Capability Statement.

Canall.ai, Inc. forward-deploys private agent platforms for teams that need AI to operate inside real security, policy, and procurement constraints.

We are a Delaware C-corporation, foreign-registered in Virginia and headquartered in Henrico County (greater Richmond). The platform — twelve capabilities, four deployment shapes, the same agent library across all four — is built and in production use. The cut that produces a company this size is the founder's combination: enough platform-engineering depth to have designed the product personally, and enough experience of regulated data environments to know what an examiner asks for.

“The deliverable at the end of a deployment is not only a working system — it is an evidence package the customer hands to their examiner.”

The twelve capabilities.

An agentic platform-for-platforms requires twelve components. A customer building this themselves builds all twelve and then staffs their maintenance indefinitely. The table below is the build-versus-buy argument in its entirety — all twelve are built and in production use.

Why it survives an audit.

The capabilities above are engineering. What follows is why a risk officer signs — the six controls that make a private agent platform defensible at the perimeter your examiner expects.

• Nothing leaves unless approved. Egress is an explicit allow-list, not a default. In air-gapped configurations there is no egress path at all.
• The agent never holds credentials. The secrets broker issues scoped, brokered access; a compromised or misbehaving agent cannot exfiltrate a key it never possessed.
• Every run is traced and replayable. Span-level traces with cost and latency produce an audit trail that answers which model processed which data, when, under whose authority.
• Outputs carry their sources. Citation-grounded generation means each assertion points back to the passage that supports it. The system reports when the record does not answer the question rather than guessing.
• Accuracy is measured, not asserted. The evaluation harness runs against your own golden tasks, producing documented performance on your data rather than a vendor benchmark on someone else's.
• Policy is enforced at runtime. RBAC plus OWASP agentic hardening, with policy evaluated by the runtime rather than trusted to application logic.

The commercial translation: the deliverable at the end of a deployment is not only a working system but an evidence package — trace samples, eval results on the customer's tasks, policy configuration, and access model — that the customer hands to their examiner or auditor.

Company & procurement facts.

Codes marked TBD are pending founder/ops input and will be populated as registrations and certifications complete. We will not list a certification we have not been awarded.

Where it runs.

Four deployment shapes. One platform, one agent library, one license. The perimeter is a risk-model decision, not a re-platforming one — and an upgrade between shapes is a configuration change, not a migration.

Questions we hear.

What does Canall.ai deliver?

A forward-deployed private agent platform. Twelve platform capabilities — multi-model abstraction, cost and loop guardrails, secrets broker, observability and tracing, evaluation harness, policy and governance, deployment portability, skill and connector library, human-in-the-loop surfaces, sandboxed execution, agent design and deployment loop, and a team to maintain all of it. All twelve are built and in production use.

Where does the platform run?

Four shapes, one platform: Hosted, Customer VPC, On-premises, or Air-gapped. The agent library and license are identical across all four — pick the perimeter that matches your risk model. For air-gapped work there is no outbound path at all.

How does it survive an audit?

Six controls: egress is an explicit allow-list, not a default; the agent never holds credentials (a secrets broker does); every run is traced and replayable; outputs carry their sources (citation-grounded); accuracy is measured on your tasks, not asserted; access is controlled and policy is enforced at runtime. The deliverable at the end of a deployment is an evidence package the customer hands to their examiner.