We are not building another OCR product, another document repository, or another AI chatbot. Accentus is building the evidence-governed intelligence layer between authoritative information and high-trust decisions.
Scanning companies can digitize documents.
Content-management platforms can store and retrieve them.
AI platforms can summarize and answer questions about them.
Accentus connects those capabilities into an evidence-governed intelligence architecture designed to preserve the relationship between authoritative information, authorized access, derived intelligence, supporting evidence, and accountable decisions.
Our differentiation is not OCR, a language model, vector search, or a knowledge graph individually. Those technologies are increasingly available across the market.
Our differentiation is how they operate together.
Evidence-Governed Intelligence
Accentus separates original, authoritative information from machine-derived intelligence.
OCR text, summaries, classifications, extracted entities, relationships, embeddings, and AI answers can enrich the source—but they do not silently become the source.
This distinction is especially important in regulated and high-consequence environments where organizations must know what the source actually states versus what a machine inferred from it.
We do not rely on a prompt telling an AI model not to disclose restricted information.
The architecture is designed so identity, role, policy, information sensitivity, and other authorization attributes determine what evidence may be retrieved before that evidence enters an AI workflow.
If the user is not authorized to retrieve the evidence, the AI should not receive it.
Enforced in retrieval—not in a prompt.
Restricted records never reach the model.
Different questions require different forms of retrieval. A contract number may require exact search. A conceptual question may require semantic search. A relationship among people, organizations, events, contracts, or records may require graph-based discovery.
One retrieval method is not sufficient for every intelligence problem.
Exact keyword
Contract numbers, case IDs, names
Metadata & structured
Dates, series, owners, types
Semantic
Conceptual questions
Relationship & graph
People, orgs, events, records
Re-ranking
Prioritize the strongest evidence
AI-assisted research
Synthesis across the evidence set
Controlled Evidence Set
Authorized · ranked · traceable
High-trust AI should not simply produce an answer. Accentus designs systems to maintain the connection between material conclusions and the information supporting them.
The objective is not merely citation. The objective is reconstructable evidence.
Conclusion
“The series was approved for transfer under the applicable schedule.”
Accentus uses AI to accelerate discovery, classification, summarization, comparison, research, relationship analysis, and recommendations.
Where regulation, mission risk, professional responsibility, or organizational policy requires human authority, the architecture preserves that decision boundary.
AI assists. Accountability remains.
Accentus is intentionally model-, cloud-, OCR-, search-, and database-agnostic. Better technology should improve the environment rather than force the customer to redesign it.
The stable layer is: source authority → access → evidence → intelligence → validation → accountable action. The technical components underneath that architecture can evolve.
The market already contains excellent records-management, document-processing, content-intelligence, digital-preservation, and AI platforms. Accentus is not attempting to reproduce every capability of those companies. We are concentrating on the connective layer between authoritative information and defensible AI-assisted decisions.
| Market Approach | Primary Strength | Typical Focus | Accentus Position |
|---|---|---|---|
| Traditional scanning / OCR | High-volume digitization | Convert physical documents to digital files and text | Accentus begins here when necessary, then continues into intelligence, search, evidence, governance, and decision support |
| Enterprise Content Management | Storage, workflows, content governance | Manage enterprise documents and content | Accentus can operate with existing repositories rather than requiring every authoritative system to be replaced |
| Intelligent Document Processing | Extraction and automation | Classify documents and extract structured information | Accentus treats extraction as one stage in a larger Source-to-Decision architecture |
| Digital Preservation | Long-term integrity and accessibility | Preserve records and formats over time | FRIP combines preservation requirements with search, relationships, governed AI, and research intelligence |
| Enterprise AI / RAG | Search, summarization, question answering | Give AI access to organizational content | Accentus adds explicit source authority, pre-retrieval authorization, evidence lineage, validation, and human decision boundaries |
| Accentus AI | Evidence-governed intelligence | Connect authoritative information to defensible AI-assisted decisions | Source integrity + authorized retrieval + hybrid discovery + provenance + evidence + human accountability |
Strength: High-volume digitization
Focus: Convert physical documents to digital files and text
Accentus begins here when necessary, then continues into intelligence, search, evidence, governance, and decision support
Strength: Storage, workflows, content governance
Focus: Manage enterprise documents and content
Accentus can operate with existing repositories rather than requiring every authoritative system to be replaced
Strength: Extraction and automation
Focus: Classify documents and extract structured information
Accentus treats extraction as one stage in a larger Source-to-Decision architecture
Strength: Long-term integrity and accessibility
Focus: Preserve records and formats over time
FRIP combines preservation requirements with search, relationships, governed AI, and research intelligence
Strength: Search, summarization, question answering
Focus: Give AI access to organizational content
Accentus adds explicit source authority, pre-retrieval authorization, evidence lineage, validation, and human decision boundaries
Strength: Evidence-governed intelligence
Focus: Connect authoritative information to defensible AI-assisted decisions
Source integrity + authorized retrieval + hybrid discovery + provenance + evidence + human accountability
| Digitize | Store & Manage | Extract | Preserve | Search & Answer | Govern Evidence & Decision | |
|---|---|---|---|---|---|---|
| Scanning / OCR | ||||||
| Enterprise Content Mgmt | ||||||
| Intelligent Doc Processing | ||||||
| Digital Preservation | ||||||
| Enterprise AI / RAG | ||||||
| Accentus AI |
↑ Vertical axis: emphasis on evidence governance & decision accountability
Competitive comparisons are based on publicly documented product capabilities and are intended to explain architectural positioning—not to claim superior production performance or certifications.
Accentus does not need to out-scan a global records company, out-store an enterprise content-management provider, or build a new foundation model. Our opportunity is to own the evidence-governed intelligence layer that connects those technologies.
That is the Accentus layer.
The first phase of enterprise generative AI focused on whether a model could produce useful answers. The next phase asks harder questions:
Accentus is being built around those questions.
AI capability should be demonstrated against the customer’s actual information and workflow—not assumed from a vendor demonstration. Accentus establishes measurable acceptance criteria during pilots and production deployments.
Measure whether access restrictions are correctly enforced before retrieval.
Measure whether AI-generated or machine-extracted information can be traced to its parent source, processing event, and applicable version information.
Verify that every cited source actually exists in the retrieved evidence set.
Measure whether material generated claims are actually supported by the cited evidence—not merely whether the cited document exists.
Measured with established information-retrieval metrics.
Precision, recall, F1, character accuracy, field accuracy, or human validation—measured separately by content type.
Evaluates whether answers are supported, complete, and appropriately withheld.
Monitors the health of the human decision boundary.
Defined per environment and written into acceptance criteria.
We do not publish unvalidated benchmark numbers as product claims. Performance is established against representative customer data during the proof phase and becomes part of the deployment acceptance criteria.
Federal records modernization and responsible AI adoption are not speculative future requirements. Agencies are already operating under records-management, digitization, information-security, and AI-governance requirements that create demand for architectures like FRIP.
Establishes federal responsibilities for creating, managing, preserving, and disposing of federal records.
Lifecycle-aware records architecture, provenance, records classification, preservation, authorized disposition, and auditability.
Federal agencies were directed to manage permanent records electronically to the fullest extent possible for eventual NARA transfer, with the June 30, 2024 transition milestone.
Digitization, authoritative electronic records, metadata, preservation, lifecycle controls, and transfer preparation.
Establishes requirements for digitizing temporary federal records.
Controlled digitization, image quality, validation, metadata, source integrity, and records lifecycle support.
Establishes mandatory digitization standards for applicable permanent federal paper records and photographic prints.
Digitization quality, preservation, metadata, validation, provenance, and transfer readiness.
Explains how agencies must apply the Federal Records Act definition to AI inputs, outputs, data, audit trails, software, and other AI-related materials, and confirms that AI-related federal records may only be disposed of under an approved records schedule.
Separation of authoritative and derived information, AI interaction logging, provenance, lifecycle review, and records-management determination.
Federal AI policy encourages agency AI adoption while requiring additional risk management for high-impact AI and protection of privacy, civil rights, and civil liberties.
Use-case risk assessment, human decision boundaries, evidence validation, governance, and measurable AI assurance.
Federal AI acquisition policy emphasizes efficient AI procurement, performance-based requirements, competition, and avoidance of unnecessary vendor lock-in.
Technology-neutral architecture, measurable acceptance criteria, replaceable AI components, and outcome-based pilot evaluation.
| Framework / requirement | Source Authority | Access Control | Provenance & Evidence | AI Validation | Human Oversight | Records Lifecycle | Security Ops |
|---|---|---|---|---|---|---|---|
| NIST AI RMF | |||||||
| NIST AI 600-1 Generative AI Profile | |||||||
| NIST SP 800-53 Rev. 5.2.0 | |||||||
| NIST SP 800-207 Zero Trust | |||||||
| NIST SP 800-171 Rev. 3 | |||||||
| Federal Records Act | |||||||
| M-23-07 | |||||||
| 36 CFR 1236 D/E | |||||||
| NARA AI Guidance AC 11.2026 | |||||||
| OMB M-25-21 | |||||||
| OMB M-25-22 |
Framework mapping describes architectural alignment and implementation intent. It does not represent an authorization, certification, agency ATO, FedRAMP authorization, or legal determination unless explicitly stated.
The Source-to-Decision architecture is designed to remain stable while the regulatory and control layer changes by industry.
Supports governance, risk identification, measurement, testing, evaluation, verification, validation, and lifecycle monitoring.
Provides an international AI-management-system framework covering governance, risk, transparency, accountability, and continual improvement.
Provides security and risk-management practices that can support deployment across regulated enterprises.
HIPAA has no general product certification. Accentus does not represent itself or FRIP as “HIPAA certified.”
No single architecture automatically satisfies all banking, securities, consumer-finance, or state requirements.
AI output is not legal advice and does not replace licensed counsel.
Every engagement moves from a defined decision to measured acceptance criteria — and keeps the components replaceable.
Define the authoritative sources, users, controls, risks, and decision.
Bounded pilot on representative data against agreed benchmarks.
Integrate into existing repositories, identity, cloud, and workflows.
Monitor, re-test, and swap components without redesign.
We are not building another OCR product, another document repository, or another AI chatbot. Accentus is building the evidence-governed intelligence layer between authoritative information and high-trust decisions.
Preserve the Source. Govern the Intelligence. Prove the Connection.
FRIP is a target/reference architecture under development. Capability descriptions represent intended design and implementation requirements unless specifically identified as deployed or demonstrated.
We will map the authoritative sources, controls, and acceptance criteria before recommending any technology.
Discuss a Use Case