Document Intelligence & Retrieval
Your documents already know the answer.
Contracts, invoices, reports, tickets, wikis — we turn the documents your business runs on into grounded answers and automated actions, wired into the systems where work happens.
What we build
Knowledge assistants
Grounded answers from your internal documentation, with citations back to the source. Your data, not the open internet.
Document processing pipelines
Extraction, classification, and validation for invoices, contracts, and forms — feeding your existing systems, not a new silo.
Retrieval done right
Chunking, indexing, and evaluation tuned to your corpus; retrieval quality measured, not assumed.
Human-in-the-loop review
Confidence thresholds route the ambiguous cases to people, so accuracy stays auditable.
Proof
Our own hiring
An agent that turns interview transcripts into client-ready profiles has run our hiring since early 2025 — a year in production; two to six hours per profile became about ten minutes.
the field note- Hiring agent
- Since early 2025
- Transcript → client-ready profile
- 2–6 h → ~10 min
- Per profile
Two kinds of reading
Extraction
Documents in, structured fields out: the invoice's line items, the contract's parties and terms, the form's answers — validated against what you already know and fed into the systems you already run. Invisible when it works; measured so you know when it doesn't.
Retrieval
Questions in, grounded answers out — from your documentation, your records, your history, with the source shown and permissions enforced at query time. When the corpus doesn't contain the answer, "I don't have that" is the correct response, and the system says it.
Both stand on the same discipline: quality that is measured on your corpus, citations a reader can check, and a gate for the cases the model shouldn't decide alone.
When teams come to us
- Hours lost searching internal knowledge
- Manual document entry that scales linearly with volume
- A compliance or legal team drowning in review work
How it starts
The Discovery Sprint tells you which document flow pays back first — and what your data needs before it can.
How we build it
Corpus audit
which documents, what quality, what access
Retrieval design
chunking and indexing tuned and measured on your corpus
Ship with citations
every answer traceable to its source
Feedback loop
wrong answers become improvement signal
What “reliable” means
Validation
a field is correct because it agrees with something you know — the purchase order, the vendor record, the sum of its own lines — not because the model was confident.
Routing
confidence decides who sees each document: straight through, a one-click review, or a person — review reserved for judgment.
Citations
every answer and every flag points back to its page; an output without a source gets ignored the second time it's wrong.
Evaluation
a test set built from your real documents, run every time a prompt, a model, or the corpus changes.
Extraction that is right 95% of the time still leaves every twentieth document wrong. The system's job is knowing which one.
What an engagement looks like
It starts with the corpus, not the model: one document flow, one team, one metric — invoices into the ledger, contracts into review, questions into answers. The AI Discovery Sprint finds which flow pays back first and what your documents need before it can; if you already know, you bring the flow straight to implementation. What ships: the pipeline or the assistant, the review screen your team will actually use, the citations, and the evaluation set — yours to run, with us or without us. A first document flow is weeks of work, not quarters.
Works well with
FAQ
Our documents are a mess — is that a blocker?
It's the first deliverable: the audit tells you what to fix and in what order.
How do we know the answers are right?
Citations to source plus measured retrieval quality. "Trust me" is not an architecture.
Does our data train someone else's model?
No. Your data stays inside your access controls; grounding is not training.
Scans, photos, handwriting — can it read them?
Layout-aware models handle most of what template OCR couldn't; scans and photos go through OCR first; handwriting is honest case-by-case. The corpus audit tells you what your documents actually need — before anyone promises accuracy.
Our documents change — contracts get renegotiated, policies get updated.
The index updates when the document does, not on a schedule. An answer from last month's policy is worse than no answer, and freshness is part of the design, not an afterthought.
Can it act on what it reads?
That's the next step: extraction feeding actions — records updated, tickets opened, payments scheduled — with a person approving until each action class earns autonomy. That work lives in AI Agents & Assistants; the reading built here is what makes it safe.