Capabilities
Document and case processing
What it is
This capability covers pipelines that ingest documents — invoices, claims, applications, contracts, medical forms, shipping paperwork — and produce typed fields, labels and decisions. It is not batch OCR dumped into a spreadsheet. Outputs feed ERP, case management, underwriting or compliance archives with lineage.
Case processing extends the same idea to work items that arrive as email, portal uploads or API payloads: classify case type, extract entities, check completeness, assign priority and open records in downstream systems.
Pipeline stages
- Ingestion from scanners, email, SFTP, portals or message queues.
- Pre-processing: deskew, language detection, layout analysis.
- Extraction: key fields, line items, signatures, tables.
- Classification: document type, product, region, risk tier.
- Business rules: cross-field validation, duplicate detection, sanctions checks where applicable.
- Routing: auto-post, exception queue, specialist review.
- Archival with searchable metadata and retention policies.
When it pays back
Payback is strong when skilled staff spend hours retyping or re-reading similar documents: accounts payable, claims intake, loan origination, customs, supplier onboarding. Throughput and error reduction matter as much as headcount.
Regulated industries benefit from consistent extraction and immutable audit logs — the same field read the same way every time, with confidence attached. Measure straight-through processing rate, average handling time for exceptions, error rate on posted records and time from receipt to decision.
When to wait
If document formats change weekly with no samples, or if legal has not defined acceptable automation levels, fix those first. Discovery will tell you if a twelve-week release should target one document family rather than the whole mailroom.
How Incubics engineers it
Two-week discovery. Production pipeline for a defined document or case family by week twelve. We prefer measurable accuracy on held-out samples over demo screenshots.
Perceive — weeks 1–2
We collect representative samples across quality levels, interview processors on edge cases and map target systems for posting. We label a gold set and estimate straight-through potential. Output: scope for first release, accuracy targets, exception UX and integration plan.
Engineer — weeks 3–10
We build extraction with model ensembles where needed — layout models, LLM extraction with schema validation, traditional ML for classification. Human review UI shows source snippets highlighted on the page. We integrate posting APIs with dry-run mode until accuracy gates pass.
Deliver — by week 12
Production path for the scoped document types with monitoring on confidence distributions and override rates. Processors trained on review screens. Runbook covers retraining triggers when supplier templates change.
Run — ongoing
We track drift when new layouts appear, refresh training sets from corrected reviews and rerun evals before model updates. FinOps covers per-document inference cost so finance sees unit economics.
Failure modes
- Chasing hundred-percent automation on messy scans — exceptions balloon and trust dies.
- No human review UX — operators cannot see why the model chose a value.
- Posting to finance systems without duplicate detection.
- Treating extraction as stateless — missing case context from prior submissions.
- Weak retention rules — PII in the wrong archive tier.
Layout changes from a major supplier can drop accuracy overnight. Monitoring override rates catches this faster than waiting for customer complaints.
What you get
- Ingestion adapters and pre-processing for agreed channels.
- Extraction and classification models with schema validation.
- Business rules engine hooking into your policy tables.
- Review workstation with side-by-side source and fields.
- Integration to target systems with idempotent posting.
- Metrics dashboard: STP rate, confidence, overrides, latency.
- Evaluation reports on gold sets with version history.
What we refuse to ship
We refuse unattended posting to financial systems without accuracy thresholds and rollback. We refuse black-box extraction with no highlighted evidence on the page. We refuse skipping legal review on categories they marked high risk in discovery.
Is this classic OCR or generative AI?
Often both. Layout-sensitive fields use specialised models; ambiguous free text may use LLMs with strict JSON schema and validation. The architecture follows accuracy and cost per document, not fashion.
How much labelled data do we need?
Hundreds of diverse samples for a narrow document type often suffice with modern models plus human review. Discovery quantifies label effort for your formats.
Can processors correct the system?
Yes. Corrections feed retraining datasets and eval updates. That loop is part of run, not a nice-to-have.
What about handwritten forms?
We scope honesty in discovery. Handwriting raises error rates; the first release may route handwritten pages to full manual entry while typed sections automate.
How do you integrate with our case system?
API-first where possible. Where only legacy UI exists, we work through the API layer capability or modernisation service rather than brittle screen scraping in production.
Next step
Start with two weeks.
A fixed-fee discovery gives you a ranked use-case portfolio, a target architecture, a cost model and a build proposal you can take to your board. If we don't find a case worth building, we tell you.