AI and integration architecture

Architect AI that connects to your systems of record and behaves predictably

Prototypes fail at the boundary with core systems. We design the integration, retrieval, agent and release architecture that lets generative and predictive models work with enterprise transactions safely, measurably and auditably.

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Three architecture services

From integration backbone to production AI

Integration

Enterprise integration architecture

API-first design, contracts and versioning
Event-driven backbones, including Apache Kafka
Hybrid-cloud middleware and gateways, including webMethods and comparable platforms
System-of-record integration: idempotency, outbox, CDC and reconciliation

AI systems

Production AI systems architecture

Agentic workflows with bounded tools, limits and human approval
Enterprise RAG with permission-aware retrieval and measured quality
LLMOps: versioned prompts, evaluation gates, observability, rollback
Guardrails, red-teaming and model evaluation

Modernisation

Legacy-to-AI core modernisation

Deterministic boundaries: models propose, validated services decide and write
Secure, auditable data flows between transaction engines and AI models
Phased roadmap that avoids a risky big-bang rewrite
Governance hooks that feed ISO/IEC 42001 evidence

Design principle

Models suggest. Deterministic systems commit.

Whenever AI output can change a financial, customer or operational record, we put validated, idempotent, logged services between the model and the system of record, with human approval where impact is high.

Layer	What we specify	What you receive
Systems of record	Access patterns, write paths, consistency and reconciliation	Integration contracts and failure-mode catalogue
Integration	API and event design, gateways, schema governance	Reference architecture and decision records
AI services	Retrieval, agents, model selection, evaluation, guardrails	Evaluation harness, release gates, LLMOps pipeline design
Governance	Controls, logging, oversight, incident paths	Evidence map aligned to your AIMS

Engagement model

Start with an assessment, then design, then build support

Architecture assessmentA fixed-scope review of one AI system or platform with findings ranked by risk and effort. See the assessment details.
Reference architectureTarget design, contracts, evaluation approach and roadmap your teams can build to.
Build supportDesign authority, reviews and coaching while your teams or partners deliver. Scope agreed in writing.

We provide architecture, design authority and enablement. Implementation scope is agreed per engagement; we do not accept open-ended delivery commitments without a defined scope. Capabilities of products named here change; we confirm against your versions.

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Talk to an architect about your AI programme

Tell us where you are. We reply by email with scoping questions and a clear next step. No obligation.

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