AI Governance & Security Vendor Wiki

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ModelOp

Primary category: ai-governance-platform · Layer: governance

One-liner — An enterprise AI/ML governance and ModelOps platform that acts as a centralized “system of record” for every model, enforcing inventory, validation, approval, and monitoring controls — built around bank-grade model-risk management (SR 11-7).

What it does

ModelOp Center provides a central inventory and lifecycle-governance layer for all of an enterprise’s models — traditional ML, generative AI / LLMs, agentic AI, and third-party “vendor AI.” It automates the workflow from model intake through validation, approval, production monitoring, and retirement, attaching enforceable policies and controls at each gate. The pitch is “real-time visibility into AI risk, performance, health, and value” plus audit-ready documentation, so risk, compliance, and audit teams can see and challenge what the model builders are doing.

The company frames adoption around a “Minimum Viable Governance” / right-sizing approach: start with a governance inventory for visibility, add lightweight verification-and-approval controls, then standardize reporting — rather than imposing heavyweight process on every model at once.

Where it sits in the stack

This is a ai-governance-platform at the governance layer — the oversight/system-of-record tier that sits above the models and the runtime/data tooling, not in the request path.

  • Runtime-control role: none directly. ModelOp is an out-of-band governance and documentation control plane; it does not screen prompts, gate data access, or block exfiltration at runtime the way an inline guardrail or DLP tool does. Its value is process assurance, inventory, and evidence, not interception.
  • Trust zone: spans the governance/oversight plane across all zones — it inventories and tracks models regardless of where they run, and integrates with the MLOps/monitoring stack to pull signals back.

Deployment & architecture

  • ModelOp Center is the core governance software; an Enterprise AI Command Center provides the management/reporting interface, with automation and orchestration underneath.
  • Designed to integrate with an enterprise’s existing data-science, MLOps, CI/CD, and monitoring tooling via APIs rather than replace it — it orchestrates and records around the model stack.
  • Deployment is enterprise-oriented (SaaS and customer-hosted options); given its bank/FS customer base, self-hosted / private-cloud installation is common where data-residency and model-risk rules demand it. (Exact current deployment SKUs not fully confirmed from primary docs — see open questions.)

Positioning & differentiators

ModelOp’s distinguishing angle is depth on regulated model-risk management, not breadth of trust-and-safety features. It grew out of work with major global banks, and leans into SR 11-7 (Fed/OCC model-risk guidance) as a first-class framework alongside EU AI Act, NIST AI RMF, and ISO/IEC 42001. It was named a Visionary in the 2026 Gartner Magic Quadrant for AI Governance Platforms.

Versus neighbors:

  • ibm-watsonx-governance — closest large competitor on model-risk + inventory for regulated enterprises; ModelOp is the focused independent vs. IBM’s broader platform/stack play.
  • credo-ai and holistic-ai — more policy/responsible-AI and EU-AI-Act-compliance oriented; ModelOp is heavier on quantitative model lifecycle/ModelOps and SR 11-7 evidence.
  • monitaur — also targets regulated FS/insurance model governance and assurance; a direct overlap, with ModelOp positioned more on enterprise-scale operationalization.

Ownership, funding & M&A

  • Independent, VC-backed. Founded 2018, HQ Chicago, IL.
  • $10M Series B led by Baird Capital, announced 2024-08-13 (vendor press release — high confidence). Other investors named across sources include The Valley Fund / Valley Capital Partners and Jim McLean.
  • Total funding ~$16M across two rounds per Crunchbase/PitchBook aggregator snapshots (2026-06-28) — aggregator, medium/low confidence; not vendor-confirmed.
  • No M&A. No acquisition of or by ModelOp found; the seed carried no M&A flag and none was confirmed. ownership_confidence: medium (funding round well-sourced; total-funding figure is aggregator-based).

CTO / hedge-fund lens

  • Day-2. This is governance/oversight infrastructure you stand up once you have a real portfolio of models in production and a regulator or auditor asking how you control them — not a Day-1 control for a small shop.
  • SR 11-7 / model-risk relevance is the headline. For a hedge fund or asset manager, SR 11-7 (and OCC 2011-12) is the canonical model-risk-management discipline: documented development, independent validation, effective challenge, ongoing monitoring, and a model inventory. ModelOp is purpose-built to operationalize exactly that, and to extend the same discipline to GenAI/LLM and vendor-AI use. If your firm already runs an MRM function (banks, large asset managers), ModelOp is a natural fit; it also maps that evidence to EU AI Act and NIST AI RMF for multi-jurisdiction coverage.
  • When you need it: when model count, regulatory exposure, or audit demands outgrow spreadsheets and a model-inventory wiki — typically a larger, regulated shop. A 50-person systematic fund with a handful of models likely does not need a platform of this weight on Day-1; a multi-strategy manager with a formal MRM/validation team and examiner attention does.
  • Size of shop: enterprise / regulated mid-to-large. Named customers skew large-FS (Fidelity, FINRA) and Fortune 500.

Competitors / alternatives

ibm-watsonx-governance · credo-ai · holistic-ai · monitaur

Open questions / to verify

  • Confirm current deployment SKUs (true on-prem / air-gapped vs. SaaS vs. private-cloud) from primary docs.
  • Confirm total funding figure ($16M) and full cap table against a primary/filing source; only the $10M Series B is vendor-confirmed.
  • Confirm leadership (CEO/founders) names from a primary source.
  • Pricing model and typical deal size — unknown.

Sources

History

  • [2026-06-28] Stub created from seed registry.
  • [2026-06-28] Researched; established founded 2018, HQ Chicago, independent VC-backed ($10M Series B led by Baird Capital 2024-08-13, ~$16M total per aggregators), enterprise/regulated-FS target, deployment saas/self-hosted/api, SR 11-7 + EU AI Act + NIST AI RMF + ISO 42001 mapping, no M&A; set status researched, confidence medium, hedge_fund_fit medium.

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