Demo
Conference room
Works on sample data, skips security review, no audit trail, no daily users.
Technology
Production AI in your cloud.
Agentic workflows, retrieval, automation, and the governance layer that keeps them auditable. Model-agnostic, built for handoff. Not POC theater.
Book Your Verdo Sequence
Production grade
Built for contact
with reality.
40%of agentic AI projects predicted to be canceled by 2027External · Gartner
Production
Contact with reality
Real data volumes, security review, edge cases, audit requirements, and daily users.
System classes
What Verdo builds.
Four system classes, with governance and human approval built into every consequential deployment.
Workflows & orchestration
Multi-step agents inside your processes, with human approval where consequences are real.
Proof · $135B AUM fund due diligenceKnowledge systems
Grounded in your documents, every answer source-cited.
Proof · red-team modeAutomation
High-volume workflows run start to finish, with full audit trails.
Proof · €25M in 90 daysPlatform & product
AI investments re-architected around real workflows.
Proof · 22% → 94% adoption
Whose stack?
Yours, including the legacy parts.
Nearly 60% of AI leaders name legacy integration as the primary barrier. Verdo builds on what you already run: your cloud, model-agnostic, auditable, handoff-ready.
Evidence
One platform.
Measured adoption.
Seven tools became one AI platform for a $135B private-equity fund.
FAQ
Technology questions.
Does Verdo implement platforms, or only build custom systems?
Both, on merit. Where a platform covers the need, Verdo says buy. Where the value lives in your proprietary workflows, Verdo builds.
Will Verdo pass our security review?
Built to. Systems deploy in your cloud with scoped permissions, full audit trails, and human approval gates, and nothing leaves your environment. We also hold a SOC-2 Certification.
What stops an agent from doing something it shouldn't?
Three things, by design. Scoped permissions cap what an agent can touch. Human approval gates sit wherever a step has real consequences, so nothing irreversible happens without a person saying yes. And every action lands in the audit trail, so what the agent did can be reconstructed step by step, not guessed at.
How does this hold up in a compliance or audit review?
That is what the governance layer is for. Every automated action carries a full audit trail, every answer cites its source, consequential steps pass through human approval, and all of it runs inside your environment. When an examiner asks why the system did what it did, the answer is on record, not reconstructed from memory.
Models get leapfrogged every few months. What happens to what you build for us?
It gets better, because the model is a component, not the foundation. The durable asset is everything around it: the workflows, the integrations, the retrieval, the governance layer. When a stronger or cheaper model ships, it swaps in and the system inherits the upgrade. Marrying a model is how AI systems depreciate. Yours is built to appreciate.
Who owns the systems after handoff?
You do. Documentation, runbooks, and training ship with every build.
Who maintains the system after handoff?
By design, your team can: documentation, runbooks, and training ship with every build precisely so handoff is a transfer, not a goodbye.