AI Development
Document AI (PDF Intelligence)
Turn messy PDFs and documents into structured data your systems can use. We build extraction pipelines, validation rules, and review flows—so automation is reliable and audit-friendly.
Overview
What this service is
Document AI covers parsing and extracting structured fields from PDFs, scanned documents, and forms—often with OCR and schema validation.
We implement workflows that handle real-world messiness: low-quality scans, missing fields, tables, and inconsistent formats.
Delivery includes review queues, confidence thresholds, and monitoring so automation improves over time without breaking compliance expectations.
Start with evidence
Begin with a focused opportunity sprint
In 1–2 weeks, we map one consequential operation, test its automation case, and define a governed implementation plan before a larger build.
Standard
AI delivery standard
Quality and safety practices we ship with AI builds so the system stays measurable, maintainable, and production-ready.
Logging + tracing
Conversation and tool traces with request IDs, error visibility, and debug-friendly runbooks.
Guardrails + safety
Tool allowlists, PII-safe patterns, refusal behavior, and escalation routes for edge cases.
Evals + regression tests
Golden queries, scorecards, and regression checks so quality improves over time instead of drifting.
Cost + latency controls
Caching, prompt discipline, retrieval tuning, and routing so your app stays fast and predictable at scale.
Documentation + handoff
Architecture notes, environment setup, and next-step roadmap so your team can iterate safely after launch.
Security-first integration
Secrets isolation, role-based access, audit-friendly actions, and minimal data retention by design.
Benefits
What you get
Reduce manual data entry and review effort
Handle messy PDFs with validation and fallbacks
Turn documents into clean JSON and database records
Add human review queues for edge cases
Improve accuracy with evals and sampling
Keep an audit trail for compliance workflows
Features
What we deliver
OCR + parsing pipeline
Extract text from scans, handle multi-page PDFs, preserve structure, and normalize outputs consistently.
Schema-based field extraction
Define the fields you need and extract into typed JSON with validation rules and confidence scoring.
Table and invoice-style documents
Capture line items and tabular data reliably with post-processing and reconciliation checks.
Human review workflow
Review queues for low-confidence outputs with side-by-side previews, edit controls, and approval logs.
Document search + Q&A (optional RAG)
Search across document libraries and answer questions with citations and permission-aware access.
Monitoring + quality iteration
Sampling, evaluation tests, drift tracking, and dashboards for extraction accuracy and failure modes.
Process
How we work
Discovery
Document types, required fields, and success criteria.
Prototype
Extraction baseline on sample documents.
Pipeline build
OCR/parsing, schema extraction, validation, and storage.
Review UX
Human review queue and audit logging.
Launch
Monitoring, sampling, and iteration plan.
Tech Stack
Technologies we use
Core
Tools
Services
Use Cases
Who this is for
Invoice and receipt processing
Extract vendor, totals, line items, and metadata with validation and review workflows.
KYC and onboarding documents
Capture required fields, verify completeness, and route to review with auditable trails.
Contract and policy extraction
Extract clauses, dates, obligations, and structured summaries with citations for verification.
Research PDF libraries
Search and compare across large document sets with citations and controlled access.
Operations forms digitization
Convert internal forms into structured records and workflow triggers for faster ops.
Implementation Patterns
How we frame common AI workflows
Illustrative patterns only—not client case studies, endorsements, or production-result claims.
Regulated mobile data workflow pattern
Challenge: Sensitive data workflows need explicit access boundaries, traceability, and documented operating responsibilities.
Approach: Threat-model the workflow, map authorization rules, select encryption controls, and define auditable state transitions.
Validation: Test access boundaries and recovery paths, record residual risk, and obtain any required independent compliance assessment.
Large knowledge-base retrieval pattern
Challenge: Long, mixed-format source collections need traceable retrieval and safe behavior when evidence is weak.
Approach: Evaluate hybrid retrieval, reranking, citations, structured outputs, and defined fallback or human-review paths.
Validation: Use a representative offline evaluation set and report citation quality, latency, and cost under documented test conditions.
Operations automation pattern
Challenge: Approval and system-sync workflows need deterministic controls around exceptions, retries, and ownership.
Approach: Model the workflow, add validation and approval gates, and use AI only for bounded classification or extraction tasks.
Validation: Baseline manual steps, test exception paths and audit logs, then compare pilot measurements before considering wider rollout.
Explore
Related solutions & technologies
Useful next pages if you’re planning an AI pilot or scaling this into a larger product.
Related solutions
Decision Guides
Not sure which to choose?
FAQ
Frequently asked questions
Yes. We use OCR to extract text and then apply schema extraction and validation, with review queues for low-confidence cases.
Yes. We implement table capture with post-processing checks to make sure totals and line items reconcile correctly.
We define validation rules, set confidence thresholds, add human review, and run evaluation tests on representative document samples.
We can store in your existing storage (S3/Blob) and keep metadata and extracted fields in your database with clear retention rules.
Yes. We can add document search + RAG Q&A with citations and permission-aware access on top of extraction workflows.
A sample set of documents, the fields you need, and any compliance or retention constraints.
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