All analysis was generated autonomously, without human review. Scores are analytical opinions drawn from the cited public sources, without hands-on testing. They are not audits, certifications, investment reports, purchasing advice, or evaluations of quality.
ModelOp sells enterprise software that inventories the AI systems a large company builds or buys, routes each one through risk tiering and approval, and keeps the audit trail regulators ask for. An analyst record documents a 2016 founding under the former name Open Data Group. Its named references are large regulated enterprises, chiefly financial institutions. A 2026 partnership extends the same approval decision into live traffic, letting ModelOp open or close an AI gateway route for a tool it has cleared. The customer names and the outcome claims reach the public record through ModelOp and its lead investor, so a buyer has little independent material to check them against.
| Description | Software that gives a large organization one inventory of the AI it builds and buys, routes each use case through risk tiering and approval, and records the evidence auditors ask for. | [f1] |
|---|---|---|
| Founded | 2016 | [f2] |
| HQ | Chicago, Illinois, United States | [f2] |
| Funding | $16.68M total | [f2] |
| Latest funding | Series B, 10 million dollars, announced August 2024 | [f3] |
| Product | What it does |
|---|---|
| Enterprise AI Command Center | System of record for every model, agent, and vendor AI system an enterprise runs, with automated lifecycle management and reporting on cost, risk, and value. |
| AI Delivery Engine (MADE) | Automation layer that connects ModelOp to the surrounding enterprise AI stack and runs the workflows carrying a use case from intake to production. |
AI Defense Matrix
| Govern | Identify | Protect | Detect | Respond | Recover | |
|---|---|---|---|---|---|---|
| AI-Workload Platforms Inference servers, training platforms, vector DB platforms, and the model-loading supply chain. | ||||||
| AI Orchestration Tools Agentic orchestration tools, plus their plugins, skills, hooks, system prompts, scaffolding, harnesses, configuration settings, and MCP clients on user devices. | ||||||
| AI-Generated Code Code produced by AI tools, AI-assisted reviews, AI-generated infrastructure-as-code and tests, and vibe-coded apps that bypass CI/CD. | ||||||
| AI Gateways & Routers MCP proxies and gateways, LLM routers, outbound AI-service traffic, shadow AI egress, and model-registry traffic. | ||||||
| AI Model Model weights, fine-tuning checkpoints, model cards, registries, AIBOM, and the third-party LLMs your enterprise consumes. | ||||||
| Training Data Datasets used for training, fine-tuning, and continued learning. | ||||||
| Runtime AI Data User prompts, inference inputs, RAG content, vector DB content, persistent agent memory, and interaction history. | ||||||
| AI Agent Identities AI agents as non-human principals, plus credentials, keys, permission scopes, service accounts, and delegation chains across agents and tools. |
The Enterprise AI Command Center maps controls to the EU AI Act, NIST AI RMF, OCC SR 11-7 and ISO 42001 and enforces them, keeps a searchable registry of every AI solution an enterprise runs, and runs automated bias, drift and performance tests. Mapped to the AI Defense Matrix. [f4]
How well the company can compete in its security market, scored across eight dimensions against public evidence.
| Dimension | Score | Rationale |
|---|---|---|
| Problem Clarity How precisely the company defines its problem, with evidence the problem exists at the scale claimed. | 3/5 | The buyer and the obligation are specific, with US model-risk guidance and the EU AI Act named as the rules the product works against, but every figure sizing the pain in the reviewed record is ModelOp's own. [s8, s9, s1] |
| Capability Depth How specific the technical capabilities are, with evidence beyond marketing claims such as docs and third-party validation. | 3/5 | The vendor pages document the lifecycle in concrete steps, from use-case intake and automatic risk tiering to bias and drift testing and gateway enforcement, and the reviewed sources carry no third-party technical evaluation of any of it. [s4, s8, s7] |
| Market Timing Whether the market is ready for this product, with evidence that buyers are actively seeking solutions. | 3/5 | Gartner published a Magic Quadrant for AI Governance Platforms in June 2026 and ModelOp appears in it, but that signal reaches the reviewed record through ModelOp's own pages, and the other recent buyer-side signals here are vendor-carried customer references. [s6, s1, s11] |
| Team Credibility Demonstrated domain expertise with public signals such as prior exits, publications, and industry recognition. | 2/5 | The leadership page documents senior enterprise analytics roles for the chief executive and chief technology officer, both previously at Teradata's Think Big unit, and ModelOp published every one of those biographies, so the career evidence in the reviewed record is one-sided. [s3, s10] |
| GTM Proof Evidence of actual traction (customers, revenue signals, partnerships) beyond stated intentions. | 4/5 | Fidelity, RBC Capital Markets and Prudential appear as named references with an attributed customer quote, and the investor that led its Series B describes Fidelity Investments, FINRA and Bristol Myers Squibb as users, while no reviewed source reports revenue or customer counts. [s9, s2, s12, s11] |
| Funding Efficiency Whether funding matches go-to-market ambition, with signs of capital-efficient growth. | 3/5 | An analyst record puts total funding at 16.68 million dollars against the founding year that same record documents and visible shipping, which is proportional to the motion, and no revenue, margin or growth-efficiency figure appears in the reviewed record. [s11, s12] |
| Category Clarity Whether the company creates or fits a recognizable category that buyers can quickly place in their stack. | 4/5 | A CB Insights page records ModelOp as an Outperformer in an algorithmic auditing and risk management market, and ModelOp's own pages report its inclusion in Gartner's 2026 AI Governance Platforms Magic Quadrant, so two analyst houses place it in a recognised market under different category labels. [s11, s6] |
| Incumbent Defensibility How vulnerable the core value proposition is to absorption as a feature by a platform vendor. | 3/5 | Running above MLOps, GRC and IT service systems gives ModelOp workflow integration and an accumulating governance record inside each customer, and the vendors of those underlying systems could add the same coordination layer. [s8, s1, s5] |
Large regulated enterprises run AI they cannot fully list, and ModelOp sells the record that closes that gap. The platform registers models, agents and vendor AI systems, assigns each use case a risk tier, and captures the evidence an auditor would ask for as the work happens.
The obligation behind that purchase predates generative AI. ModelOp's financial services page describes SR 11-7, the Federal Reserve and OCC model-risk guidance, as the rule US firms work against, and it describes the model risk management teams banks already staff. Its product page documents the same controls against the EU AI Act, the NIST AI Risk Management Framework and ISO 42001.
Every figure sizing that pain in the reviewed record is ModelOp's own. Its pages carry claims of ten times faster time to value and full policy adherence, and the reviewed sources contain no independent measurement of either, so a buyer testing the business case has to run its own numbers. [s1, s8, s9]
ModelOp Center runs the AI lifecycle as a workflow rather than a document review. Use-case intake, automatic risk tiering, control mapping, approval routing, testing and production monitoring run in one system, and the platform produces model cards and validation summaries as a by-product of that work.
Testing covers bias, drift, performance and cost. The command centre page documents continuous monitoring of every AI system on those measures plus token usage and tool invocations, which gives chief information and chief financial officers portfolio-level visibility into AI spend and return.
Enforcement reaches past approval into live traffic. A July 2026 partnership post describes ModelOp evaluating a tool against policy and instructing Kong's gateway to create or withhold an API route, so an unapproved tool receives no connection. The reviewed record documents the integration and does not measure its use in production. [s4, s8, s7]
ModelOp positions itself above the systems it connects to rather than against them. The command centre page describes the product sitting above MLOps, AI execution, security, GRC, IT service management and data infrastructure, and the homepage states that it operates above those systems and does not replace them.
ModelOp's own pages carry one of its two analyst placements and a third-party database carries the other. ModelOp's own pages report a Visionary placement in the 2026 Gartner Magic Quadrant for AI Governance Platforms and cite the report by title, authors and date. A CB Insights page records ModelOp as an Outperformer among 15 other companies in an algorithmic auditing and risk management market that also includes Microsoft, Amazon and Credo AI.
Neither placement carries information about revenue, deployment scale or how well the product works, so a buyer cannot read either as evidence that the product performs. The Gartner disclaimer reproduced on ModelOp's own page states that Gartner does not endorse vendors and that its research publications are opinions rather than statements of fact. [s1, s8, s6, s11]
ModelOp's references are named enterprises in regulated industries. Its financial services page attaches a quote to Paul Howard, Head of Compliance Analytics Architecture at Fidelity, and describes RBC Capital Markets using ModelOp Center for bond trading. Its About page points to a fireside chat on how Prudential builds a responsible AI framework powered by ModelOp.
A third party repeats some of those names. Baird Capital, announcing the Series B round it led in August 2024, describes Fidelity Investments, FINRA and Bristol Myers Squibb using ModelOp for real-time visibility into AI risk, performance, health and value. Baird led that round, so its account is not disinterested.
No source in the reviewed record reports revenue, customer counts or renewal rates. A CB Insights page records a 2026 conference presentation scheduled with Manulife and lists a ModelOp case study video covering RBC and QBE, and neither of those sizes a deployment, so a procurement team has no independent measure of how widely the product runs. [s9, s2, s12, s11]
ModelOp's leaders come from enterprise analytics delivery rather than from security research. The leadership page describes chief executive Dave Trier as previously Vice President of Advanced Analytics Services at Think Big Analytics, acquired by Teradata, where he led a 400-person organisation across the Americas. It describes chief technology officer Jim Olsen as previously Director of Software Development at the same Teradata company, responsible for its analytics operations framework.
Leadership has turned over since the rebrand. A 2019 ADTmag report describes Pete Foley as ModelOp's chief executive, and the current leadership page describes Dave Trier in that role alongside Jennifer Grunebaum as chief financial officer.
ModelOp publishes every biography of its current leadership in the reviewed record. The reviewed sources carry no independent confirmation of those prior roles or exits, so a buyer weighing the team is reading the company's own account of it. [s3, s10]
No ModelOp-run trust portal or attestation badge appears on the probed surfaces. One security attestation appears in the reviewed record, in ModelOp's entry in CHAI's assurance resource provider directory, which states SOC 2 Type I certification and work toward Type II. That same entry describes ModelOp as a CHAI-Certified Assurance Resource, a designation in a healthcare AI assurance programme. The reviewed record does not describe what that designation certifies about ModelOp's own controls. A buyer that requires an inspectable audit report has nothing in this record to inspect.
The architecture keeps governance data inside the customer's own systems. The integrations page describes ModelOp installing on-premises or in private, public or hybrid cloud, referencing data in place without copying or transferring it, and connecting to enterprise identity providers over OAuth2 and SAML. The same CHAI entry states that ModelOp is not a public SaaS product, citing the security and policy needs of enterprises and healthcare organisations. [s13, s5, s14]
| Company | Relationship | Note | Compare |
|---|---|---|---|
| Credo AI | competes with | A CB Insights page records both companies in the same algorithmic auditing and risk management market. | N/AWe captured the evidence for these companies under different evidence-model versions (v1 vs v2), so the totals were scored under different conditions and are not directly comparable. |
| OneTrust | adjacent | ModelOp's own FAQ describes it as a GRC platform ModelOp integrates with, and describes GRC platforms as governing business processes rather than AI. | N/AWe scored these companies at different scopes, so the totals measure different things. |
| Holistic AI | competes with | Competes for the same enterprise AI-governance buyer. | N/AWe captured the evidence for these companies under different evidence-model versions (v1 vs v2), so the totals were scored under different conditions and are not directly comparable. |
| Fiddler | adjacent | A CB Insights page records it among ModelOp's competitors, on the model monitoring side of the same lifecycle. |
Add analyzed competitors to compare them side by side with ModelOp.
A closer look at the company's product strategy, measuring how defensible it is against market forces and examining the eight areas behind it.
reinforce or reposition
ModelOp is bought by regulated enterprises whose procurement and legal teams sit between a decision and a swap. Its platform accumulates the AI inventory, risk tiers, approvals and audit evidence those buyers run their governance on. That accumulation stays inside the customer's own systems, because ModelOp installs on-premises or in the customer's cloud and references data in place rather than copying it. An analyst record documents 12 patents filed, and the one patent record it carries is granted, dated May 2025, covering the analytic model execution engine. No curated corpus or cross-customer data asset appears in the reviewed record. The workflow depth inside those accounts is a head start rather than a durable lead.
| Dimension | Score | Rationale |
|---|---|---|
| Value Delivery Does the product sell software as the product, or judgment, trust, or accountability with software as the delivery mechanism. | 1/3 | The customer's own team installs, configures and operates the platform and owns the outcomes, and ModelOp is licensed as an annual subscription with software support included. |
| Switching Cost How expensive leaving is for a customer: data portability, integrations, learned workflows, network effects, regulatory data residency. | 2/3 | The inventory, risk tiers, approvals and audit evidence build up over years of use and other systems consume the approval record at runtime, and the reviewed record does not size what leaving would cost. |
| Compliance Moat Whether certifications, liability acceptance, or audit trails block an easy replacement. | 1/3 | The security attestation in the reviewed record is a SOC 2 Type I recorded in ModelOp's CHAI directory entry, which an enterprise-market competitor can obtain through ordinary preparation, and the CHAI-Certified Assurance Resource designation in that same entry is one a competitor can also seek, so neither blocks replacement. |
| Problem Complexity Whether the product requires ML, optimization, real-time systems, or years of specialized expertise. | 2/3 | The engineering is broad integration work plus automated bias, drift and performance testing across many enterprise stacks, and the reviewed record documents no specialised research or real-time systems work behind it. |
| Buyer Profile Whether buyers are SMB operators, mid-market IT teams, or regulated enterprises and governments with procurement gates. | 3/3 | The evidenced buyers are regulated enterprises and a regulator, with Fidelity, RBC Capital Markets and Prudential named on ModelOp's pages and Fidelity Investments, FINRA and Bristol Myers Squibb named by its investor. |
| Layer Whether the product is an end-user application, a platform with application features, or infrastructure other applications depend on. | 2/3 | The product runs above MLOps, GRC, IT service management and data infrastructure and connects to them, and the reviewed record documents no application that depends on it to run. |
| Proprietary Data, Content, or IP Whether the product accumulates datasets, content licenses, or IP that a rival cannot recreate from scratch. | 2/3 | An analyst record documents 12 patents filed and carries one granted patent, dated May 2025, covering the analytic model execution engine, which is retained IP, while the architecture references customer data in place so no cross-customer corpus accumulates to ModelOp. |
ModelOp sells to the function inside a large regulated enterprise that has to answer for AI to a board or a supervisor. Its financial services page describes banks, investment firms, insurance companies and regulatory bodies as the buyers, and it describes the model risk management teams those firms already staff. The named references are in the same band, with Fidelity, RBC Capital Markets and Prudential on ModelOp's own pages, and Fidelity Investments, FINRA and Bristol Myers Squibb in the investor's announcement of the last funding round.
Healthcare is the second declared segment. ModelOp's entry in CHAI's assurance resource provider directory describes healthcare organisations, insurers, pharmaceutical companies, banks, regulatory bodies and global consumer goods companies as the institutions using the product.
Security appears among the reviewers rather than as the named buyer. The command centre page describes approval workflows orchestrating security, privacy, data, IT, architecture, legal, risk and compliance in one traceable workflow, and the reviewed record documents no security budget as the source of the purchase.
The product turns a governance process into a running workflow. Use-case intake is standardised, a risk tier is generated automatically, controls are mapped to that tier, approvals route to the teams that own them, and monitoring continues after production, all inside one system of record.
Discovery and evidence generation are documented concretely. The command centre page describes continuous discovery of unregistered AI including custom GPTs and agents, and automatic capture of model cards, validation summaries and evidence across the lifecycle. Testing covers bias, drift, performance, data stability and prompt vulnerabilities, and consumption reporting covers token usage, tool invocations and throughput.
Runtime enforcement is a partnership rather than an in-house control plane. A July 2026 post describes ModelOp evaluating a tool against corporate policy and instructing Kong's gateway to create or withhold an API route, so approval state decides whether traffic flows. The reviewed record documents the design and carries no measurement of it running in customer production, so a buyer evaluating runtime enforcement is weighing a documented design rather than a proven one.
Sales run through named enterprise references and a demo request rather than self-service. The reviewed pages route the buyer to a demo request, no self-service sign-up or trial appears in the reviewed record, and the customer material names Fidelity, RBC Capital Markets and Prudential, with a quote attributed to Paul Howard, Head of Compliance Analytics Architecture at Fidelity.
One documented partnership carries a technology route to market. A July 2026 post describes a partnership with Kong in which ModelOp's approval state drives Kong's AI gateway, which puts ModelOp in front of enterprises already running one.
Independent confirmation of scale is what the record lacks. A CB Insights page records a 2026 conference presentation scheduled with Manulife and lists a ModelOp case study video covering RBC and QBE, and no reviewed source reports revenue, customer counts or retention, so a buyer cannot size the installed base.
ModelOp prices by the size of the governed estate. Its CHAI directory entry describes an annual subscription licence with software support included, priced on consumption of models under management.
That structure ties the licence to the size of the customer's AI portfolio rather than to seats, which aligns the vendor with the discovery capability the product leads on. No rate, tier, minimum or true-down term appears in the reviewed record, so a buyer cannot size a deal without contacting sales and cannot tell what retiring AI systems would save.
ModelOp is deployed software rather than a hosted service. Its CHAI directory entry states that the product is not a public SaaS solution, citing the security and policy needs of enterprises and healthcare organisations, and the integrations page describes installation on-premises or in a private, public or hybrid cloud.
The design keeps customer data where it already is. The integrations page describes ModelOp referencing data in place without copying, extracting or transferring it, and connecting to enterprise identity providers over OAuth2 and SAML. It also documents versioned releases and release notes for ModelOp Center, and the reviewed record does not describe how customers move between versions, so a buyer cannot judge how quickly a new control reaches an installed deployment.
One security attestation appears in the reviewed record. ModelOp's entry in CHAI's assurance resource provider directory states SOC 2 Type I certification and work toward Type II, and that same entry describes ModelOp as a CHAI-Certified Assurance Resource in a healthcare AI assurance programme. No ModelOp-run trust portal or attestation badge appears on the probed surfaces.
Compliance coverage and compliance assurance are different things here. The command centre page describes controls mapped to the EU AI Act, the NIST AI Risk Management Framework, OCC SR 11-7 and ISO 42001, which describes what the product helps a customer prove rather than what an auditor has certified about ModelOp itself.
A buyer that requires an inspectable report has nothing in this record to inspect, which matters for a vendor whose product is evidence for auditors.
ModelOp defines itself by what it sits above. The command centre page describes the product integrating with and sitting above the existing AI stack, unifying MLOps, AI execution, security, GRC, IT service management and data infrastructure into one operating layer, and the homepage states that it operates above those systems and does not replace them.
The integration catalogue is broad and its per-customer depth is not stated. The integrations page describes native connections to AWS, Azure, Google Cloud, OpenAI, MLflow, CI/CD tools, ServiceNow, Jira, Databricks, Snowflake, observability tools, Power BI, Tableau and GRC systems, and the homepage puts the count at more than 50 technologies. A catalogue of that size describes what a customer may connect, and the reviewed record does not state what any customer has connected, so integration count is not evidence of deployed depth.
The same architecture names the systems ModelOp depends on. The integrations page describes ServiceNow, Jira, Databricks, Snowflake and the major cloud platforms as the systems it connects to, and the reviewed sources do not establish what any of those vendors offers in AI governance. A buyer already standardised on one of them holds that relationship before ModelOp is evaluated.
The leadership bench comes from enterprise analytics delivery. The leadership page describes chief executive Dave Trier as previously Vice President of Advanced Analytics Services at Think Big Analytics, acquired by Teradata, where he led a 400-person organisation across the Americas, and chief technology officer Jim Olsen as previously Director of Software Development at that same Teradata company. Chief financial officer Jennifer Grunebaum is described as having served as chief financial officer at several venture and private-equity-backed technology companies.
The chief executive role has changed hands since the rebrand. A 2019 ADTmag report describes Pete Foley as ModelOp's chief executive, and the current leadership page describes Dave Trier in that role.
ModelOp publishes every biography of its current leadership. The reviewed sources carry no independent confirmation of the prior roles or exits, so a buyer weighing the team is reading the company's own account of it.
| Id | Source | Tier | Accessed |
|---|---|---|---|
| f1 | ModelOp: homepage | official | 2026-08-31 |
| f2 | CB Insights: ModelOp company profile | research | 2026-08-31 |
| f3 | Baird Capital: announcement of its investment in ModelOp | press | 2026-08-31 |
| f4 | ModelOp: Enterprise AI Command Center product page | official | 2026-08-31 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | ModelOp: homepage “ModelOp's Enterprise AI Command Center is the system of record that unifies every AI asset, automates the delivery lifecycle, and maximizes ROI — so you bring ML, GenAI, and agentic AI to production 10× faster.” | official | 2026-08-31 |
| s2 | ModelOp: About page “In 2018, ModelOp was founded to address this gap, creating a team with strong competencies in data science, software engineering, business process, risk management and compliance.” | official | 2026-08-31 |
| s3 | ModelOp: leadership page “Dave Trier is Chief Executive Officer at ModelOp” | official | 2026-08-31 |
| s4 | ModelOp: ModelOp Center AI governance software page “ModelOp provides self-service governance with role-based workflows that automate every step of the end-to-end AI lifecycle by integrating with and orchestrating your enterprise's many processes and systems related to AI innovation and governance so you can bring AI to market faster.” | official | 2026-08-31 |
| s5 | ModelOp: interoperability and integrations page “ModelOp installs directly into your environment—on-premises, private cloud, public cloud, or hybrid—so governance lives where your data and systems already operate.” | official | 2026-08-31 |
| s6 | ModelOp: Gartner Magic Quadrant landing page “Gartner, Magic Quadrant for AI Governance Platforms, Lauren Kornutick, Sumit Agarwal, Priya Sundararaman, Nader Henein, Brandon Medford, June 2026.” | official | 2026-08-31 |
| s7 | ModelOp: blog post announcing the Kong partnership “ModelOp and Kong Partner to Deliver Zero-Trust Enforcement for the Agentic Enterprise” | official | 2026-08-31 |
| s8 | ModelOp: Enterprise AI Command Center product page “The Enterprise AI Command Center integrates with and sits above your existing AI stack — unifying MLOps, AI execution, security, GRC, ITSM, and data infrastructure into a single operating layer.” | official | 2026-08-31 |
| s9 | ModelOp: financial services solutions page “ModelOp's AI Governance software helps leading financial institutions, banks, investment firms, insurance companies, and regulatory bodies get visibility in their AI initiatives across large enterprises, mitigate risk, comply with regulations, and deliver value-generating models at scale” | official | 2026-08-31 |
| s10 | ADTmag: news report on the Open Data Group rebrand, with a CEO interview “The Open Data Group (ODG) this week announced it's re-launching as ModelOp, reflecting the importance of modeling to artificial intelligence (AI) and machine learning (ML) implmentations.” | press | 2026-08-31 |
| s11 | CB Insights: ModelOp company profile “ModelOp was formerly known as Open Data Group. It was founded in 2016 and is based in Chicago, Illinois.” | research | 2026-08-31 |
| s12 | Baird Capital: announcement of its investment in ModelOp “Fortune 500 companies and innovative enterprises — including Fidelity Investments, FINRA, and Bristol Myers Squibb — use ModelOp as a single source of truth that provides real-time visibility into AI risk, performance, health, and value.” | press | 2026-08-31 |
| s13 | CHAI assurance resource provider directory: the ModelOp entry “ModelOp is not a public SaaS solution due to the security and policy needs of enterprises and healthcare organizations.” | official | 2026-08-31 |
| s14 | Trust-surface probe 2026-08-31: no trust portal found. trust. and security. subdomains and a control name did not resolve, /security /trust /compliance 404 | official | 2026-08-31 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | ModelOp: homepage “ModelOp's Enterprise AI Command Center is the system of record that unifies every AI asset, automates the delivery lifecycle, and maximizes ROI — so you bring ML, GenAI, and agentic AI to production 10× faster.” | official | 2026-08-31 |
| s2 | ModelOp: About page “In 2018, ModelOp was founded to address this gap, creating a team with strong competencies in data science, software engineering, business process, risk management and compliance.” | official | 2026-08-31 |
| s3 | ModelOp: leadership page “Dave Trier is Chief Executive Officer at ModelOp” | official | 2026-08-31 |
| s4 | ModelOp: ModelOp Center AI governance software page “ModelOp provides self-service governance with role-based workflows that automate every step of the end-to-end AI lifecycle by integrating with and orchestrating your enterprise's many processes and systems related to AI innovation and governance so you can bring AI to market faster.” | official | 2026-08-31 |
| s5 | ModelOp: interoperability and integrations page “ModelOp installs directly into your environment—on-premises, private cloud, public cloud, or hybrid—so governance lives where your data and systems already operate.” | official | 2026-08-31 |
| s6 | ModelOp: Gartner Magic Quadrant landing page “Gartner, Magic Quadrant for AI Governance Platforms, Lauren Kornutick, Sumit Agarwal, Priya Sundararaman, Nader Henein, Brandon Medford, June 2026.” | official | 2026-08-31 |
| s7 | ModelOp: blog post announcing the Kong partnership “ModelOp and Kong Partner to Deliver Zero-Trust Enforcement for the Agentic Enterprise” | official | 2026-08-31 |
| s8 | ModelOp: Enterprise AI Command Center product page “The Enterprise AI Command Center integrates with and sits above your existing AI stack — unifying MLOps, AI execution, security, GRC, ITSM, and data infrastructure into a single operating layer.” | official | 2026-08-31 |
| s9 | ModelOp: financial services solutions page “ModelOp's AI Governance software helps leading financial institutions, banks, investment firms, insurance companies, and regulatory bodies get visibility in their AI initiatives across large enterprises, mitigate risk, comply with regulations, and deliver value-generating models at scale” | official | 2026-08-31 |
| s10 | ADTmag: news report on the Open Data Group rebrand, with a CEO interview “The Open Data Group (ODG) this week announced it's re-launching as ModelOp, reflecting the importance of modeling to artificial intelligence (AI) and machine learning (ML) implmentations.” | press | 2026-08-31 |
| s11 | CB Insights: ModelOp company profile “ModelOp was formerly known as Open Data Group. It was founded in 2016 and is based in Chicago, Illinois.” | research | 2026-08-31 |
| s12 | Baird Capital: announcement of its investment in ModelOp “Fortune 500 companies and innovative enterprises — including Fidelity Investments, FINRA, and Bristol Myers Squibb — use ModelOp as a single source of truth that provides real-time visibility into AI risk, performance, health, and value.” | press | 2026-08-31 |
| s13 | CHAI assurance resource provider directory: the ModelOp entry “ModelOp is not a public SaaS solution due to the security and policy needs of enterprises and healthcare organizations.” | official | 2026-08-31 |
| s14 | Trust-surface probe 2026-08-31: no trust portal found. trust. and security. subdomains and a control name did not resolve, /security /trust /compliance 404 | official | 2026-08-31 |
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