Questions About the DocAI Platform

DocAI is a modular document validation platform that combines intelligent AI extraction with a deterministic, rule-based validation layer and Human-in-the-Loop (HITL) governance. It is built for compliance-critical workflows such as MTR processing, PPAP generation and review, CBAM emissions reporting, AP invoice automation, and digital KYC onboarding, and it pushes 100% validated data directly into ERP, CRM, or core banking systems.

Standard OCR and generic extraction tools digitize text but stop there. DocAI treats extraction as only the first step: every extracted value is then cross-referenced against your internal spec libraries, master data, purchase orders, or regulatory limits through a deterministic rule-based engine, with a Human-in-the-Loop queue handling anything that falls outside your configured tolerances. See a detailed, sourced comparison against Google Document AI, AWS Textract, UiPath, and ABBYY.

Audit-ready means every piece of data the platform outputs can be traced back to its original source document, the validation rule that checked it, and the identity of any human who reviewed or modified it. DocAI logs an immutable audit trail for every document processed, so auditors can verify data provenance instead of trusting an unverifiable AI output.

When AI confidence drops below a pre-set threshold, or a rule-based check fails (a mismatch, missing field, or out-of-spec value), the system halts straight-through processing and routes that specific item to a HITL interface for manual review by your team or Aekam's back-office reviewers. It is optional but recommended for compliance-sensitive fields; DocAI can also be configured for 100% straight-through processing where risk tolerance allows.

Rule-Based Validation is a secondary, deterministic layer that runs after AI extraction. Engineers write specific validation logic — ranges, spec-library lookups, PO cross-references, tolerance checks — based on your internal standards, so every extracted value is verified mathematically rather than merely trusted because a model produced it.

Aekam AI currently supports five production modules: MTR validation for manufacturing, PPAP document generation and review for automotive/industrial quality teams, CBAM emissions reporting for EU carbon compliance — extended by CBAM Copilot into a full carbon evidence platform for exporters, importers and verifiers — AP invoice automation with 3-way matching for finance teams, and Digital Onboarding & KYC automation for banking, NBFC, and fintech customer onboarding.

DocAI's rule engine is configured against the specific standards each customer requires — commonly ASTM and ASME material specifications and EN 10204 3.1 certificates for MTR validation, AIAG PPAP element requirements for automotive/industrial quality, and the EU CBAM Communication template and Specific Embedded Emissions (SEE) methodology for carbon reporting.

Engagements follow five stages: Workflow Discovery (identifying bottlenecks, data sources, and ERP endpoints), a Proof of Concept where the base DocAI engine runs on your own historical documents in a sandbox, Rule Configuration (engineers write validation logic bound to your standards), Production Deployment (API integration and HITL rollout), and Ongoing Governance (continuous rule and model tuning).

No. The Proof of Concept stage runs the base DocAI engine against your own historical documents in a sandbox before any rule configuration or production commitment, so you can evaluate extraction quality on real data first.

Yes. Once data passes AI extraction and rule-based/HITL validation, it is pushed via secure REST API directly into systems such as SAP, Oracle, NetSuite, Dynamics 365, or core banking platforms, triggering downstream workflows like payment or account activation.

It is configurable. You can run DocAI for 100% straight-through processing where your risk tolerance allows, or keep Human-in-the-Loop review active for compliance-sensitive fields. In practice, most deployments route only the exceptions — low-confidence extractions or rule failures — to a human, not entire documents.

DocAI is available as a secure, managed multi-tenant cloud environment or as a dedicated private instance for customers with stringent compliance or data-isolation requirements. The processing model is stateless and horizontally scalable to absorb volume surges like month-end invoicing or bulk historical migrations.

Yes. Regional deployments are configurable so that data processing and storage stay within your required geographic boundaries, supporting data residency and sovereignty requirements.

Data is encrypted in transit and at rest, access is governed by role-based access control (RBAC), and every processed document generates an immutable audit log recording the original extraction, the rules applied, and any human edits.

Yes. DocAI is not a one-size-fits-all tool. Every deployment involves custom-configured validation logic — a "Validation Moat" built around your specific quality standards, spec libraries, and supplier document formats.

Depending on the module, DocAI supports mobile app capture (e.g., factory-floor photos), automated email fetch from dedicated inboxes, and secure supplier or customer portals for direct uploads — all feeding the same extraction and validation pipeline.

DocAI is built for compliance-critical, document-heavy organizations — manufacturers processing MTRs and PPAP packages, importers managing CBAM reporting, finance teams automating AP invoicing, and banks or fintechs handling KYC onboarding — where the cost of an unvalidated AI error is a failed audit, a regulatory penalty, or a compliance violation.