Capability maps, not big-bang rewrites
We inventory systems of record, integration seams, and data sensitivity before touching code. MCP scopes what AI can see and do—so you can open access gradually.
MCP · Legacy modernisation
Connect AI Assistants to Legacy Systems—with MCP
Your roadmap probably includes AI copilots, agentic workflows, or developer productivity tools. The blocker is rarely “which model?”—it is safe, deterministic access to decades of business logic sitting in systems that were never built for LLMs. We specialise in bridging that gap using MCP and disciplined integration architecture.
How we de-risk legacy AI
Authn, authz, and audit by design
Tools inherit your identity model: SSO, role mapping, per-tenant isolation, and tamper-evident logs for regulator-ready operators.
Operate like software, not a demo
Versioned prompts, staging environments for tools, synthetic tests, and dashboards for latency, errors, and spend—so AI keeps working after the launch party.
Where MCP shines first
- Support & operations: ticket enrichment, knowledge surfacing, runbooks executed against live systems with approvals.
- Sales & account teams: CRM hygiene, quote prep, and research pulls constrained to entitled records.
- Engineering velocity: internal docs, deployment hooks, and bespoke admin APIs exposed safely to coding agents.
- Regulated environments: explicit data residency paths, redaction, and separation between retrieval and mutating tools.
FAQ
What is MCP in plain language?
Model Context Protocol (MCP) is an open standard for connecting AI clients—such as Claude Desktop, coding agents, or custom assistants—to your data and actions through well-defined “tools”. Instead of pasting credentials into a chat, models request structured operations you implement and govern.
Do we need to replace our legacy ERP or CRM?
Usually no. MCP sits beside existing systems: we wrap stable APIs, stored procedures, or message buses so AI gets narrow, policy-checked capabilities. Replacement projects become optional instead of prerequisites for AI value.
How is this different from a generic integration or RPA?
MCP gives models a consistent tool surface with explicit schemas, which improves reliability versus brittle screen scraping. We still combine it with RAG, workflow automation, and human approvals where outcomes are high stakes.
What does an engagement look like?
Typically a discovery sprint, a thin vertical slice (one workflow end-to-end), then expansion: more tools, tighter evals, and broader rollout. Timelines depend on API quality and compliance gates—we quote after a technical walkthrough.
Bring your architecture diagrams
If you can share integration constraints and a priority workflow, we can outline an MCP tool map, risk register, and incremental delivery plan—usually within a short discovery window.