AI Document Processing
Intelligent document processing that reads invoices, contracts, IDs and forms, extracts validated data and routes exceptions.
- Understand requestIntent: refund · order #4821
- Retrieve policyRefund policy v3 · 2 sources cited
- Call toolsorders.lookup · payments.status
- 4Human approvalRefund above auto-approve limit
- 5Execute actionpayments.refund
- 6Respond & logCustomer reply · audit trail
Tool call
orders.lookup({
order_id: "4821"
}) → { status: "delivered",
amount: 42.00 }Approval required
Refund 42.00 to original payment method
Division
Service area
AI Agents & Intelligent Automation
Team
Engagement
Project · Team · Managed
Overview
Documents still drive much of business: invoices, bank statements, contracts, identity documents, claims forms, shipping papers. We build document processing pipelines that combine OCR, layout analysis, vision-language models and validation rules to extract structured data accurately, check it against business rules and systems of record, and send only genuine exceptions to people. Every extracted field carries its source location and confidence for auditability.
Common use cases
- Accounts payableInvoice capture, line-item extraction and PO matching.
- Lending and onboardingBank statements, payslips and identity documents parsed and validated.
- Insurance claimsClaims forms, reports and receipts extracted and checked against policy data.
- Logistics documentsBills of lading, customs forms and delivery notes digitised.
Quick answers
AI Document Processing at a glance
The essentials in brief. Every project is scoped individually — ask us for specifics.
- What is AI document processing?
- Intelligent document processing that reads invoices, contracts, IDs and forms, extracts validated data and routes exceptions.
- Who is it for?
- Typically product companies adding AI features, enterprises automating knowledge work, and teams whose AI pilot needs to become a dependable production system.
- What does Shivacha provide?
- Document classification
- Field extraction
- Validation rules
- Review interface
- Integrations
- Accuracy monitoring
- Which technologies are used?
- AI Agents, Large Language Models, OpenAI Models, Retrieval-Augmented Generation, Python, Node.js — chosen to fit your stack and constraints.
- How does the process work?
- Map the workflow → Choose the autonomy level → Build tools and guardrails → Shadow mode → Expand autonomy.
- What affects the cost?
- Number and quality of data sources
- Accuracy targets and evaluation effort
- Integrations with business systems
- Model choice, hosting and per-request cost
- Human-in-the-loop and audit requirements
- Data residency and privacy constraints
- How long does it take?
- A scoped proof of value usually takes 4–8 weeks; a production system with evaluation, integrations and guardrails typically takes 3–6 months.
- How do I get started?
- Share a short brief in the form below, book a 30-minute call or message us on WhatsApp. A senior engineer replies within one business day; NDA on request.
Capabilities
What we deliver
Document classification
Automatic identification of document type and routing.
Field extraction
Key-value, table and line-item extraction with source highlighting.
Validation rules
Cross-checks against business rules, totals and external systems.
Review interface
Side-by-side verification UI for low-confidence fields.
Integrations
Push validated data into ERP, core systems or data warehouses.
Accuracy monitoring
Field-level accuracy tracking and retraining loops.
Architecture
Engineered right from day one
The layers we typically design for AI agents & intelligent automation, adapted to your stack and partners.
- Least-privilege toolsAgents receive narrowly scoped tools rather than broad system access.
- Deterministic where possibleRules, schemas and code handle what does not need a model; the model handles judgement.
- Full auditabilityEvery tool call, input and output is logged for review and debugging.
- Graceful failureUncertain or failed steps route to an exception queue instead of guessing.
Delivery
How an engagement runs
- 1
Map the workflow
Document the current process, decisions, systems touched, exceptions and who owns each step.
- 2
Choose the autonomy level
Decide which steps are fully automated, which are suggested, and which always need a human.
- 3
Build tools and guardrails
Expose narrow, well-typed tools with scoped permissions and validation.
- 4
Shadow mode
Run the agent alongside humans, compare outcomes and tune before it acts on its own.
- 5
Expand autonomy
Increase automation per task as measured reliability justifies it.
Security
Security built into delivery
Controls we apply by default on this kind of work — not a separate phase at the end.
Data boundaries
Permission-aware retrieval so users only see answers from documents they may access.
Guardrails
Input and output checks, tool allow-lists and human approval for consequential actions.
No training on your data
Provider settings and contracts chosen so your data is not used to train third-party models.
Audit trail
Prompts, sources, tool calls and approvals logged for review.
Technology
Tools we use for this
Products
Start from a platform
Shivacha Document AI
Extract, validate and route data from any business document.
Learn moreShivacha Logistics
Dispatch, tracking and delivery operations in one platform.
Learn moreShivacha Agent Platform
Build, govern and operate AI agents that do real work across your systems.
Learn moreRelated services
Often combined with
AI Agent Development
AI agents that plan, use tools and complete multi-step business tasks — with scoped permissions, approvals and full audit trails.
Learn moreAgentic AI Development
Multi-agent and agentic systems that coordinate specialised agents across long-running workflows with supervision and control.
Learn moreAI Automation
Automate document-, email- and decision-heavy work by combining AI with rules, integrations and existing automation tools.
Learn moreDedicated team
AI Agent Team
Specialists in agentic systems, tool integration and workflow automation.
Work & insights
Related thinking
Agentic claims intake with human approval
A reference design for an AI workflow that reads claim submissions, extracts and validates data, and prepares cases for adjusters.
Learn morePermission-aware enterprise knowledge assistant
How we design a RAG assistant that answers from thousands of internal documents while respecting every user's access rights.
Learn moreAI development cost: what you pay for when you build an AI product or agent
Model fees are rarely the main cost. Data preparation, evaluation, integrations and guardrails decide both the budget and whether the system works.
Learn moreFAQ
Frequently asked questions
Does it work on scanned and handwritten documents?
Scanned documents work well with modern OCR and vision models; handwriting accuracy varies by quality and language. We benchmark on your samples before committing to accuracy targets.
How is sensitive document data protected?
Processing can run in your cloud environment with encryption, access control and retention policies; only permitted services receive document content.
What is the difference between automation and an AI agent?
Traditional automation follows fixed rules. An AI agent can interpret unstructured input, decide which steps to take and adapt to variations — while still being constrained by the tools and policies you give it. Most production systems combine both.
Are agents safe to connect to production systems?
They can be, with scoped credentials, validated tool inputs, approval gates for consequential actions, rate limits and complete action logs. We introduce autonomy gradually, starting in shadow mode.
Next step
Build Your AI Product.
Tell us about your AI document processing requirements — goals, timeline and constraints. We will reply with questions, an approach and next steps.
- Senior engineer reads every enquiry
- Reply within one business day
- NDA on request
Your details are used only to reply to this enquiry.