We build intelligent document processing pipelines that classify what arrived, extract the fields that matter, validate them against your rules, and push the result into the system that owns the next step — with a human review path for anything the pipeline is not confident about.
Document work is expensive precisely because it looks trivial: read this, type that somewhere else, decide where it goes.
PDF, scan, phone photo, email body — each vendor different, and someone keys them into the finance system by hand.
It reads clean documents fine and fails on anything with tables, multi-column layouts, or handwriting, so a person still checks everything.
Inspection sheets and job reports come back from site as photos or scans, then get manually transcribed into the system of record.
Renewal dates, payment terms, and liability caps are locked inside documents nobody can query, so they are tracked in a parallel spreadsheet.
The process worked at fifty documents a day. At five hundred it is a backlog with a growing error rate.
An audit asks where a figure came from, and the answer is that someone read it off a document that may or may not still be findable.
Each stage is instrumented separately, so when accuracy drops you know which stage caused it.
Identify the document type and route it to the right extraction schema before reading any fields.
Pull the fields that matter into structured output, with a confidence score attached to each value.
Check values against your own systems and business rules — not just whether text was read, but whether it makes sense.
Anything below threshold goes to a human queue with the source document and extracted values side by side.
Validated output posts to the owning system, with the original document linked to the record it produced.
The pipeline is the same shape each time. What differs is the schema, the validation rules, and where the output lands.
Vendor invoices matched against purchase orders and receipts, with exceptions surfaced rather than guessed.
Inbound orders read from PDF or email and turned into system records without manual entry.
Key terms extracted into a queryable form so renewals and obligations stop living only inside the document.
Site paperwork and photos captured and structured, so the office does not re-type what the crew already wrote down.
Onboarding and verification packs checked for completeness and validity before they reach a reviewer.
Specifications, drawings, and datasheets made searchable and queryable across a document estate.
Most production pipelines use both. Choosing one for everything is how projects end up expensive and inaccurate at the same time.
| Classic OCR + rules | Multimodal / LLM extraction | |
|---|---|---|
| Best on | Fixed templates, clean scans, known layouts | Variable layouts, mixed formats, unseen vendors |
| Tables and layout | Reliable when the template is stable | Handles shifting structure without a template |
| Handwriting | Weak outside constrained forms | Considerably better, still needs review |
| Cost per page | Very low | Meaningfully higher — matters at volume |
| Explainability | Deterministic and easy to trace | Needs confidence scoring and source linking |
| Typical role | High-volume known formats | The long tail everything else falls into |
Nobody can quote you an accuracy figure before seeing your documents. Here is how we get to a number you can rely on.
We build an evaluation set from your real documents, including the awkward ones. Accuracy is reported per field, because a pipeline can be excellent at dates and poor at line items.
You choose the trade-off between automation rate and review volume. A stricter threshold means more human review and fewer errors reaching your systems.
Corrections made by reviewers become evaluation cases, so the queue is a feedback loop rather than a permanent tax.
Model and prompt changes run against the full evaluation set first, so an improvement on invoices cannot silently break contracts.
Extraction that ends in a CSV is a demo. The value is in the document arriving as a record in the system that owns the process.
ERP & Finance
CRM & Operations
Storage & Intake
Data & Search
Where documents cannot leave a jurisdiction or a network boundary, we route to models that satisfy that constraint, including self-hosted where required.
Fields you do not want sent to a third-party model can be masked before extraction, with the redaction applied at ingestion rather than after.
Every extracted value links back to the source document and the run that produced it, so an auditor can trace a figure to its origin.
Reviewers see only the document classes their role permits, which matters when the same pipeline handles finance and HR documents.
We look at real samples across your formats, map what happens to them today, and establish what accuracy would need to be to remove manual work.
A labelled sample becomes the benchmark. You get a measured per-field accuracy figure before committing to a full build.
Classification, extraction, validation, review queue, and the integration into the system that receives the output.
Confidence thresholds set against your tolerance for review volume, with monitoring you can read without us.
Model and tooling choices follow the document types. Most production pipelines combine several rather than betting on one.
Send a representative sample and we will tell you what a pipeline could realistically extract, at what confidence, and how much manual review would remain. Measured on your documents, not a generic benchmark.