not retained by a third party. Knowledge assistants are grounded in your own data rather than uploaded into a shared external service you cannot audit.
How do you stop hallucinations?
Foxquart constrains the model rather than trusting it. Retrieval grounding ties answers to your own data, strict output schemas force a parsable shape, and confidence thresholds route uncertain results to a person. Anything financial or legal keeps human review in the loop by design, not as an optional extra.
Why do AI pilots never reach production?
Because a demo has no error handling, no monitoring and no owner. Foxquart builds AI automation as production systems instead: document and OCR pipelines with human-in-the-loop review, agents with guardrails on the actions they can take, and evaluation and monitoring so quality is measured rather than assumed after launch.
Can AI read our invoices and paperwork?
Yes. Foxquart builds document and OCR pipelines that extract fields from invoices and paperwork and write them into your systems, replacing humans typing one field at a time. Extraction runs against strict output schemas, and low-confidence results route to human review rather than being saved silently.
How do you know the AI is performing well?
Foxquart measures it. Evaluation and monitoring are part of every AI automation, so accuracy is tracked against real cases instead of inferred from one good demo. Confidence thresholds send uncertain outputs to human review, and anything financial or legal keeps a person in the loop permanently.
What does an AI automation project cost?
Foxquart quotes no standard price, because scope decides it. A fixed-price discovery ranks candidate processes by hours consumed and automation feasibility, then produces the delivery scope and cost. Delivery is fixed price per phase or a dedicated team retainer, and a first production automation typically ships in 10–15 working days.
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