Agentic AI Platform for a Large Enterprise in Just 5 Months: Enabling Secure AI Adoption at Scale

Location

United Kingdom

Industry

Services

Overview

A UK-headquartered global financial group serving millions across banking, insurance, and open finance faced a common enterprise challenge. AI exploration was everywhere, but it lacked clear ownership, measurable ROI, and a path to scale:

Data scientists were working in fragmented environments to build and test.
Engineers spun up isolated AI tools with no shared standards.
Business units had no secure channel for large-scale experimentation.
Leadership saw token costs climbing without a clear ROI.

The result was a patchwork of initiatives, rising costs, and growing risk.

The Challenge

An internal performance audit by the client’s team uncovered a strategic challenge: widespread but ungoverned usage of AI tools. Teams were duplicating efforts, and model deployments lacked governance.

The audit confirmed what some leaders already suspected: AI was maturing faster than the infrastructure and policies designed to support it. Executives recognized the urgency: Without intervention, the organization risked cost overruns and stalled innovation.

The Solution

To ensure consistent, well-governed AI delivery across teams, the client needed a secure, standardized model for business and developer use. They engaged DataArt to create an internal AI platform that brought these initiatives together in a single, secure ecosystem.

This platform supported 73,000 users, giving controlled access to AI capabilities. Business users could query documents, extract insights, and automate information retrieval through a simple interface. Development teams gained a governed environment to safely experiment with LLMs, test use cases, and build prototypes without duplicating infrastructure. A custom memory optimization layer reduced token costs by avoiding redundant processing, and the entire platform was deployed inside the client’s infrastructure, eliminating external exposure.

With built-in auditability, OLAP integration, and readiness for enterprise systems, the client now has a scalable, secure foundation to support AI innovation across all functions, without sacrificing visibility, governance, or cost control.

Key Business Benefits

Cost Optimization: Custom memory management drastically reduced unnecessary LLM token consumption, lowering operating costs while maintaining performance.
Faster Decision-Making: Non-technical users can now securely upload documents, ask questions, and extract insights, reducing hours of manual work to minutes.
Enterprise-Grade Security: The platform operates entirely within the client's infrastructure, ensuring complete alignment with security policies and regulatory requirements.
Unified AI Governance: All interactions are traceable, auditable, and governed via centralized controls, addressing compliance, risk, and IT oversight needs.
Rapid Adoption with Low Friction: The intuitive UI and integrated backend allowed for rapid cross-team usage, with 3,500+ daily active users within weeks of release.
Future-Ready Infrastructure: The system integrates with enterprise knowledge repositories (e.g., Confluence) and includes an internal router layer for flexible model orchestration.

Tech Aspects of the Solution

Architecture

Backend: Python + FastAPI
Frontend: React.js

Databases

Postgres and VectorDB for chat history and document storage

Memory Management

Custom "shadow history" system to optimize LLM prompts and reduce token costs.

Security

Integration with OLAP, strict data access controls, and governance measures.

Model Routing

The platform includes an internal gateway acting as a secure LLM router, enabling model flexibility while maintaining strict data governance.

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