Docy AI helps regulated teams replace manual review and BPO-heavy document workflows with auditable AI processing—including structured extraction, cross-check validation, exception-first routing, and exportable evidence packs.

What you’ll learn

• Why manual and BPO-based compliance workflows fail at scale

• Why auditability and traceability are now operational requirements

• What “compliance-grade” means in practice (logs, versioned rules, human-in-loop, data controls)

• How Docy Engine + Studio + Marketplace work together

• Outcome-based case snapshots (energy + credit assessment)

• A buyer checklist to evaluate Document AI for regulated workflows

• Roadmap and the creator ecosystem (network effects)

Contents

1. Overview

2. The Problem: Manual/BPO Compliance Workflows

3. Why Now: Regulators, Auditors, and Traceability Expectations

4. What “Compliance-Grade” Means

5. Reference Architecture: Engine + Studio + Marketplace

6. Case Snapshots: Energy + Credit Assessment

7. Buyer Checklist: Evaluating Document AI for Regulated Workflows

8. Roadmap + Creator Ecosystem

9. Next Steps

1. Overview

Regulated workflows are document-heavy by design: evidence packs, forms, certificates, photos, invoices, statements, IDs, and policy-driven checklists. The operational problem is not “having documents”—it’s ensuring each case is processed consistently, fast, and in a way that can be audited.

Docy AI is built for that reality. We turn rules and operational know-how into AI workflows that:

• extract structured fields from messy inputs

• validate and cross-check across multiple sources

• route exceptions to humans (not everything)

• generate traceable outputs for reviewers and auditors

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2. The Problem: Manual/BPO Compliance Workflows

Most compliance operations still rely on combinations of:

• manual review and spreadsheet tracking

• offshore BPO processing

• email-based intake and follow-ups

• checklists stored in documents (not systems)

This creates predictable failure modes:

Common failure modes

• Incomplete evidence packs → rework loops, delays, customer frustration

• Inconsistent interpretation across reviewers → audit risk and disputes

• Policy updates not reflected in day-to-day operations → non-compliance exposure

• Limited traceability (who checked what, when, and why) → weak audit defensibility

• Costs scale with headcount → operational bottlenecks

The result: compliance teams spend most of their time on repetitive checks instead of exception handling and quality control.

3. Why Now: Regulators, Auditors, and Traceability Expectations

Across regulated industries, the expectations are moving from “process the work” to “prove the work.”

What is changing:

• Auditability is a requirement: decisions must be reproducible and explainable

• Evidence handling must be controlled: versioning, retention, and access governance matter

• Operational consistency matters as much as speed: especially in high-volume workflows

This is why “generic automation” or “LLM-only” approaches often struggle in regulated environments: they automate steps, but don’t reliably produce an audit-ready decision trail.

4. What “Compliance-Grade” Means

“Compliance-grade” is not just accuracy. It’s controls + evidence.

Docy AI is designed to support:

4.1 Audit logs and traceability

• case-level traceability (inputs, checks, outputs, reviewers, timestamps)

• decision trail (auto-cleared vs flagged, and why)

• exportable evidence for internal review or regulators (where enabled)

4.2 Versioned rules (change control)

• checklists and policy rules can be versioned with effective dates

• teams can track what changed and apply updates without breaking operations

4.3 Human-in-the-loop by design

• exception-first routing

• configurable approval gates for high-impact steps (e.g., submissions/approvals)

4.4 Data controls (privacy & residency-ready)

• access controls and role-based permissions

• retention/deletion aligned to policy

• infrastructure designed to support data residency requirements (where required)

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5. Reference Architecture: Engine + Studio + Marketplace

Docy AI is composed of three layers that work together:

Docy Engine (processing + validation)

• document ingestion and parsing

• structured extraction

• cross-check validation across files and fields

• comparisons (e.g., document vs photo evidence)

• output generation (CSV/JSON/reports)

Docy Studio (build and configure workflows)

• no-code / low-code agent builder

• reusable workflow templates

• checklists and rules configured for specific policies

• exception handling and human review flows

Docy Marketplace (deploy and scale expertise)

• publish and deploy ready-made AI Workers

• creators can package industry know-how into reusable agents

• teams can adopt proven templates instead of rebuilding from scratch

Simple flow (for your diagram section):
Intake (Portal/Email/API) → Classify → Extract → Validate → Exceptions Queue → Human Review → Output + Audit Pack

6. Case Snapshots: Energy + Credit Assessment

We keep this section outcome-focused. Detailed metrics and customer references can be shared upon request.

Energy compliance workflows (B2B2C)

Docy AI supports evidence-pack workflows by automating:

• intake + standardisation of files into a case record

• classification and completeness checks

• extraction and cross-checks across documents and photo evidence

• exception routing to reviewers with reasons and recommended actions

Outcomes observed: minutes-level processing, fewer rework loops, more consistent checking, and exception-first review that reduces reliance on purely manual processing.

Credit assessment workflows (API)

Docy AI supports credit workflows by automating:

• extraction and validation of income and supporting evidence

• structured outputs for downstream systems

• exception-first handling to keep decisions auditable

Outcomes observed: faster processing, consistent checks, and cleaner handoffs into internal systems.

Optional one-liner: Detailed metrics are available upon request (Security Pack / customer-approved references).

7. Buyer Checklist: Evaluating Document AI for Regulated Workflows

Use this checklist to evaluate whether a solution is truly “compliance-grade.”

Compliance & auditability

• Can we reproduce “what happened” for each case?

• Are decisions traceable (inputs → checks → outputs → reviewer actions)?

• Can we export audit-ready evidence packages?

Rules & change management

• Are rules versioned with effective dates?

• Can policy updates be deployed without breaking operations?

Human-in-the-loop

• Is exception handling first-class (not bolted on)?

• Can we configure approval gates for high-impact steps?

Security & privacy

• Is data encrypted in transit and at rest?

• Are access controls role-based?

• Are retention/deletion controls available?

• Can the vendor support residency and procurement requirements?

Integrations

• Can we ingest via portal/email/API?

• Can we export clean structured outputs (CSV/JSON) reliably?

Commercials

• Does pricing align to volume (outcome-based) rather than headcount?

• Can we forecast cost as volume scales?

8. Roadmap + Creator Ecosystem

Docy AI’s long-term vision is a Compliance OS—infrastructure that converts policy and operational know-how into deployable, auditable workflows.

The Marketplace ecosystem compounds value:

• more creators → more workflow templates

• more templates → faster deployments and lower implementation cost

• more deployments → more feedback loops and higher-quality agents

This is how compliance automation becomes scalable across industries without rebuilding from scratch each time.

9. Next Steps

If you process regulated documents at scale, Docy AI can help you move from manual review to auditable AI workflows.

FAQ

Is Docy AI a chatbot?
No. Docy AI is workflow-driven for regulated operations, focusing on structured extraction, validation, exception handling, and auditability.

Can we keep humans in the loop?
Yes. Exception-first routing and configurable approval gates are core to the design.

Do you support procurement/security reviews?
Yes. We provide a Security Pack, including documentation for vendor onboarding.

Can this integrate with our current intake process?
Yes. Intake can be portal-based, email-based, or API-based depending on your workflow.