03 · AI Implementation
Document Processing
Auditable extraction pipelines for the documents that block your ops team.
Overview
What you get
Classify, extract, validate, and route thousands of PDFs, scans, and semi-structured forms with confidence-aware reviewer queues.
The problem
Why teams call us
- Ops team is the bottleneck on a workflow that should already be automated.
- Off-the-shelf OCR breaks on your messiest 20% — the 20% that matters.
- Compliance needs every extraction auditable end-to-end.
Approach
How we work
- Sample-driven: real documents on day one, not synthetic data.
- Confidence-routed: humans review the long tail, not the easy 80%.
- Schema-validated: nothing reaches downstream systems unchecked.
Process
Week by week.
- 01 · Week 1
Sample & schema
Doc sampling, target schema, accuracy targets.
- 02 · Week 2–4
Build pipeline
Classifier, extraction, validators, reviewer queue.
- 03 · Week 5–6
Tune
Confidence thresholds, throughput tuning, audit logging.
- 04 · Week 7–8
Handoff
Dashboards, runbooks, ops team enablement.
- High-volume document workflows blocking ops or compliance
- Mixed inputs: PDFs, scans, semi-structured forms
- A reviewer-in-the-loop step you want to keep auditable
- Single document type with a vendor that already nails it
- Workflows that can tolerate 60% accuracy with no review
- Teams unwilling to label 100–500 documents to bootstrap
Deliverables
Everything we ship
- 01Document classifier and extraction pipeline
- 02Reviewer queue with confidence-based routing
- 03Schema validators and human-handoff hooks
- 04Throughput + accuracy dashboards
- 05Audit log + replay tooling
Outcomes
What you walk away with.
An auditable extraction pipeline with named accuracy on your document mix — and a reviewer surface your ops team trusts.
Tooling
Stack we ship against
Model- and infra-agnostic. We adapt to your stack, not the other way around.
FAQ
Real questions, technically answered.
- What if our documents are highly variable?
- We classify first, then route to per-class extractors. Variability becomes a routing problem, not a model problem.
- Can this run on-prem?
- Yes. We support fully self-hosted deployments where compliance requires it.
- Who labels the bootstrap data?
- Your ops team, with our labeling rubric and tooling. Typical bootstrap is 100–500 docs.
Related engagements
Often paired with.
04 · AI Implementation
RAG & Embedding
Production-grade retrieval — measured against your golden set before it ships.
05 · AI Implementation
Fine-Tuning & Inference
Specialised models that beat general-purpose ones on accuracy and unit cost.
09 · AI Infrastructure
Production Telemetry
When your AI gets worse, your team knows in minutes — not quarters.
Next step
Ready to scope Document Processing?
Book a discovery call. We'll confirm fit, sequence the engagement, and have a Statement of Work in your inbox within a week.
Refundable if we're not a fitWritten diagnostic in 48 hoursSession run by a founder, not a sales rep