How is this different from off-the-shelf intelligent document processing (IDP) tools like Hyperscience or Rossum?

Hyperscience, Rossum, Vic.ai, and similar IDP platforms work great when your document types are common (invoices, receipts, ID cards) and your volume justifies the SaaS subscription ($30K to $80K per year per platform). We build custom pipelines when (1) your documents are unusual, (2) you need deep integration with non-standard systems, (3) you process less than 5,000 documents per month, or (4) you cannot send the documents to a third-party cloud. The IDP tools are the right answer for the obvious cases; we build the rest.

What's the typical extraction accuracy we should expect?

Depends on the document type. For standard structured documents (invoices, purchase orders), 95% to 99% accuracy on individual field extraction is achievable. For unstructured documents (contracts, legal memos), 85% to 92% on key-field extraction is realistic. The exception rate (documents that fail validation and need human review) is typically 5% to 15% even with high field-level accuracy. We tune both during the build.

Can we use this for HIPAA-regulated documents (medical records, claims)?

Yes, with the right architecture. The pipeline runs in your VPC (AWS, Azure, GCP) with HIPAA-eligible services. No data leaves your environment. We pair every healthcare document engagement with our AI Compliance and Governance service for HIPAA + HHS Section 504 + CMS-0057-F readiness. The compliance work is scoped separately so you can see the cost breakdown clearly.

How long until we see ROI?

Most well-scoped engagements pay back in 3 to 6 months. A common pattern: AP automation that took 40 hours/month per accountant drops to 4 hours of review. That is $60K to $90K/year saved per accountant. We run the ROI math on your actual volume in the proposal, not a generic estimate.

What document types do you handle?

Common: invoices, purchase orders, contracts (NDAs, MSAs, SOWs), insurance claims (FNOL, medical bills, police reports), tax forms (W-2, 1099, K-1), bank statements, receipts, application forms, KYC documents, SOPs and policies, technical specifications, healthcare records (HIPAA-bounded). Less common but supported: handwritten forms, multilingual documents, scanned PDFs with poor quality. The discovery call covers the specific types you have.

What if the LLM gets a critical extraction wrong?

Three layers of safety. (1) Confidence thresholds: any extraction below the threshold (typically 92%) routes to human review before action. (2) Validation rules: extracted values are checked against business rules before downstream actions (PO totals match invoice totals, dates are within expected ranges, etc.). (3) Reversibility: any action that cannot be undone (payment, contract acceptance) requires human approval. We never ship pipelines without all three in place.

Can we add new document types after the initial build?

Yes. Each new document type added to an existing pipeline typically takes 1 to 3 weeks and runs $10K to $25K depending on complexity. The underlying architecture is reusable; we are just adding extraction logic and validation rules for the new type. The maintenance retainer covers minor format changes within an existing document type, but new types are scoped separately.

AI Document Processing and SOP Automation

Document workflows are where AI pays back fastest

Most back-office work is document work. Invoices flowing into AP. Contracts moving through redlines. Claims docs landing in adjusters' queues. Forms being entered into systems by hand. Every one of these is repetitive, rule-based, high-volume work that AI handles well.

The trick is doing it right. Production document AI is not just OCR plus an LLM. It is structured extraction, validation against business rules, exception handling, and an audit trail every regulator will accept.

When to talk to us

Your AP team is drowning in invoices. 40+ hours per month per accountant on data entry.
Your legal team spends days redlining contracts that mostly differ in 3 to 5 standard clauses.
Your claims team has adjusters reading and classifying claim documents one at a time.
Your operations team has SOPs spread across PDFs, Confluence, Notion, and SharePoint that nobody can find when they need them.
Your compliance team wants AI-assisted document review but cannot use a public API on regulated data.

What you get

Three project tiers plus an ongoing maintenance retainer.

Single Document Pipeline ($25,000 to $50,000, 4 to 6 weeks)

One document type, one workflow. Example: AP automation processing invoices from 50 to 200 vendors. Or contract intake auto-classifying NDAs vs MSAs vs SOWs.

What's included:

Discovery and document sample analysis (we look at 50 to 100 real examples of your documents).
Document ingestion pipeline (email, watch folder, API webhook, etc.).
OCR + structured extraction (vendor, amount, dates, terms, custom fields).
Validation against business rules (PO matching, approval thresholds, tax calculations).
Human-in-the-loop review for low-confidence extractions.
Output to your accounting system, CRM, or document store.
Audit trail and logging for every document processed.
Acceptance test on 100 of your real documents.
2 weeks of post-launch tuning included.

Multi-Document Pipeline ($50,000 to $120,000, 8 to 12 weeks)

For processes that handle 2 to 4 connected document types. Example: end-to-end procurement (PO + invoice + receiving doc + contract). Or insurance claim intake (FNOL + medical bills + police report).

Everything in Single Pipeline, plus:

2 to 4 document types with shared validation logic.
Cross-document reconciliation (matching POs to invoices, claim docs to policy data).
Workflow orchestration across the pipeline.
Exception routing to the right team for each document type.
Consolidated dashboard across all pipelines.
4 weeks of post-launch tuning.

Enterprise SOP Automation ($120,000 to $250,000, 12 to 20 weeks)

For organizations that want their entire SOP library searchable, queryable, and actionable via AI. Includes a RAG layer over your SOPs plus agents that execute them.

Everything in Multi-Document Pipeline, plus:

SOP corpus ingestion (PDFs, Confluence, Notion, SharePoint, Google Docs).
Semantic search across all SOPs with citation.
AI agents that execute SOP-defined workflows (with human approval for irreversible actions).
Cross-SOP coordination (one workflow can pull from multiple SOPs).
Compliance audit trail (every action logged with the SOP version that triggered it).
6 weeks of post-launch tuning.

Ongoing maintenance retainer ($2,500 to $5,000 per month)

For projects that have shipped. Document workflows drift as vendors change form templates and as your business rules evolve.

Monthly health check on extraction accuracy, exception rate, and rule violations.
Up to 4 hours of pipeline tuning per month.
Same-week response when accuracy drops.
Vendor change tracking (when accounting software updates its UI or schema).

Compliance fit

Pairs naturally with our AI Compliance and Governance service for regulated industries (healthcare, financial services, insurance, legal). HIPAA-compliant deployments, SOC 2 audit trails, and EU AI Act conformity assessments are scoped together.

Guarantee

Every pipeline has to hit its agreed extraction accuracy on 100 real documents before we send the final invoice. If we miss it, we rebuild on our dime.

Payment terms

50% on contract signing. 25% at midpoint. 25% on acceptance. INR pricing on request for India-based clients.

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Not sure if this is the right page? This is for high-volume documents arriving and needing to be read, extracted and routed. If your problem is a multi-step process moving work between tools, start at AI Agents and Workflow Automation.