Delivering an Enterprise AI Governance & Optimisation Platform Client Context
The Problem
Our client was a fast-growing AI governance and optimisation start-up serving mid-market and enterprise organisations adopting generative AI. They had a compelling vision: to give organisations visibility, control, and cost intelligence across their AI usage — spanning LLMs, infrastructure and internal applications.
However, they faced several critical challenges:
• Inability to move fast enough to meet rapidly growing market demand
• Lack of deep technical knowledge across multi-cloud AI routing, observability, data governance, and ML infrastructure
• Difficulty translating their product vision into a scalable, multi-service architecture
• Pressure from enterprise prospects demanding SSO, RBAC, audit logs, cost tracking, and advanced governance
• Limited internal capacity to build a platform touching backend services, frontend systems, high-performance routing, and ML-powered optimisation
The founders recognised they needed an expert engineering partner to accelerate delivery and bring their vision to life.
The Solution
We partnered with the client through a structured engagement designed to rapidly clarify scope, define architecture, and deliver a production-ready platform.
1. Discovery
We began with a comprehensive discovery phase focused on understanding product goals and diagnosing why internal progress had stalled.
Key insights:
• The internal team lacked the senior engineering depth to build a high-performance, multi-provider AI platform.
• Development velocity was too slow to support enterprise sales cycles.
• The architectural foundations were not yet designed in a scalable or future-proof way.
• Their vision required expertise across model routing, cloud infrastructure, data security, and observability — beyond the team’s in-house skills.
We reviewed their early technical concepts and shaped them into a clear, actionable roadmap.
2. Design
Next, we designed a complete architecture capable of supporting enterprise-grade AI operations.
This included:
• A multi-service microservices architecture across frontend, backend, ML services, routing components, and forecasting engines
• A high-performance AI request routing gateway capable of selecting optimal models across multiple providers based on cost, latency, and accuracy
• ML-powered prompt and model classification to optimise model selection
• Real-time AI usage tracking, token-level cost visibility, and multi-team segmentation
• Enterprise governance including SSO, RBAC, audit logs, encrypted key management, and policy enforcement
• Infrastructure monitoring foundations for GPU workloads, MFU analysis, runtime alerts, and cost overlays
We provided end-to-end architectural definitions, data flows, service boundaries and scaling strategies - removing uncertainty and giving the client a clear blueprint.
3. Delivery
We delivered the platform through a series of structured sprint cycles:
• Multi-disciplinary engineering squads across frontend, backend, DevOps, routing, and ML
• Weekly demos, daily collaboration, and rapid iteration
• Full implementation of cost analytics, routing logic, user management, billing, governance, and observability features
• Deployment pipelines, testing frameworks, and security controls
• Production hardening and load testing
• Client-led UAT before go-live
This approach allowed us to move far faster than the client’s existing team could have achieved alone.
The Success
The engagement materially transformed the client’s technical capability and market position.
Quantifiable Results
• 10 - 18 months of development time saved compared to hiring internally
• 30 - 45% reduction in AI inference costs for early adopters using routing optimisation
• Significant acceleration of enterprise readiness through governance and compliance features
• Dramatic increase in engineering velocity, enabling rapid iteration and new feature delivery
• High reliability and scalability through a robust multi-service architecture
Strategic Impact
• The client now has a fully production-ready platform built to enterprise standards
• They shifted from being slowed by technical constraints to leading a new category of AI optimisation and governance
• The founders were freed to focus on sales, fundraising, and ecosystem partnerships
• Their technical architecture now provides long-term differentiation through routing, observability, and governance capabilities
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