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

Off-the-shelf tools such as Hyperscience, Rossum, and Vic.ai are the right answer when your documents are common (invoices, receipts, ID cards) and your volume is high enough to justify an ongoing SaaS subscription. A custom pipeline is the better call when at least one of these holds: your documents are unusual, you need deep integration with systems that are not standard, you process a modest volume of documents per month, or you cannot send the documents to a third-party cloud. These platforms handle the obvious cases well. This service covers what they do not.

What extraction accuracy should we expect?

There is no single number that applies across document types, so the target is set per project rather than promised up front. Structured documents (invoices, purchase orders) extract more reliably than unstructured ones (contracts, legal memos), because the fields sit in more predictable places. Before the build starts, we agree on a target accuracy for your specific documents. The acceptance test then validates it: 100 of your real documents, checked field by field, before the final invoice goes out.

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, so no data leaves your environment. Healthcare document work pairs with the AI Compliance and Governance service for HIPAA, HHS Section 504, and CMS-0057-F readiness. That work is scoped and priced as a separate line item so the cost breakdown stays clear.

How long until we see ROI?

There is no generic timeframe to quote, because payback depends entirely on your document volume and on what manual processing costs you today. We run that math against your actual numbers, not a template, and put it in the proposal before you commit to a tier.

What document types do you handle?

| Category | Examples | |---|---| | Finance | Invoices, purchase orders, receipts, bank statements | | Legal | Contracts (NDAs, MSAs, SOWs) | | Insurance | Claims (FNOL, medical bills, police reports) | | Tax and identity | W-2, 1099, K-1, KYC documents | | Operations | SOPs and policies, technical specifications, application forms | | Healthcare | Records bounded by HIPAA requirements | | Harder cases (supported) | Handwritten forms, multilingual documents, scanned PDFs with poor quality | Bring the specific types you have to the discovery call. That is where scope gets set.

What if the LLM gets a critical extraction wrong?

Three layers of safety catch this before it reaches your systems. First, confidence thresholds. Any extraction below an agreed threshold, set per document type, routes to a human for review before any action is taken. Second, validation rules. Extracted values are checked against business rules before downstream actions run: PO totals match invoice totals, dates fall within expected ranges, and so on. Third, reversibility. Any action that cannot be undone, such as a payment or a contract acceptance, requires human approval. All three are non-negotiable before a pipeline goes live.

Can we add new document types after the initial build?

Yes. Adding a document type to an existing pipeline typically takes 1 to 3 weeks and is priced at $10,000 to $25,000 depending on complexity. The underlying architecture is reusable, so the work is mostly new extraction logic and validation rules. The maintenance retainer covers minor format changes within an existing document type. A genuinely new type is scoped and priced separately.

AI Document Processing and SOP Automation

Where document AI pays back fastest

Most back-office work is document work. Invoices flow into AP. Contracts move through redlines. Claims documents land in adjusters' queues. Forms get 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 a regulator will accept.

When to talk to us

Your AP team spends significant time on manual data entry from invoices.
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.

Tier	Price	Timeline	Scope
Single Document Pipeline	$25,000 to $50,000	4 to 6 weeks	One document type, one workflow
Multi-Document Pipeline	$50,000 to $120,000	8 to 12 weeks	2 to 4 connected document types, cross-document reconciliation
Enterprise SOP Automation	$120,000 to $250,000	12 to 20 weeks	Full SOP library made searchable and actionable by agents
Maintenance retainer	$2,500 to $5,000 per month	Ongoing	Health checks, tuning, and vendor-change tracking after launch

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

One document type, one workflow. An example is AP automation that processes invoices from 50 to 200 vendors. Another is contract intake that auto-classifies 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, and similar entry points.
OCR plus structured extraction of vendor, amount, dates, terms, and custom fields.
Validation against business rules such as PO matching, approval thresholds, and tax calculations.
Human 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. An example is end-to-end procurement: PO plus invoice plus receiving document plus contract. Another is insurance claim intake: FNOL plus medical bills plus police report.

Everything in Single Pipeline, plus:

2 to 4 document types with shared validation logic.
Cross-document reconciliation: matching POs to invoices, claim documents 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 by AI. It includes a RAG layer over your SOPs plus agents that execute them.

Everything in Multi-Document Pipeline, plus:

SOP corpus ingestion from PDFs, Confluence, Notion, SharePoint, and 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, so 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 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, for when accounting software updates its UI or schema.

Compliance fit

Pairs with the AI Compliance and Governance service for regulated industries: healthcare, financial services, insurance, legal. HIPAA-aligned deployments, SOC 2 audit trails, and EU AI Act conformity assessments are scoped alongside the document pipeline and priced separately.

Guarantee

Every pipeline must hit its agreed extraction accuracy on 100 real documents before the final invoice goes out. If it misses, the pipeline gets rebuilt at no extra cost.

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.