Enterprise AI Infrastructure & Legacy Integration
AI is only as capable as the data it can reach. We architect secure integration layers that let agents and teams read and write across isolated legacy platforms — built on proven Azure patterns for reliability.
01 / problem
Intelligent systems are useless on fragmented data
Most established organisations run a patchwork: a CRM here, an ERP there, bespoke tools built a decade ago, SaaS platforms adopted department by department. Each holds part of the truth; none holds all of it.
Point-to-point integrations accumulate into brittle spaghetti that nobody fully understands. Adding AI on top of that foundation produces confident-sounding answers built on incomplete data.
The durable fix is an integration layer: decoupled messaging, canonical data flows and managed APIs that both humans and agents can use safely.
02 / delivered
What we deliver
- An integration architecture covering message flows, data contracts, API surfaces and ownership boundaries.
- Decoupled messaging using Azure Service Bus for reliable, ordered, retryable communication between systems.
- Data persistence and synchronisation patterns on Cosmos DB and relational stores, matched to each workload.
- Serverless processing with Azure Function Apps and Python workers for transformation, enrichment and ETL.
- Managed API gateways so agents and applications access systems through governed, logged endpoints.
- Monitoring, alerting and operational runbooks for the whole integration estate.
03 / use cases
Common integration programmes
System synchronisation
CRM, finance, HR and service platforms kept consistent through event-driven flows — no more nightly CSV exports and manual reconciliation.
Agent data access
Agents given read/write reach into legacy systems through governed APIs and queues — capability without direct database access.
Real-time pipelines
High-volume transactional and operational data streamed, transformed and routed in near real time.
04 / controls
Controls engineered in
- All cross-system access flows through managed, authenticated endpoints — nothing reaches around the layer.
- Messages are persisted and replayable, so failures are recoverable rather than silent.
- Every read and write is logged with actor, timestamp and purpose for audit.
- Schema and contract validation at boundaries prevents bad data propagating downstream.
05 / process
How an engagement runs
01
Map the estate
We catalogue systems, data flows and ownership to find where integration effort pays back.
02
Design the layer
Messaging, storage and API patterns are specified against your availability and security requirements.
03
Build incrementally
Flows are delivered in vertical slices that prove value early rather than a year of plumbing.
04
Operate & extend
Monitoring and documentation hand the estate to your team, with patterns ready to reuse.
06 / evidence
Related delivery snapshot
See the Azure Service Bus architecture synchronising Salesforce, SITS and Canvas in near real time for the Global Digital Campus programme — the integration layer agentic systems stand on.
Read the University of London case studyGive your AI something reliable to stand on
An architecture review maps your current integration estate and shows what a governed layer would unlock — for AI and for the teams who depend on that data today.
Request an architecture review