AI Integration

Deterministic processing always produces the same output for the same input: rules, calculations, data lookups, and transformations. Non-deterministic processing (AI/LLM inference) can produce different outputs each time because it involves probabilistic reasoning. Deterministic processing is predictable, auditable, and free per invocation. AI processing is flexible and powerful but variable and costs tokens. The best architectures use both: deterministic logic for the predictable parts, AI for the parts that genuinely require reasoning.

Every call to a language model consumes tokens (roughly 4 characters per token). You pay per token, with costs varying by model: GPT-4o costs significantly more per token than a smaller model like Phi. Token costs scale with usage, so architecture matters more than model choice. Strategies to control costs include: caching frequent responses, using smaller models for simple tasks, deterministic preprocessing to reduce prompt size, prompt optimisation to reduce token count, and batching requests where possible. We design for cost from the start.

MCP (Model Context Protocol) is an open standard that lets AI agents interact with external data and tools through a structured interface. Instead of pasting data into a prompt, an MCP server exposes your databases, APIs, and files as typed resources that an agent can query, filter, and act on. This gives you fine-grained access control, better data freshness, and cleaner separation between AI reasoning and data access. We build custom MCP servers for enterprise data sources.

Use RAG when your AI needs to answer questions about data that changes (documents, knowledge bases, databases). RAG retrieves relevant context at query time, so the AI always works with current information. Use fine-tuning when you need the model itself to behave differently (specialised vocabulary, consistent formatting, domain-specific reasoning patterns) and you have quality training data. For most enterprise use cases, RAG is the right starting point. We evaluate both during our proof of concept.

Yes. Our AI Search Accelerator is specifically designed to discover and ingest content from on-premise file shares, SQL databases, and legacy systems. We convert your data into semantic vectors and upload them to Azure AI Search for use with Azure OpenAI. Your raw data stays on-premise; only embeddings and metadata move to Azure.

All integrations use Azure-native security: private endpoints, managed identity, RBAC, and encryption in transit and at rest. MCP servers enforce resource-level permissions so agents only access data each user is authorised to see. We add application-level output validation, content safety filtering, and structured audit logging. Our ISO 27001 certification and Cyber Essentials Plus accreditation apply to all delivery.

AI integrations inherit your existing identity infrastructure. Service-to-service calls use Entra ID managed identities (no shared secrets). User-facing AI features authenticate through your existing OAuth 2.0/OIDC flows. MCP servers and RAG pipelines pass the user's identity context through the stack so that access controls are enforced at every layer, from the AI model down to the data source. The AI never sees data the requesting user would not normally have access to.

A focused RAG pipeline or MCP server integration starts at around £20,000. A full AI integration programme with multiple data sources, hybrid processing, and custom connectors ranges from £50,000 to £200,000+ depending on scope. We provide a detailed estimate after the data and integration assessment. See our pricing guide.