Solutions · Use case
Structured Extraction.
Generative AI, scoped to the shape of the problem it solves.
Schema-conformant extraction of fields, entities, and tables from messy inputs.
Use-case context
Half the back-office spend in document-heavy industries goes to humans transcribing fields off PDFs, emails, and scanned forms into ERP, claims, or case-management systems. Rules-based OCR fails on layout drift, and naive LLM extraction silently invents fields when the source is ambiguous — both failure modes are expensive when the downstream system trusts the value. We build extraction pipelines that bind output to an explicit JSON schema, score each extracted field with a per-field confidence, and route low-confidence fields to a human reviewer queue. The same schema doubles as the contract for downstream integrations so the receiving system never sees an unexpected shape. Measured by per-field F1 (precision and recall on extracted values) against a labelled holdout, plus calibration error on the per-field confidence scores so that low-confidence flags actually correspond to low accuracy. Throughput is tracked against the cost of human review the system displaces. We do not deploy extraction pipelines that write to downstream systems of record without explicit confidence gates and human review for low-confidence fields — auto-write to claims, billing, or trading systems is out of scope. In our coverage footprint this lands first across financial services, healthcare, energy — the sectors where the data shapes and evaluation criteria line up cleanly with what this use-case actually measures.
How this shows up across industries
Where structured extraction lands in production engagements.
Financial Services · Generative AI Consulting
See industry →In financial services, the highest-yield generative AI deployments tend to be document-intelligence over policy and compliance corpora and structured-extraction agents over claims, contracts, and statements.
Healthcare · Generative AI Consulting
See industry →In healthcare, generative AI is most reliable when scoped tightly: clinical-letter drafting, prior-authorisation automation, and structured extraction over EMR notes — under human review.
Insurance · Generative AI Consulting
See industry →In insurance, the strongest generative AI use cases are claims summarisation, underwriting research, and document classification across submission packets.
Retail · Generative AI Consulting
See industry →In retail, generative AI shifts from buzzword to bottom line via search re-ranking, product-attribute extraction, and post-purchase support deflection.
Logistics · Generative AI Consulting
See industry →In logistics, generative AI tends to attach to exception handling, shipment-document extraction, and customer-service triage rather than core routing.
Data shape
PDFs, emails with attachments, scanned forms, EDI feeds, supplier documents, submission packets, and the target schema in the receiving system of record.
Where it lands first
Industries that include structured extraction in their applicable use-cases.
- 01
Financial Services
Generative AI for banks, asset managers, and capital markets — under explicit regulatory posture.
- 02
Healthcare
Tightly-scoped generative AI for clinical operations and document automation — never direct clinical decisioning.
- 03
Energy
Generative AI for technical document retrieval, regulatory submissions, and operational summarisation.
- 04
Insurance
Generative AI for claims summarisation, underwriting research, and submission-document classification.
- 05
Retail
Generative AI for search, product-attribute extraction, and post-purchase support.
- 06
Logistics
Generative AI for exception handling, document extraction, and customer-comms automation across TMS / WMS surfaces.
- 07
Manufacturing
Generative AI for supplier-document extraction, technical-manual retrieval, and quality-incident summarisation.
- 08
Education
Generative AI for curriculum drafting, accessibility tooling, and administrative automation — under explicit student-data residency.
- 09
Media
Generative AI for editorial research, asset tagging, and personalised distribution — with human-in-the-loop quality gates.
Where we draw the line
We do not deploy extraction pipelines that write to downstream systems of record without explicit confidence gates and human review for low-confidence fields — auto-write to claims, billing, or trading systems is out of scope.
Talk to us about a structured extraction engagement
A 30-minute call to scope where structured extraction actually moves the curve against your evaluation criteria.
Book strategy callWhy work with Veso AI on structured extraction
Measured
Evaluation, not opinion
Measured by per-field F1 (precision and recall on extracted values) against a labelled holdout, plus calibration error on the per-field confidence scores so that low-confidence flags actually correspond to low accuracy. Throughput is tracked against the cost of human review the system displaces.
Fixed-fee
After paid discovery
Two-week discovery assembles the labelled evaluation set with your subject-matter experts, then converts into a fixed-fee proposal with explicit gates.
Your repo
Your IP, day one
Code, infrastructure-as-code, evaluation harness, and runbooks land in your accounts — no vendor lock-in on the data, models, or evaluation history.
Related use-cases
Document Intelligence
Citation-grounded retrieval and summarisation over heterogeneous document corpora.
Internal Copilots
Role-shaped copilots over internal knowledge corpora — Confluence, runbooks, policies, code.
Evaluation Harnesses
Continuous, automated evaluation pipelines for production generative-AI systems.
FAQ
Structured Extraction — frequently asked questions
How is success measured for structured extraction engagements?
Measured by per-field F1 (precision and recall on extracted values) against a labelled holdout, plus calibration error on the per-field confidence scores so that low-confidence flags actually correspond to low accuracy. Throughput is tracked against the cost of human review the system displaces. The evaluation harness is part of the deliverable, not an afterthought — we build it during the engagement so your team can run it against the next prompt, model, or pipeline change without us.
Where does Veso AI NOT apply structured extraction?
We do not deploy extraction pipelines that write to downstream systems of record without explicit confidence gates and human review for low-confidence fields — auto-write to claims, billing, or trading systems is out of scope. This is a deliberate trust boundary, not a capability gap — we are equipped to build the systems we decline to build, and we decline to build them because the risk-to-value ratio in those surfaces does not justify it.
Which industries does structured extraction apply to?
In our coverage footprint, structured extraction most commonly lands in financial services, healthcare, energy, insurance. The specific deployment shape varies by industry — data shapes, evaluation criteria, and regulators differ enough that we re-scope each engagement against the sector it lands in.
What data shape do you need to start a structured extraction engagement?
PDFs, emails with attachments, scanned forms, EDI feeds, supplier documents, submission packets, and the target schema in the receiving system of record. During the paid two-week discovery we map the actual data surface — what exists, what is labelled, what residency posture it carries — and the proposal for the next gate is shaped against that, not against an assumption.
Which Veso AI services ship structured extraction?
structured extraction ships under our Generative AI Consulting, Custom Software Development, AI Integration Services, Data Science & Analytics service lines, depending on the integration surface and the build-vs-platform trade-off. Most engagements draw on more than one — the boundary between consulting, custom build, and integration is a scoping decision we make explicit during discovery.
How does a structured extraction engagement typically start?
With a paid two-week discovery: workshops with leadership and operators, an evaluation-set assembled with your subject-matter experts, and a fixed-fee proposal for the next gate. The evaluation set anchors every subsequent decision — model choice, prompt strategy, retrieval design — so quality is measurable from week one, not from go-live.
Industries where structured extraction applies
- Financial Services Generative AI for banks, asset managers, and capital markets — under explicit regulatory posture.
- Healthcare Tightly-scoped generative AI for clinical operations and document automation — never direct clinical decisioning.
- Energy Generative AI for technical document retrieval, regulatory submissions, and operational summarisation.
- Insurance Generative AI for claims summarisation, underwriting research, and submission-document classification.
- Retail Generative AI for search, product-attribute extraction, and post-purchase support.
- Logistics Generative AI for exception handling, document extraction, and customer-comms automation across TMS / WMS surfaces.
- Manufacturing Generative AI for supplier-document extraction, technical-manual retrieval, and quality-incident summarisation.
- Education Generative AI for curriculum drafting, accessibility tooling, and administrative automation — under explicit student-data residency.
- Media Generative AI for editorial research, asset tagging, and personalised distribution — with human-in-the-loop quality gates.
Service lines that ship structured extraction