CASE STUDY

Turning a Construction ERP Into an
Enterprise AI-Native Platform

Legacy construction ERP data, now powering an AI-native enterprise platform.

Measuring the Bridge:

Enterprise AI Capabilities Shipped, Domains Covered, Data Made Agent-Ready

Enterprise AI Agent Capabilities Live — Comprehensive agentic tool coverage enabling secure ERP data access.

ERP Domains Exposed to AI Agents — Spans payroll, jobs, labor, invoices, and other core business areas.

Lines of Code Powering the Platform — Reflects the scale of enterprise engineering behind the AI-native bridge.

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Live Projects Enterprise AI Can Query — Real project data now accessible directly through connected AI agents.

Expense Records Served via Agentic Pagination — Large-scale datasets managed without overwhelming AI agent context windows.

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A production-grade enterprise bridge transforms CPM's legacy schema into 62 secure, agent-ready AI capabilities: Built for Agentic AI at scale.

Behind every enterprise AI response was a legacy schema architected to resist exactly that kind of access.

A legacy schema decoupled open/closed from status, stored money as floats, and held 15K+ expense records; Solved through an enterprise-grade agentic bridge applying domain-accurate filters, pagination, and bulk APIs.

We didn't fix the schema. We architected around it, and made it enterprise AI-ready.

HOW WE DID IT

AI agents had no safe way to query CPM directly. The legacy schema decoupled open/closed status from actual status, monetary values were stored as floats, and 15K+ expense records risked overwhelming AI agent context windows, all while requiring accurate filtering, pagination, and reliable bulk data access.

Enterprise AI agents had no safe way to query CPM directly. The legacy schema decoupled open/closed status from actual status, monetary values were stored as floats, and 15K+ expense records risked overwhelming AI agent context windows; all while requiring accurate filtering, pagination, and reliable bulk data access at enterprise scale.

TechAhead architected a NestJS-based enterprise AI bridge exposing CPM through 62 agentic capabilities spanning 14 ERP domains. The system normalized schema inconsistencies, handled floating-point monetary data with precision, and applied pagination and bulk APIs to manage large-scale datasets like 15K+ expense records, enabling Claude, Cursor, and other enterprise AI agents to safely query live payroll, job, and financial data without custom SQL or screen scraping.

Built for Trust: Precision, Safety, and Enterprise-Grade AI Access

TechAhead approached this as an enterprise systems architecture challenge: Ensuring every AI agent capability respected the ERP's real business logic, handled sensitive financial data responsibly, and delivered production-grade, agentic AI access to live enterprise systems.

TechAhead turned our most complex ERP data into something our AI agents could finally access safely and accurately.

Our Success Recipe

Delivering an enterprise-grade agentic AI platform meant balancing systems-level architecture with real business logic. From schema design to protocol implementation, every layer was engineered to handle live financial and operational data accurately, without disrupting CPM's existing enterprise architecture or requiring changes to the underlying ERP system.

NestJS + TypeScript Enterprise Foundation

We architected the AI bridge on NestJS with TypeScript, ensuring enterprise-grade type safety across 62 agentic capabilities handling sensitive financial and operational data. This modular architecture allowed each ERP domain: Payroll, jobs, invoices, to be exposed as independently testable, maintainable AI agent capabilities without compromising reliability or scalability.

Model Context Protocol at Enterprise Scale

Implementing the Model Context Protocol over Remote Streamable HTTP allowed Claude, Cursor, and other enterprise AI agents to call capabilities in real time. This protocol-first architecture ensured consistent, standardized communication between AI agents and CPM's live MySQL database; Enabling true Agentic AI without custom SQL or screen scraping workarounds.

Schema-Aware Enterprise Data Handling

Rather than altering CPM's legacy schema, we engineered systems-level logic to correctly interpret open/closed status mismatches and convert floating-point monetary values into accurate, agent-readable formats, preserving enterprise data integrity while making previously inaccessible business logic safely accessible to AI agents.

Enterprise Pagination and Bulk AI Access

With 15K+ expense records and similarly large datasets across domains, we engineered pagination and bulk API support directly into the AI agent capabilities, allowing enterprise AI agents to query efficiently without overwhelming context windows or sacrificing completeness of financial data.

Domain-Accurate Agentic Architecture

Each of the 62 AI agent capabilities was architected around real business logic across 14 ERP domains, with clear systems instructions guiding enterprise AI agents toward accurate, context-aware queries, turning payroll, pipeline, and AR data into safely accessible, production-grade Agentic AI infrastructure.

What began as a legacy ERP with an inaccessible schema is now a live, enterprise AI-native platform. Sixty-two agentic capabilities. Fourteen domains. Zero shortcuts: No custom SQL, no screen scraping, no compromise on data integrity. This is what happens when systems architecture precision meets real-world enterprise complexity: complex systems don't just get simpler, they become intelligent, secure, agentic infrastructure.

From Inaccessible to AI-Queryable

Whatever shape your data is in, we engineer the bridge that makes it safely queryable by the agents your team already uses.

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