Design the interface.
Use APIs, MCP tools or event-driven integration where appropriate. Define inputs, outputs and failure behaviour.
AI integration with your business systems
Connect your systems. Control each action.
Give staff and AI workflows access to the information they need, without rekeying it between systems. We connect documents, records and applications through interfaces that validate inputs and enforce access limits.
Talk to an engineer Start with a scoped reviewEngineering focus
01Validated request
02Authorised tool
03Checked result
Possible applicationManufacturing & steel
Match an order with stock records and supporting material certificates. Flag missing or conflicting information and prepare an update for approval. Explore this sector.
Reading and changing need different controls.
Reading a record, drafting an update and committing it are separate operations. A model can request a tool call; the application checks whether the action is authorised and validates what it is about to change.
The Model Context Protocol (MCP) connects AI applications to tools and data. Agent2Agent (A2A) supports coordination between agents. Each connection needs a defined identity, permissions and responsibility for the result.
Scope the access.
Authenticate requests, minimise privileges and keep credentials out of prompts. Treat tool output as data, not new authority.
Make changes traceable.
Record the request, approval and resulting action. Handle timeouts, duplicates and partial completion explicitly.
A protocol is not a security guarantee. Implement and test consent, authorisation, credential handling and network restrictions.
Model orchestration
The model is a component. Keep the architecture yours.
Choose models for task quality, response time, cost and data requirements. Make routing and fallback explicit, testable decisions.
Compare on the task.
Test candidate models against your own examples. Include difficult cases, cost per completed task and response time.
Engineer the context.
Define what the model can see: instructions, approved information, available tools and task state. Control what it remembers and carries forward. Version the configuration so changes can be traced.
Plan the fallback.
Restrict allowed providers and regions. A failure should not reroute sensitive information to an unapproved destination.
Private & hybrid AI
Choose where it runs. Control where data goes.
Match deployment to the workload. Treat model serving, access controls, networking and monitoring as parts of one system.
Choose the placement.
Compare local and hosted options against quality, latency, capacity and cost. Check provider terms, supported regions and data handling.
Enforce the boundary.
Control outbound connections, isolate workloads and protect credentials. Make allowed model routes explicit; do not use an unrestricted fallback.
Operate the platform.
Plan upgrades, capacity, recovery and observability. Local inference adds infrastructure responsibilities as well as deployment options.
Enterprise knowledge & RAG
Your information. Answers with evidence.
Help people find the right procedure, policy or record without searching several systems. Preserve source permissions and versions, show the evidence and make gaps in the information visible.
Prepare the knowledge.
Extract the text and structure needed for the task, retain source and version information, and assign ownership. Plan how updates and deletions reach the search index.
Find relevant evidence.
Combine metadata, keyword and semantic retrieval where useful. Test chunking, reranking and access filters on real questions.
Show the limits.
Attach source references, handle missing information and surface contradictions. Test retrieval separately from the generated answer.
Multimodal knowledge
The answer is not always in a document.
Search across text, drawings, images and recordings where the task calls for it. Keep references back to the source and apply its access rules to the results.
Prepare each source.
Extract useful structure, preserve versions and record timestamps or page locations. Keep links back to the original material.
Retrieve across formats.
Combine suitable multimodal embeddings, metadata and text search. Test whether results match the actual question, not just the general topic.
Keep evidence visible.
Carry access rules into retrieval. Show the source and make uncertainty clear when the material is missing, outdated or contradictory.
A closer look
Good questions. Straight answers.
What does MCP add?
MCP gives AI applications a standard interface to tools and data. A2A addresses a different boundary: coordination between agents. Authentication, authorisation and safe handling of returned content still need to be implemented and tested.
What about software without an API?
Controlled computer-use workflows may be an option after assessing the interface and failure modes.
Can we avoid dependence on one provider?
Designing clear interfaces can reduce unnecessary coupling. Provider features, terms and model behaviour still differ.
Technical reference: MCP: authorisation specification, July 2026 (opens in a new tab)
Technical reference: Linux Foundation: A2A stable protocol, April 2026 (opens in a new tab)
Technical reference: MCP: security best practices (opens in a new tab)
Technical reference: NVIDIA: runtime isolation and policy (opens in a new tab)
The first step
Map one path through the systems.
Start with one workflow and the systems it needs to connect. We scope a review of the interfaces, data ownership and permissions, including what should happen when a request fails or arrives twice.
What you get. An integration plan covering the agreed interfaces, access boundaries, validation and handling of failures or incomplete work.
Talk to an engineer