How Aravo used AI to cut vendor risk assessments from nine hours to minutes

Challenge

• A platform at an inflection point: Aravo’s CEO and board set a clear mandate—show visible, working AI progress by year-end, in a way that felt genuinely native, not bolted on.

• A build-vs-buy dilemma: Internal teams had domain expertise and vision, but not the bandwidth to build an AI stack from scratch—or the luxury of time.

• An industry stuck in outdated patterns: The third-party risk management (TPRM) industry had long relied on manual vendor questionnaires and unreliable self-reported data, leaving compliance teams buried in documentation that no generic bolt-on AI solution could fix.

Solution

The Devs.ai platform by AppDirect, white-labeled and embedded natively into Aravo’s platform as AI Canvas, featuring:

• Agent Builder—A no-code/low-code studio for building and deploying custom AI agents, embedded natively into Aravo’s platform

• Agent Catalog—For managing, distributing, and governing agent portfolios across internal teams and enterprise customers

• Multi-tenant white-labeling—Enabling Aravo to brand the full experience as “AI Canvas” within their own product

• MCP server integration—Connecting the Devs.ai studio directly to Aravo’s core data model and external risk intelligence sources

• Enterprise governance controls—Role-based permissions, multi-tenant segmentation, audit trails, and private workspaces

Results

Devs.ai gave Aravo something the TPRM industry hadn’t seen before: a genuinely native AI layer built on 25 years of risk management infrastructure, deployable to internal teams today and enterprise customers tomorrow.

• Assessment labor cut 80–90%: AI-powered survey agents parse audited vendor documents and auto-populate compliance questionnaires—complete with confidence scores, inline citations, and full audit trails.

• Regulatory research accelerated: Impact analysis that once took a senior executive nine hours of cross-system research now completes in minutes.

• Fully branded AI Canvas: Aravo’s native agent interface, built and deployed without constructing an AI stack from scratch.

• Multiple agent types in production: Survey, decision, corrective action, and interactive agents, each embedded directly into existing TPRM workflows.

• Charter customer pipeline: Six to ten implementations targeted over the next three to six months, with interest from some of the world’s largest companies.

The problem: An industry buried in questionnaires

For 25 years, Aravo has been the system of record for some of the world’s most complex third-party risk programs. Their enterprise customers—Fortune 100 companies spanning financial services, pharmaceuticals, high-tech, and oil and gas—manage anywhere from 5,000 to more than 500,000 third parties, each carrying its own web of risk domains: information security, data privacy, ESG, financial, legal, ABAC, and more.

The assessment process had long been a recurring source of pain. Compliance teams sent out questionnaires. Vendors answered them—or didn’t. Back-and-forth emails accumulated. Unanswered questions piled up. Then someone on the risk team had to manually comb through the responses, identify discrepancies, flag failures, and document corrective actions—all before any real remediation could begin. And when a new regulation dropped or a disruption hit, finding the answer meant running a report buried in a system only a handful of administrators knew how to navigate.

The SaaS industry’s response had largely been to bolt AI features onto existing platforms. Aravo rejected that approach from the start.

From build-vs-buy to a two-day breakthrough

Aravo had the domain expertise, the data model, and the customer relationships. What they lacked was a path to genuine AI capability—fast—without standing up an entire AI infrastructure from scratch. After evaluating a range of alternatives, including Replit and several generic AI platforms, nothing met the core requirements: a white-label capable, multi-tenant, API-first agent studio that could be embedded directly into Aravo’s existing platform and UX.

When Aravo learned about Devs.ai, things changed quickly. Unlike everything they had evaluated, Devs.ai was built to be embedded natively inside an existing product. Rather than a standard demo, AppDirect proposed a two-day hands-on hackathon with Aravo participants from product, UX, engineering, and executive leadership—building working agents directly inside the Aravo platform. What might have taken months of evaluation compressed into a decision made in days. Aravo signed as an early-adopter customer, and work began.

A genuinely native AI layer

What Aravo built isn’t a feature. It’s a foundation. A native AI layer lives inside the platform—inheriting the data model, the permissions architecture, the configured workflows, and 25 years of institutional risk knowledge. That is what makes the capabilities that follow possible, and what makes them trustworthy in the way that enterprise compliance programs require.

Survey agents now parse audited vendor documents—SOC 2 reports, information security policies, EISB documentation—and automatically populate compliance questionnaires. Every automated answer includes a confidence score, an inline citation linked to the source document, and a full audit trail. Every answer can also be disagreed with, overridden, and flagged, with all actions reflected in the audit trail. Aravo’s patented architecture ensures that if the system is not confident in an answer, it will not attempt to provide one.

Underpinning these capabilities is the Devs.ai Agent Builder—a no-code studio that lets Aravo’s teams create and deploy agents without engineering support—and the Agent Catalog, which manages and governs the full agent portfolio across internal teams and, eventually, enterprise customers.

The AI Canvas: Rich intelligence in the hands of everyone who needs it

The AI Canvas is the visible face of Aravo’s AI strategy: a native interface embedded directly into the Aravo platform where users can discover and interact with the full catalog of intelligent agents. By design, it simply feels like Aravo—because it is.

The Canvas automatically inherits the persona and permissions of each user. A financial risk professional sees financial agents. An information security professional sees InfoSec agents. No configuration required. On that foundation, Aravo built a library of agent types, each powered by the Devs.ai Agent Studio and connected to Aravo’s core data model via MCP:

• Expert agents tap into curated knowledge bases to answer regulatory and domain-specific questions

• Query agents surface structured data from the Aravo system of record in plain language

• Survey agents parse vendor documents and auto-populate assessments

• Decision agents use historical data and business rules to recommend risk scoping, criticality classifications, and due diligence assignments

• Corrective action agents extract findings from completed assessments and generate non-compliance records, giving teams a head start on remediation

Regulatory agility in a shifting landscape

One of the highest-stakes demands in TPRM is navigating regulatory change. Regulations like DORA arrive with complexity, amendment cycles, and immediate implications for supplier scope. Compliance professionals have historically had to piece together the impact manually—jumping between systems and reports, often spending hours to answer a question that a board member needs answered now.

When a new regulation drops or an amendment shifts the compliance landscape, agents connected to Aravo’s data model via MCP can surface the answer—which suppliers fall within scope, what next steps are required—in minutes, not hours, and without ever leaving the platform.

When Aravo shared the platform with Dell, the reaction captured something deeper than time savings. Dell’s team described it with a phrase that has since become shorthand for what the platform delivers: it gives more people agency over the information. The ability to ask a question at 1 a.m.—during a disruption, without needing to find the right administrator—is what enterprise-grade AI access actually looks like.

Trust but verify: The philosophy that separates Aravo from the market

Aravo’s approach to AI is shaped by a conviction that distinguishes it sharply from the rest of the TPRM industry: automation without accountability is not good enough. Most competitors stop at the feature and ask users to accept the result on faith. Aravo doesn’t.

Every agent-generated response includes a confidence score, a direct citation to the underlying source document, and a link for users to verify the reference themselves. Every automated action is logged in the audit trail. Users can disagree with any automated finding at any time, and that disagreement is recorded and factored into future model behavior. The result is a platform defensible whether reviewed five minutes or five years after the fact.

That philosophy extends into model selection, too: Aravo’s customers are not locked into a single LLM, but can choose from all commercially available flagship models—preserving flexibility as the AI landscape continues to evolve.

Looking ahead: From internal adoption to an AI-native enterprise platform

Aravo’s AI roadmap unfolds in deliberate phases.

• Phase 1 is already underway: Internal adoption has taken hold across product, engineering, UX, security, and executive teams. The AI Canvas is deployed, the MCP bridge connecting Devs.ai to Aravo’s core data model is live, and agents are built, tested, and delivering results.

• Phase 2 scales to enterprise customers: Aravo will extend agent-building capability to Fortune 500 risk, compliance, procurement, and security teams—giving them the ability to build their own bespoke agents within the Aravo platform, with role-based access and agent catalogs tailored to each organization.

• Phase 3 introduces monetization: Agent-building will be packaged as an enterprise add-on module, with individual agent licensing to follow as adoption patterns mature.

In the near term, Aravo is targeting six to ten charter customers—companies that are, as Dean Elms put it plainly, “some of the biggest companies on the planet.”

What Aravo has built is twenty-five years of institutional risk management knowledge, now accessible through natural language, embedded natively into the workflows where it matters most, and grounded in a level of transparency and auditability that never asks users to simply trust the AI.

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