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team@kovazu.comAI Document Intelligence & Compliance Pipeline
Operations SaaS Platform
Key Outcome
22 hrs/week saved in manual document review — 99.2% extraction accuracy at scale
The Challenge
The client's operations team was manually reviewing 300+ contracts and compliance documents per week — a process split across three team members, averaging 8 hours per person. Errors in extraction were creating downstream CRM data quality issues that were costing an estimated 6 additional hours per week in reconciliation. The team had tried a no-code OCR tool that delivered 71% accuracy and was subsequently abandoned.
Our Approach
We designed a multi-stage pipeline that separates document parsing, entity extraction, validation, and routing into distinct, auditable steps. Each stage has its own error handling and human-review escalation logic, so the system degrades gracefully on documents outside its training distribution rather than producing silent failures. Full integration was delivered in 5 weeks with zero downtime to the existing CRM.
Tech Stack
System Architecture
How the system flows
Build Pipeline
How We Built It
Document Ingestion & Pre-Processing
Documents arrive via email attachment, direct upload, or API from connected systems. A pre-processing layer normalises file formats (PDF, DOCX, scanned image), applies OCR where needed (AWS Textract for scanned documents), and classifies document type using a fine-tuned classifier with 97.4% accuracy across 14 document categories.
Structured Entity Extraction
Each classified document is processed by a Claude Opus 4.8 call with a document-type-specific extraction schema and strict structured-output enforcement. The model returns validated JSON covering all required contract fields: parties, dates, values, obligations, termination clauses, and custom fields per document type. Schema validation is enforced via Pydantic before any data is written.
Confidence Scoring & Uncertainty Flagging
Every extracted field receives a confidence score derived from model logprobs and cross-referenced against expected value formats. Fields below threshold (configurable per field type) are flagged for human review rather than auto-written to the CRM. This architecture is what delivers 99.2% accuracy — the model doesn't guess, it escalates.
Semantic Deduplication & Entity Resolution
Extracted party names and entities are matched against the existing CRM via vector similarity search on a Pinecone index of existing account records. Fuzzy matching handles variations in company naming ("Acme Ltd", "ACME Limited", "Acme") and surfaces potential duplicates for human confirmation before record creation.
CRM Integration & Async Routing
Validated extractions are written to the CRM via its REST API through a Redis-queued worker system, ensuring rate limits are respected and retries are handled gracefully. Routing rules determine whether a document triggers a new record, updates an existing one, or creates a linked document object. All writes are logged with the source document reference.
Human Review Interface
A lightweight internal web app (Next.js) surfaces documents requiring human review in a side-by-side interface: the original document on the left, the AI-extracted fields on the right, with flagged fields highlighted. Reviewers approve, edit, or reject extractions. All human decisions are logged and fed back into monthly model evaluation cycles.
Results
What We Delivered
22 hrs
Saved Per Week
The three-person document review function was freed from manual extraction entirely, redirecting effort to higher-value compliance analysis work.
99.2%
Extraction Accuracy
Measured across 4,000 documents in the first month of production, against a ground-truth set validated by the client's compliance team.
5 weeks
Delivery Timeline
Full pipeline design, build, integration testing, and CRM rollout completed in 5 weeks with zero disruption to live operations.
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TOGETHER
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