DocAI is an AI-powered extraction engine that plugs directly into your .NET backend. Your users upload a document — a claim form, a contract, a loan application — and structured data auto-populates in seconds with field-level confidence scores. The entire pipeline runs inside your Azure subscription. Your tenant, your encryption keys, your network. No data ever leaves your wall.
30 minutes · Your actual documents · No commitment
Your users upload a PDF and then retype every field into your system. Or worse — you have an ops team of 4–6 people whose entire job is reading uploaded documents and keying data into forms. That's $250K+ a year in labor cost for work that software should be doing. Your product team has had "smart document upload" on the roadmap for 14 months. Your .NET engineers tried Azure Form Recognizer for a week and couldn't get accuracy above 70% on your document types. You evaluated ABBYY or Kofax — $80K–$150K in licensing, and you'd still need to build the integration yourself. Meanwhile, a competitor just shipped auto-extraction and your clients mentioned it on a churn call.
Document hits your existing upload endpoint. PDF, image, or scan.
AI identifies the document type and routes it to the correct extraction schema. A claim form doesn't go through the same pipeline as a bank statement.
Fields are pulled with per-field confidence scores. Structured data mapped to your data model, not raw text dumped into a blob.
Your business rules are applied. Confidence thresholds you set. Fields below your bar are flagged for human review. Everything above passes through automatically.
Extracted data maps directly to your database schema and saves. Your data model, not ours. Your engineers can read every line of the C# code.
A legal tech company's platform handles thousands of uploaded contracts, filings, and correspondence daily. Paralegals were manually reading each document, identifying the type, and extracting key fields — party names, dates, clause references, jurisdiction, case numbers — into the system. 8–12 minutes per document. Backlog growing faster than the team could clear it.
We deployed DocAI into their .NET/Azure stack in 4 weeks. The pipeline classifies legal documents by type, extracts relevant fields with confidence scores, and routes low-confidence results to human review. High-confidence data auto-populates their case management system. The entire architecture runs inside their Azure subscription — attorney-client privilege intact, no third-party processor to vet, compliance team signed off in a single review.
The entire pipeline runs inside your Azure subscription. Your encryption keys, your network, your audit logs. We never access your data from our infrastructure. Your compliance team reviews infrastructure they already own.
Not 4 months. Not "Phase 1 discovery." A production-ready extraction pipeline integrated into your .NET backend, processing real documents, in 4 weeks. Fixed price. Defined scope. Delivery date you can hold us to.
Clean C# code your .NET developers already understand. No Python. No Jupyter notebooks. No ML frameworks your team has never seen. After handoff, your existing engineers maintain and extend it.
HIPAA, SOC 2, PCI-DSS covered under Microsoft's certifications on your tenant. Private endpoints and customer-managed encryption keys available for regulated industries.
Azure-native stack · Zero vendor lock-in
90–99% field-level accuracy depending on document type and quality. Structured documents like forms and applications hit the high end. Every field comes with a confidence score. You set the threshold for what passes automatically and what gets routed to human review.
No. The entire pipeline — ingestion, classification, extraction, validation, storage — runs inside your tenant. We deploy via Infrastructure-as-Code into your Azure subscription. Your keys, your network, your logs. We are never a data processor.
Azure AI Document Intelligence is one of the services we use under the hood — it's a great extraction API. But using it and having a production-ready pipeline in your product are two different things. We build everything around it: ingestion pipeline, document classification, business rule validation, human review UI, confidence threshold routing, and persistence layer mapping to your database schema. The API is lumber. We deliver the house.
Yes — that's the design. Everything is written in C# by .NET engineers using patterns your team already knows. No exotic dependencies. No separate ML infrastructure. Your existing developers can maintain, tune, and extend the extraction pipeline from day one after handoff.
Every implementation includes prompt tuning specific to your document types. We test against your actual documents during the build, not generic samples. If accuracy on a specific field doesn't meet your threshold, we adjust extraction logic until it does — or we flag it for human review. You never ship something that doesn't meet your standard.
Send us 2–3 sample documents from your actual workflow. We'll show you exactly which fields can be auto-extracted, expected accuracy per field, and what the architecture looks like inside your Azure subscription.
30 minutes · Your actual documents · 1-page summary delivered after the call · No commitment