No API-Ready Systems
Legacy systems and data aren't exposed as clean, callable APIs that agents can safely use.
AI can only act on the systems it can reach. We modernize your APIs and build the MCP servers that let AI agents use them securely.
Teams building agents on Claude, OpenAI and open-source models hit the same wall. Their systems are not exposed as clean APIs, and no one has decided how an agent signs in, what it may touch, or how its actions are audited. Every agent action is an API call, so the API layer is where AI gets planned, deployed, secured and monitored.
The Gap
In-house agent builds move fast until they have to touch real systems. That is an API problem, and most teams have not solved it yet.
Legacy systems and data aren't exposed as clean, callable APIs that agents can safely use.
No clear way to secure endpoints, manage tokens or control what each agent can read and do.
Sensitive data flows through agent calls with no tokenization, redaction, or audit trail.
One person overseeing hundreds of agents, with no plan to deploy, throttle and monitor that traffic.
What We Build
From modernizing the first legacy endpoint to governing a fleet of agents, we build and run the layer every AI workflow depends on.
Wrap legacy and SaaS systems in clean, well-documented REST and GraphQL APIs that AI can call, without replacing what already works.
Expose tools and data to agents through Model Context Protocol (MCP) servers, the open standard agents use to discover and call tools.
Authentication, authorization, mTLS, and least-privilege access on every endpoint.
Issue, scope, rotate and revoke tokens for people and for fleets of agents alike.
Tokenization, redaction, and policy enforcement so sensitive data stays protected in every call.
Quotas and traffic shaping that keep agent fleets from overwhelming your systems.
Separate, govern, and expose internal, partner, and public APIs with the right controls for each.
Full logging, tracing and audit of every API call and agent action, so each one is provable and reversible.
Every agent action is an API call. We make that call safe, governed and ready for AI.
Built for Agentic Scale
When a handful of people oversee hundreds of agents, API access can't be managed by hand. We build the control plane, so every agent gets exactly the access it needs and nothing more.
Map which systems, data, and actions agents need, and the guardrails around each.
Ship the APIs and MCP servers agents call, versioned and documented from day one.
Scoped tokens, least-privilege access, PII/PHI protection, and per-agent throttling.
Observe every call and agent action in real time, with audit and rollback when it counts.
Plan → Deploy → Secure → Monitor
The control plane for a fleet of agents.
Proven in Production
We build this foundation for clients because we depend on it ourselves. Each of our AI products acts on real systems through governed APIs.
Provider directory data validated against the CMS NPPES registry API and submitted to CMS and payers in FHIR-aligned formats, with an audit trail behind every change a health plan attests to.
Visit pdichecker.comSales agents that act across CRM, email, LinkedIn and WhatsApp through 3,000+ governed integrations, with approval gates and a log of every agent action.
Visit hybri.aiPersonal data tokenized before every model call and restored after, with self-hosted and air-gapped inference for privileged legal matters.
Explore VaultMindFAQ
What engineering and security leaders ask before they connect AI agents to production systems.
An MCP server exposes tools, data and actions to AI agents through the Model Context Protocol, an open standard introduced by Anthropic and now supported across the major AI platforms. Instead of writing custom glue for every model, you describe a capability once and any MCP-compatible agent can discover and call it, under the permissions you set.
Yes. An MCP server is a thin, agent-friendly layer over your APIs. The APIs underneath still do the work, and they still need authentication, rate limits and monitoring. We build both layers together so they share one security and governance model.
Every agent gets its own scoped, short-lived credentials instead of a shared key. We enforce least-privilege authorization per tool, mTLS between services, per-agent rate limits and quotas, and log every call so each action traces back to the agent, the person who approved it and the data it touched.
Sensitive fields are tokenized or redacted before they reach a model and restored only where policy allows. Policies are enforced at the API gateway, so protection does not depend on every agent or prompt getting it right, and every access is audited to support HIPAA and SOC 2 evidence.
Yes. We wrap legacy and SaaS systems in governed APIs without replacing them, and deploy on Azure, AWS, Google Cloud, on-premises or hybrid, including private NVIDIA DGX environments for data that cannot leave your boundary.
Most teams start with an API and MCP readiness review. We map the systems and actions your agents need, score the security and governance gaps, and deliver a rapid proof of concept or business case. You can also start with our free AI Maturity Check.
Ready to Build?
Book a consultation to review how ready your APIs are for AI agents. We deliver a rapid proof of concept or business case tailored to your systems, data and security requirements.
Schedule a consultation