Documents flow into Document Gateway. Abstract.DI extracts intelligence from every one. Sentry fingerprints and certifies them. The Document Warehouse stores all of it as structured, queryable data. The Warehouse improves Abstract.DI model accuracy. Better accuracy improves Sentry signals. Better signals make Document Gateway more valuable. More value drives more documents. After 18 months, switching costs are effectively permanent — and accuracy measurably exceeds any out-of-the-box alternative.
Competitors have slides about AI. AI.DI has 200+ React/TypeScript components, 29 live serverless edge functions, an ML Learning Studio with 30 self-improving engines, a live MCP server, an AI Agent Gateway connecting to Claude/Copilot/GPT-4/Gemini, and a production AI.DI Studio running 27 active AI engines. The gap between what competitors promise and what we have already shipped is measured in years of engineering. This is the unfair advantage that cannot be purchased with a VC round.
The HITL Reduction AI engine monitors all other engines' human review rates and autonomously moves classifications to auto-approve when confidence consistently exceeds configurable thresholds. Standard document types trend toward zero human intervention at 12 months. Novel or edge-case documents always retain human oversight — the goal is the right humans reviewing the right exceptions, not zero humans.
Every legacy DMS has fixed classification models requiring expensive, time-consuming retraining. AI.DI's ML Learning Studio inverts this entirely — 30 engines improving continuously from production data, automatically, without engineering intervention. AI.DI gets cheaper and more accurate at scale. Every competitor's cost stays flat or increases.
| Tier | Focus | Example Engines | HITL Trajectory |
|---|---|---|---|
| Tier 1 — Foundation | Document type classification | CRE Type Classifier, PE Type Classifier, Legal Type Classifier | Near-zero for covered types |
| Tier 2 — Entity | Named entity extraction | Party Extractor, Property Identifier, Fund/Entity Linker | 5–15% at 6 months |
| Tier 3 — Date & Validity | Temporal signal extraction | Expiration Detector, Effective Date Parser, Renewal Classifier | Near-zero for standard formats |
| Tier 4 — Financial | Financial data extraction | Loan Terms Extractor, Rent Roll Parser, Appraisal Value Extractor | 10–20% at 6 months |
| Tier 5 — Compliance | Compliance validation | Coverage Gap Detector, Compliance Flag Engine, Signature Validator | 15–25% — domain expertise retained |
| Tier 6 — Cross-Document | Cross-document consistency | Portfolio Benchmark Engine, Anomaly Correlator, Reconciliation Engine | Complex analysis — strategic HITL |
Box cannot rebuild their data model for AI without breaking 150,000 customers. SharePoint's incentive is to preserve Teams and Office revenue, not cannibalize Copilot. M-Files is 2–3 years behind on the data model and the Warehouse layer. Egnyte wins on storage reliability but has no awareness of what documents contain. Every dollar these platforms invest in AI is constrained by the need to not break existing products. That constraint does not exist for AI.DI.
| Capability | AI.DI Platform | Box | SharePoint | M-Files | Egnyte |
|---|---|---|---|---|---|
| Architecture & Philosophy | |||||
| AI-native architecture (built for AI, not adapted) | Win 2024-2025. Zero compromise. AI is core, not a wrapper. | Bolt-on | Copilot wrapper | Aino — improving but bolt-on | Minimal |
| Zero legacy technical debt | Win No codebase older than 18 months. | 2005 origin | 2001 origin | 2003 origin | 2009 origin |
| Edge compute architecture | Win All compute at edge. Scale to zero or infinity. | None | Azure Functions (partial) | None | None |
| Modular adoption (standalone or full suite) | Win Every engine has standalone value. | Partial | Module-based but complex | Partial | Partial |
| AI & Document Intelligence | |||||
| Structured data extraction from documents | Win Abstract.DI — any type, 94% day-one, 100K batch. | None | Basic Copilot extraction | Aino — requires training | None |
| Day-one extraction accuracy (no training) | Win 94%+ on pre-built schemas. No training required. | N/A | N/A | Months of training | N/A |
| GPU-accelerated OCR pipeline | Win DocTR — 10-50x speedup on GPU. | None | Azure OCR (limited) | Basic OCR | Basic OCR |
| Batch processing (100K+ archives) | Win 100K-chunk batch. ZIP, Box, SharePoint, S3. | None | None | Limited batch | None |
| 30 self-improving ML engines | Win Continuous production learning. No ML engineers. | None | Generic Copilot | Limited self-learning | None |
| HITL Reduction AI (autonomous meta-engine) | Win Autonomous promotion of high-confidence classifications. | None | None | None | None |
| Trust, Compliance & Security | |||||
| Document fingerprinting (deterministic, patent-pending) | Win ~10,000 fingerprint catalog. Zero doc storage. | None | None | None | None |
| Zero document storage compliance model | Win Only fingerprints stored. GDPR minimization by math. | Full storage | Full storage | Full storage | Full storage |
| PII auto-detection and redaction pipeline | Win Tokenization pipeline auto-redacts at ingestion. | None | Purview (partial) | None | DLP (partial) |
| Fraud / document manipulation detection | Win Deterministic — single character change detectable. | None | None | None | None |
| Blockchain audit trail | Win On-chain anchoring. 2,814+ documents on chain. | None | None | None | None |
| Data & AI Infrastructure | |||||
| Structured document intelligence warehouse | Win Every extracted field is a queryable row. Unique. | None | None | None | None |
| Snowflake Data Share (zero-ETL) | Win Zero-copy. Join doc intelligence with financial data. | None | None | None | None |
| Live MCP server for AI agents | Win Production MCP. Claude, Cursor, LangChain — no wrapper. | None | None | None | None |
| Vector embeddings on certified chunks | Win Tied to certified versions. pg_vector native. | None | Azure AI Search (partial) | None | None |
| CTR Score (Continuous Transaction Readiness) | Win Live composite readiness score. Portfolio-wide. | None | None | None | None |
| 27 active AI engines in production | Win AI.DI Studio — live engine map with real-time status. | None | None | None | None |
| Deployment & Integration | |||||
| Unlimited hierarchy depth (any org structure) | Win Enterprise → Group → Entity → Asset → Unit. Any depth. | Folders only | Sites/subsites | Metadata-based | Folders/workspaces |
| 30-day deployment (no implementation project) | Win 30 days from contract to live. M-Files runs 3–6 months. | Weeks–months | Months–years | 3–6 months typical | Weeks–months |
| Installed base / existing trust relationships | Win 45 FileStar enterprise clients. 20+ year relationships. Zero-CAC. | Large (hard to access) | Large (bundled) | Existing clients | Existing clients |
Industry average: 40%+ of files are duplicates. A corpus with 40% duplicates means 40% of every LLM bill computes the same content twice. Sentry identifies all duplicates, consolidates to canonical records, preserves all metadata from every duplicate instance, then suppresses duplicates from AI queues. LLM compute costs drop 30–50% immediately — without changing a single prompt or model.
Structured lifecycle governance for any document type. Every document has a defined lifecycle: creation → review → approval → distribution → monitoring → archival. Configurable routing rules, approval chains, and escalation paths enforce this lifecycle.
Version control with full history. Compliance monitoring with expiry tracking. Audit trail on every action. Role-based access aligned with hierarchy.
FileStar governs documents. AI.DI makes them intelligent. FileStar-managed documents automatically flow through Sentry certification and Abstract.DI extraction without any workflow change for existing users. All FileStar metadata syncs to the AI.DI Warehouse.
Every FileStar client is one conversation away from the full AI.DI platform. No rip-and-replace. No migration project. No change management crisis.
Every FileStar client that upgrades to AI.DI is a client that could not have been won by a cold-start competitor — regardless of product quality, VC funding, or pricing. A startup raising $20M today cannot replicate a 20-year enterprise trust relationship with a client's CFO. This is structural moat, not temporary advantage.
AI.DI gives your organization Continuous Transaction Readiness — the state where every document is accessible, authentic, aligned, and actionable at all times. Enterprises that achieve this state lower their cost of capital, reduce audit risk, deploy AI with confidence, and close transactions faster.
| Score | Status | Typical Situation | Time to Transact |
|---|---|---|---|
| 90–100 | Transaction Ready | All documents present, current, valid. No violations. | 48 hours |
| 75–89 | Near Ready | 1–3 documents missing or expiring. No active violations. | 1–5 business days |
| 55–74 | Attention Required | Multiple gaps or 1–2 violations. | 2–4 weeks |
| 35–54 | Not Ready | Significant document gaps. Will not survive buyer diligence. | 30–60 days |
| 0–34 | Critical | Severely incomplete or non-compliant documentation. | 90+ days |
AI.DI is not a document management UI with an API bolted on. It is a document intelligence data platform: a PostgreSQL warehouse of structured document intelligence, a live MCP server, a webhook event stream, a REST/GraphQL API, Snowflake Data Share, JDBC/ODBC direct access, vector embeddings on certified document chunks, and a 30-engine ML pipeline that improves continuously. Every document becomes structured, provenance-tracked, certified data — available to any model, pipeline, or analytics tool you're running.
| Table | Contents | Key Fields | Primary Use |
|---|---|---|---|
document_records | Every document processed | id, original_name, document_type, workflow_status, asset_id, classification_confidence, storage_path | Document inventory, classification analysis |
extracted_fields | Structured extraction from Abstract.DI | document_id, field_name, field_value, confidence_score, extraction_model, extraction_timestamp | Contract analytics, financial extraction |
sentry_fingerprints | Cryptographic fingerprint records | document_id, fingerprint_hash, fingerprint_type, certified_at, version_chain, similarity_scores | Certification, duplicate detection, fraud monitoring |
hierarchy_nodes | Full org hierarchy | id, parent_id, node_type, node_name, industry, ctr_score, completeness_pct | Portfolio analytics, CTR aggregation |
document_activity_log | Every action on every document | document_id, event_type, actor_id, actor_role, timestamp, metadata | Audit trail, access pattern analysis |
vector_embeddings | Embeddings on certified chunks | document_id, chunk_id, certified_version_hash, embedding_vector, model_version | Semantic search, RAG retrieval, clustering |
ctr_score_history | CTR Score time series | node_id, score, dimension_scores, calculated_at, delta_from_prior | Readiness trending, portfolio benchmarking |
| Department | Acute Pain | AI.DI Entry Product | Expansion Path |
|---|---|---|---|
| Legal / GC | Contract version disputes, discovery liability, GDPR compliance | Sentry certification + Document Gateway distribution | Full Document Warehouse for corporate legal corpus |
| Finance / Accounting | Audit prep fire drills, financial document reconciliation | Abstract.DI batch (financial extraction) + Blueprint audit | Sentry certification + Warehouse integration to ERP |
| Compliance / Risk | Regulatory filing tracking, compliance gaps, audit exposure | Sentry + Warehouse (compliance corpus) + CTR Score | Full platform across regulated document types |
| Transactions / Deal Team | Due diligence prep time, data room chaos | Document Gateway + Distribution Studio + Transaction Rooms | Abstract.DI batch for portfolio-wide extraction |
| IT / Data Engineering | Unstructured data not in Snowflake; LLM hallucinations | Document Warehouse + Snowflake + MCP Server | Full platform as enterprise document intelligence backbone |
| Operations / HR | Employee records, policy tracking, onboarding compliance | FileStar lifecycle governance + Abstract.DI HR extraction | Sentry certification + Document Gateway policy distribution |
The world's largest institutional real estate portfolios run on the same platform as a 12-asset regional operator starting their first compliance program. A single compliance officer in one department gets the same AI intelligence, the same CTR Score, the same Warehouse, the same MCP server as a 500-person investment management firm running 20 funds. We built for scale from day one — which means the smallest client gets the most powerful platform available at any price point. No feature tiers. No locked capabilities. No "upgrade to get the real thing."
Blueprint evaluates your entire document ecosystem — every repository, every system, every process — and delivers a scored readiness assessment and a prioritized AI.DI product roadmap. Blueprint invariably reveals exactly which products the client needs and why. The roadmap we deliver IS the AI.DI implementation plan for your organization.
You get the full platform from the moment you deploy — every engine, every view, every integration. There are no feature gates, no capability tiers, and no "enterprise unlock" for core functionality. Your first document gets the same AI pipeline as document number one million. We believe you should see the full value immediately, not earn access to it through a ramp-up process.
No. AI.DI layers over your existing infrastructure. Start with your highest-priority asset group or begin fresh with new documents. There is no requirement to migrate your entire historical archive before going live. The batch engine can process any legacy archive on its own timeline — you decide when and what to bring in.
Sentry generates a mathematical fingerprint — a unique hash derived from document content. Two identical documents always produce identical fingerprints. Any change produces a different fingerprint. The original document is never stored by Sentry. GDPR data minimization is achieved structurally — your documents never leave your control.
The live MCP server exposes 6 tools: search_documents, get_compliance_status, get_obligations, query_warehouse, get_hierarchy, get_document_url. Add AI.DI to Claude, Cursor, LangChain, AutoGen, or any MCP-compatible environment and your agents immediately have certified document search and structured extraction queries. Authentication via OAuth2 — agents only access what the connecting user is authorized to see. Keys are revocable instantly.
Yes. Full platform via Docker containers — no Kubernetes required. Azure Cloud, AWS, fully on-premise, and hybrid (metadata in cloud, documents on-prem) are all supported. Air-gapped environments with no internet connectivity are also supported. Contact the enterprise team for deployment architecture details.
Snowflake Data Share (zero-copy, no ETL), Databricks connector (Delta Lake, streaming), Tableau and Power BI native connectors, dbt compatibility, BigQuery export, direct JDBC/ODBC access, REST API with OpenAPI 3.0 spec, Python SDK, and webhook event streaming to any HTTP endpoint. SSO via SAML 2.0 and OAuth 2.0.