Enterprise Legacy System AI Integration Services UK

Ageing platforms often contain the transaction history, customer context, inventory signals and operational rules that AI needs, but those assets remain locked behind manual hand-offs, fragmented extracts and brittle interfaces. Connecting AI without understanding those constraints can create inaccurate outputs, duplicate work and unacceptable operational risk.

Salyant starts with the process and the P&L, not the model. We identify high-volume, repeatable or exception-heavy workflows, establish a baseline and define the control points required for a reliable business case. This turns AI from a technology experiment into a portfolio of prioritised operational improvements.

• Connect AI to systems of record without requiring a wholesale core-platform replacement.
• Prioritise processes where automation can improve cost-to-serve, throughput, working capital or control quality.
• Create an auditable link between each use case, data source, owner, control and financial benefit.

Our service covers the full integration lifecycle: system and process discovery, opportunity qualification, target architecture, data and API design, security review, pilot delivery, production hardening, monitoring and handover. We work alongside internal IT, data, security, finance and operations teams so the solution fits the existing operating model.

Deliverables may include a system map, data-flow diagram, integration specification, risk register, evaluation plan, tested connectors or adapters, workflow orchestration, dashboards, runbooks and a benefits-realisation plan. Salyant selects the appropriate AI service, model or automation pattern for the use case rather than prescribing a single platform.

• Discovery workshop and legacy-system inventory
• Use-case prioritisation and business-case model
• Target architecture and integration design
• API, event, file and workflow integration
• Data-quality, security and governance controls
• Pilot, production rollout and knowledge transfer

Step one is the business case. We define the process owner, current baseline, addressable volume, value hypothesis, implementation constraints, risk appetite and success metrics. This prevents a compelling demonstration from becoming an unfunded or unowned production obligation.

Step two is the API and integration strategy. We inventory supported interfaces, latency requirements, transaction boundaries, failure modes and observability options, then choose synchronous APIs, asynchronous events, secure batch exchange or a controlled adapter. Step three is data governance: ownership, classification, quality, lineage, retention, access and permitted AI use must be explicit before deployment.

• Business case: value, baseline, owner and success metrics
• API strategy: interface choice, resilience, security and observability
• Data governance: quality, lineage, privacy, retention and access
• Go/no-go gate before pilot and before scale

We preserve the system of record wherever it remains commercially and operationally sound. API-first integration is preferred when stable interfaces exist; event-driven patterns suit near-real-time workflows; secure file or batch exchange can be appropriate for mainframes and regulated data. Where no reliable interface exists, a controlled adapter or tightly scoped automation layer can bridge the gap without exposing the core platform to uncontrolled change.

Each pattern is designed with authentication, least-privilege access, encryption, idempotency, retries, rate limits, audit logging, fallback behaviour and rollback. The architecture also separates AI orchestration from fragile legacy logic so future platform changes do not force a complete automation rebuild.

• ERP and CRM integration through supported connectors or domain APIs
• Mainframe, database and secure file-transfer patterns
• Event streams and workflow orchestration for operational processes
• Document, email and exception-handling automation
• Tightly scoped RPA or adapter use only where stable interfaces are unavailable
• Parallel running, reconciliation and rollback plans

Legacy integration often surfaces personal, confidential or commercially sensitive data that was previously difficult to access at scale. We design data flows around minimisation, purpose limitation, classification, retention and role-based access, with UK GDPR and Data Protection Act 2018 requirements considered from the outset. A data protection impact assessment is included where the processing risk warrants one.

AI outputs are treated as operational decisions or recommendations that require appropriate validation. We define evaluation datasets, human approval thresholds, escalation routes, monitoring for drift and degradation, prompt and content controls, and change records. The control model follows the NIST AI Risk Management Framework and can be mapped to ISO/IEC 42001 and ISO/IEC 27001 practices.

• Data ownership, classification and quality rules
• Encryption, secrets management and network segmentation
• Audit trails, access reviews and retention policies
• Model evaluation, human oversight and incident response
• Vendor, hosting and data-residency due diligence

The roadmap is deliberately incremental. Discovery establishes the estate map and opportunity register; architecture defines the integration and control design; a pilot validates data, model behaviour, workflow fit and user adoption; production hardening adds resilience, security, monitoring and support; scaling extends the pattern to additional processes or sites.

Every phase has an explicit exit criterion and named decision owner. We use test data where appropriate, reconcile outputs against source systems, run parallel checks for critical processes and document rollback steps. This gives COOs and PE Operating Partners visibility over cost, risk and value before capital is committed to a wider rollout.

• Phase 1: discovery, baseline and opportunity qualification
• Phase 2: target architecture and controlled pilot
• Phase 3: production integration, testing and control sign-off
• Phase 4: scale, monitoring and benefits realisation
• Phase gates for security, data quality, usability and financial value

The value model connects each use case to a specific operational driver: labour capacity, cost per transaction, cycle time, first-pass yield, exception rate, service level, revenue leakage, inventory accuracy or working-capital release. We distinguish addressable value from realised benefit and identify whether savings come from reduced external spend, avoided overtime, redeployed capacity or improved cash conversion.

Benefits are measured against a documented baseline and reviewed with finance and process owners after go-live. This avoids inflated automation claims and creates a portfolio view that can be compared across business units, sites and PE portfolio companies.

• Quantify process cost, volume, variability and control exposure
• Separate one-off productivity gains from sustainable run-rate improvement
• Track operational KPIs and financial outcomes after deployment
• Prioritise quick wins alongside scalable platform capabilities

Production support is designed around the criticality of the integrated process, not just the AI component. Salyant can provide monitoring, incident response, data-quality checks, model and prompt maintenance, release management, access reviews, vendor coordination and periodic value reviews. Runbooks and ownership are transferred or retained according to the agreed operating model.

SLAs are documented in the statement of work and calibrated to support hours, severity definitions, hosting dependencies and internal escalation routes. Example targets can include P1 acknowledgement within one business hour, P2 acknowledgement within four business hours and P3 triage within one business day, with RTO, RPO, release windows and escalation contacts agreed before go-live.

• Severity-based incident response and escalation
• Monitoring for integrations, data quality and model performance
• Agreed support hours, RTO/RPO and change windows
• Monthly service review and quarterly value/control review
• Continuous improvement backlog and handover documentation