AI Delivery Practice Build-Out at a Big-4 Consulting Firm — 2,400 Consultants Productive on the Platform

MindMap deployed an AI-delivery platform composed of Agentic Workflow Studio (Aw) as the consultant-facing workflow-authoring environment, Fine-Tuning Studio (Ft) for the per-customer fine-tuning capability, Sovereign LLM Platform (Sl) as the model-serving foundation, RAG Builder (Rg) for the per-engagement RAG patterns, and Model Benchmarker (Mb) for the per-engagement model evaluation.

Phase one was the platform-foundation build. The platform was deployed as a multi-tenant foundation that the firm's consulting workforce delivered against, with each consulting engagement instantiating its own tenant configuration without rebuilding the underlying infrastructure. The multi-tenancy was designed around the firm's per-customer data-handling commitments — each customer's engagement runs in its own isolated tenant with the appropriate access-controls and data-handling enforcement.

Phase two was the consultant-enablement programme. The firm's 2,400 AI consultants received structured training on the platform, with the training combining classroom sessions, hands-on labs and embedded MindMap engineers supporting the early consulting engagements. The training emphasis was on the consultant-facing developer-experience — the platform's productivity for the consultant rather than the underlying technical implementation.

Phase three was the engagement-pattern library. We worked with the firm's AI-practice leadership to develop a library of standard engagement patterns — common customer-engagement scenarios (LLM-driven document-processing, conversational-AI deployment, agentic-workflow automation, RAG-grounded knowledge-Q&A) packaged as starter patterns that consultants instantiate-and-customise rather than building from scratch.

Phase four was the continuous-improvement loop. The platform's usage telemetry across engagements feeds back into the platform's improvement programme — the patterns that engagements use most heavily receive the most ongoing investment, the patterns that engagements struggle with receive targeted improvements, the per-customer customisation patterns inform the platform's productisation roadmap.

The platform runs on the firm's preferred cloud environments (predominantly AWS and Azure depending on customer requirements) with per-customer tenant isolation enforced at the infrastructure level. The multi-tenant architecture supports the firm's per-customer data-handling commitments while providing the shared platform-foundation that the consulting workforce delivers against.

Sovereign LLM Platform serves the firm's foundation models across the model-portfolio — open-source models (Llama 3.1, Mistral, Qwen) for the customers requiring on-tenant fine-tuning, with model-serving in each customer's tenant for the customers with sovereign-deployment requirements and in shared per-region deployments for the customers without such requirements.

Fine-Tuning Studio handles the per-customer fine-tuning workflow — the consulting workforce uses the studio to fine-tune models on customer-specific data, with the fine-tuning happening inside the customer's tenant for the data-isolation requirements. The studio's workflow supports the consultant's fine-tuning work without requiring deep ML-engineering expertise.

Agentic Workflow Studio is the consultant-facing workflow-authoring environment. Consultants build per-engagement agentic workflows through the studio's low-code interface, with the workflow-pattern library accelerating the per-engagement build. The studio's output is deployable agentic workflows that run on the platform's execution layer.

RAG Builder handles the per-engagement RAG patterns — the consultant configures the customer's data sources, the indexing strategy, the retrieval logic and the response-generation pattern through the builder's structured interface, with the resulting RAG deployment running on the platform's infrastructure.

Model Benchmarker provides the per-engagement model evaluation — comparing candidate models on the customer's actual evaluation criteria, supporting the consultant's model-selection decisions with rigorous evaluation data.