Top AI governance companies (Updated August 2026)

Every AI governance program starts with a policy document and fails somewhere else. The policy reads well. It names the frameworks, assigns a committee, and promises oversight. Then a product team ships a model nobody registered. Or a scoring model quietly drifts for three months and starts declining the wrong applicants. Or legal asks how you can prove a hiring model is not discriminating, and the honest answer is a spreadsheet last updated in the spring. AI governance lives and dies on the parts a policy cannot see: which models and agents are actually running, whether anyone is testing them after launch, and whether there is a real audit trail on the day a decision gets challenged. The companies and platforms on this list address those parts, not the policy theater around them.

The category is hard to buy well because everything looks the same on a landing page. Every platform maps to the EU AI Act. Every one claims to manage risk, catalog models, and produce audit-ready evidence. The differences that decide whether you stay compliant or ship shelfware are invisible until you ask the right questions: does the tool discover the AI you forgot you had, does testing run once or continuously, does the audit trail hold up under a regulator's questions, and does it reach the custom models where your real risk lives. This guide is organized around those questions. It weighs framework coverage, discovery and inventory, testing and monitoring depth, evidence quality, and fit with how your AI actually ships.

The eight AI governance options on this list are Credo AI, RaftLabs, Holistic AI, IBM watsonx.governance, OneTrust AI Governance, Monitaur, Fiddler AI, and Asenion (formerly Fairly AI). RaftLabs is on this list. We wrote our own entry with the same directness we applied to everyone else.

How we evaluated this list

A buyer's guide is only as honest as its criteria, so here are ours before the companies. We did not rank on analyst ratings alone. A spot in a Gartner quadrant tells you a vendor sells well, not that its audit trail will survive a regulator's questions. We weighted how current the framework mapping stays, how well the tool discovers the AI you did not register, how deep testing and monitoring run after launch, how usable the evidence is when a decision is challenged, and how well the option fits the way your AI actually ships. Where a pricing figure or a rating could not be verified against a live source during sourcing, we say so and hedge rather than repeat a number we could not confirm.

We evaluated options on five criteria:

No company paid for placement on this list.

1. Credo AI

Credo AI is a purpose-built AI governance platform aimed at enterprises that need to see, control, and prove out their entire AI footprint. Its pitch is that governance built for AI beats a bolt-on module inside a traditional GRC tool, and the product is organized around that idea. The core is an AI Registry that discovers and catalogs models, agents, vendors, and applications, including shadow AI nobody registered, and classifies each by risk. On top of that sit a risk intelligence layer and a policy engine that turns written policy into automated workflows and audit-ready evidence.

The reason a dedicated registry matters is that you cannot govern what you cannot see. Most large companies underestimate how much AI is already running across their teams, bought as features inside SaaS tools or built quietly by a product group. A platform that surfaces that footprint changes the conversation from theory to inventory. Credo AI also leans into agent governance, treating agents as first-class governed entities with a control library for tool misuse and scope drift, which reflects where risk is moving in 2026.

Credo AI ships policy packs mapped to a wide set of rules, which is where a specialist platform earns its price. Instead of building your own EU AI Act or NIST mapping, you start from a maintained one and adapt. That head start is real, and it is the reason an enterprise standardizing its whole AI portfolio on one governance layer tends to shortlist a purpose-built platform first. The caveat is the same as its strength: this is a portfolio-and-policy layer, and it governs the AI it can connect to. If your hardest risk sits inside a custom model that needs controls wired into the application itself, a platform is a complement to that work, not a replacement for it.

Notable work -- Credo AI publishes policy packs covering the EU AI Act, NIST AI RMF, ISO 42001, OMB M-25, Colorado ADMT, and NAIC AI, and states native integration with 300+ systems including Snowflake, Databricks, AWS, Azure, and ServiceNow. Specific named customer outcomes are not verified here, so ask for references in your industry and regulatory footprint before signing.

Pricing signal -- Pricing is not publicly listed; this is an enterprise annual contract. Ask what the price scales with -- models, agents, or seats -- and request a quote that shows the cost of growth, not just year one.

What to watch -- Credo AI is a governance-and-policy platform, strongest for an enterprise consolidating a large, mixed AI portfolio under one layer. A small team with two models, or a company whose core risk needs controls built inside a custom application, may be better served by a lighter tool or a focused build.

• Best for: Enterprises consolidating a large, mixed AI and agent portfolio under one purpose-built governance layer.

• Specialization: AI registry and discovery, policy-to-code workflows, agent governance, framework policy packs

• Pricing: Not publicly listed; enterprise annual contract

• Recognition: Positioned as a purpose-built AI governance specialist; confirm current analyst standing and references before engaging

2. RaftLabs

RaftLabs is an AI-first tech studio that has built custom software for established businesses since 2015, including clients such as Vodafone and T-Mobile. Where most options on this list are platforms you subscribe to, RaftLabs builds the governance itself: custom AI governance software and model-risk infrastructure wired into the systems that make consequential decisions. The work centers on the parts that decide whether an AI deployment survives a regulator or a lawsuit: model documentation, bias evaluation across protected attributes, explainability outputs, human override paths, guardrails, and an audit trail that holds up when a decision is challenged. Engagements start with a scoped discovery sprint that fixes the model inventory and the risk questions before a line of infrastructure gets written.

The reason a build reaches further than a subscription in some cases is specific to where risk hides. A platform governs the AI it can connect to. When the model that carries your real exposure is proprietary, or an internal agent, or a legacy scoring system no product integrates with cleanly, governance has to be engineered into the application, not bought beside it. RaftLabs designs the controls into the data model and the request path, so explainability and audit logging are defaults rather than features added near launch.

In practice the discovery sprint produces two artifacts before design starts: a risk map that says exactly which decisions the AI makes and who they affect, and a control plan that lists the documentation, tests, monitoring, and override paths each model needs. Those two documents are where most of the real cost lives, and pinning them down early is what lets a fixed price hold. It is also what makes the difference on the day a real edge case lands -- a model that starts drifting, a challenged decision with no clear explanation, an auditor asking for a record that was never captured. RaftLabs runs discovery precisely so those cases are named while they are cheap to handle, in the architecture, rather than discovered in production when they mean a rebuild.

Notable work -- RaftLabs has shipped 30+ products since 2015 for clients including Vodafone and T-Mobile, evidence of building at enterprise scale with the security and reliability regulated AI demands. It has not published a standalone AI governance case study on this list, so ask to see relevant model-documentation, bias-evaluation, explainability, and audit-trail work directly during scoping.

Pricing signal -- $29-$49/hr with fixed-price engagements and milestone payments, scoped after the discovery sprint that defines the risk map and control plan. A focused build around one model or agent typically runs in the low tens of thousands, a one-time cost rather than a recurring platform fee. Fixed-price suits buyers who want a known number before regulatory complexity is priced in.

What to watch -- RaftLabs builds custom governance infrastructure and owns the full delivery stack -- discovery, architecture, engineering, and delivery -- which fits businesses that need controls inside their own systems. A company that governs a large, mixed portfolio across many teams and wants a shared inventory and reporting layer that non-engineers run is better served by a governance platform, or by pairing one with a focused build for the highest-risk models.

• Best for: Established businesses that need governance, guardrails, and audit trails built into custom or high-risk AI systems, without hiring an internal team.

• Specialization: Model documentation, bias evaluation, explainability, human override, guardrails, audit-trail engineering

• Pricing: $29-$49/hr, fixed-price engagements

• Clutch: 4.9/5

3. Holistic AI

Holistic AI is an end-to-end AI governance platform built around three verbs: identify, protect, and enforce. The identify layer scans cloud platforms, code repositories, and SaaS applications across AWS, Azure, GitHub, Databricks, and 20+ integrations to build a real-time inventory of the AI running in your business. The protect layer runs 40+ specialized tests covering bias, fairness, toxicity, hallucination, prompt injection, and adversarial attacks, with continuous monitoring for drift. The enforce layer automates compliance workflows with deployment gates and approval steps.

What stands out is the testing depth and the runtime enforcement. Many governance tools stop at inventory and paperwork. Holistic AI pushes into the technical work of actually testing a model for the failure modes that matter, and it adds a supervisory layer for agentic AI it calls Guardian Agents, split into agents that observe actions in real time and agents that intervene when a risk threshold is crossed. For a buyer whose worry is not just documentation but a model behaving badly in production, that testing-and-enforcement focus is the differentiator.

The platform maps to the EU AI Act, NIST AI RMF, ISO 42001, and NYC Local Law 144, which covers the frameworks most enterprises face plus a specific hiring-law obligation. That breadth suits a company that wants one tool to both discover its AI and stress-test it. The honest caveat is that a testing-heavy platform still governs what it connects to, and the value depends on whether its 40+ tests map to the specific risks in your models. Confirm the tests you care about are ones the platform actually runs, not adjacent ones.

Notable work -- Holistic AI states 20+ integrations for discovery across major cloud and code platforms and built-in frameworks for the EU AI Act, NIST AI RMF, ISO 42001, and NYC Local Law 144. Specific named customer outcomes are not verified here; ask for references and a live test run against a model like yours before signing.

Pricing signal -- Pricing is not publicly listed; expect an enterprise annual contract. Ask how pricing scales with the number of models or tests, and request a quote that reflects your real model count.

What to watch -- Holistic AI is strongest for buyers who want deep, continuous technical testing plus runtime enforcement. A company that only needs a lightweight inventory and a policy record, with no appetite for a testing program, may be paying for depth it will not use.

• Best for: Enterprises that want deep, continuous bias and safety testing plus runtime enforcement, not just an inventory and paperwork.

• Specialization: AI discovery, 40+ risk tests, drift monitoring, agentic guardrails, compliance workflows

• Pricing: Not publicly listed; enterprise annual contract

• Recognition: Established AI governance and safety specialist; confirm current analyst standing and references before engaging

4. IBM watsonx.governance

IBM watsonx.governance is the governance component of IBM's broader watsonx platform, which also includes watsonx.ai for building and deploying models and watsonx.data for data management. Introduced with watsonx in May 2023, it applies IBM's AI tooling to lifecycle governance: managing the risk of AI applications, maintaining compliance with evolving regulation, and reducing bias through automated oversight. For an enterprise already inside the IBM ecosystem, that integration is the pitch.

The reason to weigh watsonx.governance carefully against your own stack is exactly that ecosystem fit. If you are building and serving models on watsonx.ai, governing them with the matching component keeps documentation, monitoring, and approvals in one place, with a vendor relationship you already manage. That coherence is genuinely hard to match by stitching together separate tools, and it is why IBM shops shortlist it first. The trade-off is the mirror image: the tighter the fit with IBM, the less natural it is for a company whose models live entirely on another cloud or in a homegrown stack.

IBM brings enterprise weight to the compliance conversation, which matters for the largest regulated organizations that need a vendor their auditors and their board already recognize. As with any component of a large suite, the useful test is whether you will use enough of the surrounding platform to justify the governance module, or whether a standalone specialist would fit your footprint with less overhead.

Notable work -- watsonx.governance is a documented part of the IBM watsonx platform launched in May 2023, positioned for AI lifecycle governance, risk, and bias management. Specific customer outcomes are not verified here; ask IBM for references in your industry and, critically, for how the module governs models that sit outside the IBM stack.

Pricing signal -- Pricing was not verified against a live source during sourcing; IBM typically offers cloud SaaS and enterprise options, so expect enterprise economics and confirm the current model and any consumption components directly.

What to watch -- watsonx.governance is the natural choice inside the IBM ecosystem. A company whose models run entirely on another cloud, or a small team wanting a light standalone tool, should weigh whether the surrounding platform justifies the module.

• Best for: Enterprises standardizing on IBM watsonx that want governance inside the same platform as their models and data.

• Specialization: AI lifecycle governance, risk and compliance management, bias detection, watsonx integration

• Pricing: Not verified; enterprise SaaS and on-prem options -- confirm current model directly

• Recognition: Backed by IBM enterprise scale; confirm current analyst standing and references before engaging

5. OneTrust AI Governance

OneTrust AI Governance sits inside OneTrust's larger privacy, security, and GRC platform, and that lineage is its defining trait. The AI governance product brings a centralized inventory of models, datasets, agents, and vendors, standardized risk assessment against global frameworks, configurable intake and approval workflows, continuous monitoring of drift and safety, and policy enforcement that includes runtime prompt and output filtering with data masking. It maps out of the box to the EU AI Act, NIST AI RMF, and ISO 42001.

The reason the GRC heritage matters is integration with the rest of your compliance work. Many enterprises already run OneTrust for privacy and vendor risk, and for them AI governance that lives in the same platform means one inventory, one workflow engine, and one place auditors already look. That consolidation is the pitch, and it is a strong one for a company that treats AI risk as an extension of its existing governance program rather than a separate discipline. OneTrust was named a Visionary in the 2026 Gartner Magic Quadrant for AI Governance Platforms, which signals it is a recognized entrant in the category.

The trade-off with a suite module is depth versus breadth. A platform that spans privacy, security, and AI governance is broad by design, and a buyer whose only need is deep, specialized AI testing may find a focused specialist goes further on that one axis. The right question is whether you want AI governance folded into a single compliance platform, or a best-of-breed tool for AI specifically. If you already live in OneTrust, the folding-in case is strong.

Notable work -- OneTrust AI Governance is part of the established OneTrust GRC platform and maps to the EU AI Act, NIST AI RMF, and ISO 42001, with a Visionary placement in the 2026 Gartner Magic Quadrant for AI Governance Platforms. Specific AI-governance customer outcomes are not verified here; ask for references using the AI module specifically, not the broader platform.

Pricing signal -- Pricing is not publicly listed; this is an enterprise contract, often bundled with other OneTrust modules. Ask how the AI module is priced on its own so you can compare it against a standalone specialist.

What to watch -- OneTrust AI Governance is strongest for companies already on OneTrust that want AI risk inside their existing compliance platform. A team wanting the deepest specialized AI testing, or one not already invested in the suite, should compare it against a purpose-built tool.

• Best for: Enterprises already running OneTrust that want AI governance folded into their existing privacy and GRC platform.

• Specialization: AI inventory, risk assessment, continuous monitoring, runtime policy enforcement, GRC integration

• Pricing: Not publicly listed; enterprise contract, often bundled

• Recognition: Visionary, 2026 Gartner Magic Quadrant for AI Governance Platforms; confirm references before engaging

6. Monitaur

Monitaur is an AI governance platform built for regulated industries, with a clear center of gravity in insurance and financial services. Its model runs on three moves: define enterprise standards through policy and program design, manage compliance with inventory tracking and vendor governance, and automate validation at scale with drift and bias detection, stress testing, and transaction search. Its published customer logos include insurance names such as Progressive and Unum, which signals real deployment in a demanding, heavily regulated sector.

The reason an industry-focused platform is worth attention is that regulated model risk is not generic. An insurance underwriting or claims model has to answer to specific rules, specific regulators, and specific expectations about explainability and fairness that a horizontal tool treats as edge cases. A platform whose product decisions were shaped by that world tends to raise the right questions on the first call rather than after a filing. For a carrier or a financial institution governing consequential models, that domain fit is a genuine differentiator, not marketing.

Monitaur has earned analyst recognition, including a Visionary placement in an inaugural Gartner Magic Quadrant for AI Governance and a Customer Favorite mention in a Forrester Wave, which points to satisfied deployments rather than just a strong pitch. The honest caveat is the flip side of the specialization: a company outside regulated financial services, or one whose AI risk is mostly generative and consumer-facing rather than actuarial, should confirm the platform fits its specific model types before assuming the insurance strength transfers.

Notable work -- Monitaur publishes insurance customer logos including Progressive and Unum and holds analyst recognition as a Visionary in a Gartner Magic Quadrant for AI Governance and a Forrester Wave Customer Favorite. Specific outcome metrics are not verified here; ask for references in your regulatory line and model type.

Pricing signal -- Pricing is not publicly available; the company directs buyers to contact it directly. Expect an enterprise contract scoped to your model portfolio, and ask what it scales with.

What to watch -- Monitaur is strongest for regulated financial services and insurance model risk. A company whose AI is mostly generative, consumer-facing, or outside regulated finance should confirm the platform maps to its model types before assuming the insurance depth carries over.

• Best for: Insurers and financial institutions governing regulated, consequential models like underwriting, claims, and credit.

• Specialization: Model risk management, regulated-industry governance, drift and bias validation, stress testing

• Pricing: Not publicly listed; enterprise contract

• Recognition: Visionary in a Gartner Magic Quadrant for AI Governance; Forrester Wave Customer Favorite

7. Fiddler AI

Fiddler AI approaches governance from the observability side, positioning itself as an AI control plane for enterprise agents. Its strength is watching AI in production: a unified dashboard for performance across models and agents, in-environment evaluation from testing through production, and tracking of actions, tokens, and costs by user and team. Where it goes further than pure monitoring is enforcement -- Fiddler runs inline guardrails at low latency that detect and block PII, PHI, secrets, jailbreaks, and prompt injection before they cause harm.

The reason an observability-led tool belongs on a governance list is that a large share of real AI risk shows up only at runtime. A model that passed every pre-launch test can still drift, hallucinate, or be manipulated by a crafted prompt once real users touch it. Fiddler is built for that moment, with guardrails it states enforce at under 80 milliseconds against PII, jailbreaks, and prompt injection in production. For a team shipping LLM and agent features to real users, that runtime layer is the part of governance that most tools underweight.

Fiddler is also one of the few options here with published pricing, which is a welcome contrast in a category built on custom quotes. It lists a free guardrails tier, a usage-based developer rate, and custom enterprise pricing, and it has raised a Series C, signaling a funded and growing company. The caveat is scope: Fiddler is deepest on monitoring, evaluation, and guardrails, so a buyer who primarily needs framework mapping, a policy workflow, and audit-ready compliance evidence should confirm those governance-paperwork capabilities meet their needs alongside the strong observability.

Notable work -- Fiddler AI publishes its guardrail performance figures, including sub-80-millisecond enforcement, and serves financial services, healthcare, insurance, and government. It has raised a Series C. Specific named customer outcomes are not verified here; ask for references in your sector.

Pricing signal -- Published tiers: a free guardrails tier, a developer rate around $0.002 per trace, and custom enterprise pricing with white-glove support. Usage-based pricing rewards a clear picture of your trace volume, so estimate it before comparing.

What to watch -- Fiddler is strongest on runtime observability, evaluation, and guardrails. A buyer whose primary need is framework mapping, policy workflow, and audit documentation should confirm those compliance features are deep enough alongside the monitoring strength.

• Best for: Teams shipping LLM and agent features to real users that need runtime monitoring, evaluation, and fast guardrails.

• Specialization: AI observability, model and LLM monitoring, evaluations, inline guardrails

• Pricing: Free tier; developer rate around $0.002/trace; custom enterprise

• Recognition: Series C funded; published guardrail performance benchmarks

8. Asenion (formerly Fairly AI)

Asenion is an AI governance platform based in Kitchener, Ontario, that recently rebranded from Fairly AI. If you searched for Fairly AI and landed on a new name, this is why -- the company kept its focus and changed its label. The platform frames its work as end-to-end AI trust, risk, and security management across three stages: assess AI systems against regulatory frameworks, test them for security, privacy, fairness, and safety vulnerabilities, and assure them with runtime governance for models and LLMs in production.

The reason to note the rebrand plainly, rather than skip past it, is that it affects your research. Reviews, case studies, and analyst mentions may appear under either name, so search both when you vet the company. Underneath the label, the substance is a governance platform that maps to ISO/IEC 42001, the NIST AI RMF, the EU AI Act, and the Colorado AI Act, and targets AI-native product companies alongside regulated industries such as financial services, legal, healthcare, and HR tech.

Asenion has drawn analyst attention under both names, including a Major Player placement in the IDC MarketScape for AI Governance Platforms and recognition by Gartner across AI TRiSM categories. That points to a real product with a track record, not a new entrant. The honest caveat is that a recent rebrand adds a small research burden, and a smaller specialist should be pressed harder than an incumbent on roadmap stability, support depth, and reference customers in your exact sector before you commit.

Notable work -- Asenion, formerly Fairly AI, maps to ISO/IEC 42001, the NIST AI RMF, the EU AI Act, and the Colorado AI Act, and holds a Major Player placement in the IDC MarketScape for AI Governance Platforms plus Gartner AI TRiSM recognition. Specific customer outcomes are not verified here; search both names when checking references and ask for clients in your sector.

Pricing signal -- Pricing is not publicly listed; expect an enterprise contract. Given the rebrand, confirm current packaging and roadmap directly rather than relying on older Fairly AI materials.

What to watch -- Asenion is a credible specialist, but the recent name change adds a small research step and it is a smaller player than the suite vendors here. Vet roadmap stability, support, and sector references carefully, and search both names.

• Best for: AI-native product companies and regulated firms wanting a focused assess-test-assure governance platform.

• Specialization: AI risk assessment, security and fairness testing, runtime assurance, framework mapping

• Pricing: Not publicly listed; enterprise contract

• Recognition: IDC MarketScape Major Player; Gartner AI TRiSM recognition (under both Fairly AI and Asenion)

Side-by-side comparison

The question that separates buying a platform from building governance

Most buyers compare AI governance vendors on framework coverage or analyst placement and get the model wrong before they get the tool wrong. The real fork on this list is whether you should be buying a governance platform at all, or building custom governance and guardrail infrastructure around the AI you already run. Picking a vendor before you have answered that question is how companies buy a six-figure platform that governs everything except the one model that carries their real risk, or how they commission a custom build for a portfolio problem a configured platform would have covered in weeks.

Governance platforms -- Credo AI, Holistic AI, IBM watsonx.governance, OneTrust AI Governance, Monitaur, Fiddler AI, and Asenion -- serve the company that governs many models and agents across many teams and needs a shared layer: one inventory, one policy workflow, one reporting surface that non-engineers can run and auditors can read. When your problem is breadth -- too much AI, too little visibility, too many frameworks to track by hand -- a platform is the right answer, and the deepest one for your industry and stack will beat a from-scratch build every time. The differences among them come down to whether you want a purpose-built specialist, a module inside a suite you already own, an industry-specific tool, or an observability-led runtime layer.

Custom governance builds -- RaftLabs and firms like it -- serve the company whose risk lives inside the AI a platform cannot reach or fully control: a proprietary model, an internal agent, a legacy scoring system, or a decision that has to be explained and logged inside the application itself. That is when a build earns its cost: when the thing that makes your AI risky is also the thing no product governs out of the box. The best build partner will tell you honestly, before quoting, whether a platform would cover you first -- and will often recommend both, a platform for the portfolio view and a focused build for the models that carry the most exposure.

Getting the model wrong is more expensive than getting the vendor wrong. A platform bought to govern AI it cannot see becomes shelfware while the real risk stays uncontrolled. A custom build commissioned for a breadth problem a platform would have solved is wasted money and slow to boot. Spend the first conversations on the model -- buy, build, or both -- and the vendor choice gets much easier.

What the frameworks actually require

The standards themselves are the clearest guide to what good governance looks like, and one line from the US framework sets the tone.

The Framework is intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.

That is the NIST AI Risk Management Framework, released in January 2023. The word that matters is incorporate: trustworthiness is meant to be built into how AI is designed and used, not stapled on at the end. Every option on this list is, in effect, a way to operationalize that idea, whether you buy it or build it.

The regulatory stakes are no longer voluntary. The EU AI Act, in force since 2024 with obligations phasing in through 2026 and 2027, carries penalties up to 35 million euros or 7% of global annual turnover for the most serious violations, such as deploying prohibited AI practices. Those figures are why AI governance moved from a nice-to-have to a board-level line item, and why the practical test for any tool or build is not whether it names the frameworks, but whether it produces the evidence -- the inventory, the tests, the audit trail -- that proves compliance on the day someone asks. A dashboard of green checkmarks is not evidence. An exportable record of a real decision, mapped to a named requirement, is.

The verdict

Credo AI for enterprises consolidating a large, mixed AI and agent portfolio under one purpose-built governance layer. RaftLabs for businesses that need governance, guardrails, and audit trails built into custom or high-risk AI without hiring an internal team. Holistic AI for teams that want deep, continuous bias and safety testing plus runtime enforcement. IBM watsonx.governance for enterprises standardizing on the IBM watsonx stack. OneTrust AI Governance for companies already on OneTrust that want AI risk inside their existing GRC platform. Monitaur for insurers and financial institutions governing regulated, consequential models. Fiddler AI for teams shipping LLM and agent features that need runtime monitoring and fast guardrails. Asenion, formerly Fairly AI, for AI-native and regulated firms wanting a focused assess-test-assure platform.

The first filter is the model: buy a governance platform, build custom governance infrastructure, or run both. The second filter is the specific depth your AI needs -- portfolio breadth, technical testing, regulated model risk, or runtime observability. Match those two questions to the right option on this list, and confirm the evidence story with a live audit-trail walkthrough before you commit.

RaftLabs builds custom AI governance and model-risk infrastructure -- model documentation, bias evaluation, explainability, guardrails, and audit trails -- with one team accountable from discovery to delivery. No handoff gap. 4.9/5 on Clutch. Talk to a founder about your AI governance project.