Availability, latency and throughput against agreed targets.
Continuous evaluation against golden sets, with drift alerting.
Guardrails, escalations, overrides and policy exceptions monitored.
Model routing, caching and per-workflow cost ceilings.
Every run traced and retained, ready for review at any time.
Accuracy up, cost down, measured against the original baseline.
Agents that are deployed and never evaluated degrade quietly.
Traditional application support asks whether the system is up. For AI that is the wrong question. A system can be perfectly available and quietly wrong.
Nothing errors. Outputs just get worse. Without continuous evaluation against a golden set, the first signal is a customer complaint or an audit finding.
A source system changes a field, a document template is updated, a new product line appears. Retrieval quietly starts returning the wrong context.
Volume grows, context windows expand, a model is deprecated and traffic shifts to a pricier one. Nobody sees it until finance asks about the bill.
New use cases get added, permissions widen, human checkpoints get skipped for speed. By the time audit looks, the controls no longer match the policy.
Our Enterprise AI Managed Services
Sold as one service. You do not get monitoring without someone accountable for acting on what it shows.
AgentOps Services
The operating discipline for agentic systems. Release management, versioning, prompt and tool governance, regression gates, incident response and rollback for agents that take actions inside your business.
Included- Controlled release process for agent and prompt changes
- Version history with the ability to roll back a bad release
- Tool and permission change control
- On-call and incident management
- Post-incident review and corrective action
AI Application Managed Services & Support
Day-to-day operational support for the applications, agents, integrations and interfaces in production. Users raise issues, we triage, fix and close them against agreed response times.
Included- Ticket triage and resolution against agreed targets
- Integration and pipeline failure handling
- Minor enhancements and configuration changes
- Environment, release and dependency management
- Runbook maintenance and knowledge transfer
AI Model Monitoring & Evaluation Services
Continuous evaluation against golden sets, not periodic spot checks. We know what accuracy is today, we know when it moves, and we alert before your users discover it.
Included- Scheduled evaluation runs against maintained golden sets
- Accuracy, latency and escalation-rate monitoring
- Data and retrieval drift detection
- Full run tracing, searchable at transaction level
- Alerting thresholds agreed with your team
AI Performance & Cost Optimization Services
Run cost is our problem, not a line item you discover later. Model routing, right-sizing, caching, retrieval tuning and prompt efficiency, reported per workflow.
Included- Cost per workflow and per transaction, reported monthly
- Model routing and right-sizing recommendations
- Semantic caching and retrieval optimisation
- Latency and throughput tuning
- Budget ceilings and anomaly alerting
AI Governance Managed Services
Governance does not stop at go-live. Agent identity and access reviews, policy exception tracking, audit readiness, and a standing improvement backlog measured against the original baseline.
Included- Periodic agent identity and entitlement review
- Policy exception log and remediation
- Audit-ready evidence pack on request
- Human oversight and override analysis
- Quarterly value report against baseline
Decades Of Data & AI Delivery
The scale and experience behind every engagement, from strategy through production.
Uptime is the easy part. We commit to quality too.
Most managed service agreements only measure availability. For AI that misses the failure mode that actually matters.
Specific targets are agreed per workflow based on risk tier, volume and business criticality.
Three levels of cover.
Chosen by how much the workflow matters, not by how much you want to spend.
For internal or low-risk workflows where a problem is inconvenient rather than damaging.
- Availability and error monitoring
- Scheduled evaluation runs
- Business-hours support
- Monthly quality and cost report
- Quarterly service review
For customer-facing or revenue-affecting workflows that need someone actively responsible.
- Everything in Monitored
- Continuous evaluation with drift alerting
- Extended-hours support and incident response
- Active cost and performance optimisation
- Release management and rollback
- Quarterly value report against baseline
For workflows carrying regulatory weight, financial consequence or audit exposure.
- Everything in Managed
- 24x7 coverage and severity-one response
- Access and entitlement review cycle
- Policy exception tracking and remediation
- Audit evidence pack on demand
- Named service manager
We will run AI we did not build.
A four-week onboarding that starts with an honest assessment. If the system is not safe to operate as built, you will hear that before anything is signed.
AI Health Check
Review the system as it standsArchitecture, evaluation coverage, permission model, observability, run cost and governance posture reviewed against production standards.
Week 1Findings & Risks
Say what is safe and what is notWhat is safe, what is fragile and what is unacceptable, with severity, effort and a recommendation against each item.
Week 2Stabilise
Close the critical gaps firstClose the critical gaps: evaluation harness, tracing, alerting, access controls and cost ceilings, whichever are missing.
Week 2 – 4Operate
Move to steady-state operationTransition to steady-state operation against agreed service levels, with a baseline established so improvement is measurable.
Week 4 onwardThree audiences. Three different reports.
The same operational data, presented for the person who has to act on it.
Whether the workflow is delivering what it was funded to deliver.
- Volume handled
- Straight-through rate
- Cycle time
- Escalation volume
- Value against baseline
System health, quality trend and where the engineering effort is going.
- Availability and latency
- Evaluation scores over time
- Incidents and root causes
- Release history
- Optimisation backlog
Evidence the system behaved as approved, and what it cost to run.
- Agent action audit trail
- Policy exceptions raised
- Human overrides
- Access review results
- Cost per transaction
Consulting, development, or managed services?
Most enterprises need two of the three. Here is how to tell which conversation to start.
| AI Consulting | AI Development | AI Managed Services | |
|---|---|---|---|
| The question it answers | What should we build, and is it worth it? | How do we build it so it survives production? | Who keeps it working and improving? |
| Start here if | You have ideas but no agreed priorities or business case | You know the workflow and need it delivered | Something is already live and degrading or unmonitored |
| Typical duration | 4 to 8 weeks | 8 to 16 weeks to first production release | Ongoing |
| Main output | Roadmap, architecture, governance, business case | A working solution integrated with your systems | Uptime, quality, cost control and continuous improvement |
| Learn more | AI Consulting → | AI Development → | You are here |
We run AI because we build AI.
Our managed AI solutions connect technical service levels with workflow quality, operating cost, and business outcomes.
Discuss Your AI OperationsThe people operating your system are engineers who build production AI, so a fix is a fix rather than a ticket passed upstream.
We commit to accuracy against a maintained golden set, because availability alone does not tell you whether the system is still right.
Cost per transaction is reported monthly and reducing it is part of the service, not a separate consulting engagement.
Access reviews, policy exception tracking and audit evidence continue for as long as the system runs.
The IX Value Engine keeps reporting against the original baseline, so the business case does not quietly disappear after year one.
Real Outcomes, Delivered at Scale
See how we help enterprises apply AI to improve operations, decision-making and customer experiences.
Transforming Dealer & Customer Support with AI
An AI-powered knowledge platform streamlined dealer and customer support by delivering faster, context-aware responses and reducing manual effort.
Response
AI Virtual CFO Platform for Smarter Financial Decisions
A multi-tenant AI platform consolidated financial data, automated reporting, and delivered advisor-level insights for thousands of concurrent users.
Reporting
From Customer Requirements to Engineering-Ready Designs
An AI-powered platform converted customer requirements into engineering-ready design inputs, reducing design iterations and accelerating production readiness.
Iterations
Decision Intelligence Agents for Financial Institutions
A unified AI finance engine integrated ERP and credit models to deliver real-time liquidity insights and automated working capital decisions.
Decisioning
Managed AI Services Across Key Industries.
Our AI Operations Services reflect the workflows, systems, risk requirements, and operational priorities of each industry.
Support AI across commercial operations, dealer networks, supply chains, engineering, production, quality, and service workflows.
Explore Manufacturing Financial ServicesMonitor and govern AI applications supporting banking, lending, insurance, wealth management, risk, compliance, and customer operations.
Explore Financial Services Healthcare and Life SciencesOperate AI systems with appropriate data controls, evaluation, security, oversight, and workflow support.
Explore Healthcare Retail and E-CommerceSupport AI applications across merchandising, personalization, pricing, inventory, fulfillment, and customer experience.
Explore Retail Consumer GoodsManage AI solutions supporting demand planning, product operations, supply chains, sales execution, and customer engagement.
Explore Consumer Goods Travel, Transportation and LogisticsMonitor AI across planning, logistics, asset utilization, service delivery, and customer support workflows.
Explore Transportation Energy and UtilitiesSupport governed AI systems used for forecasting, asset management, field operations, compliance, and customer service.
Explore Energy Media and EntertainmentOperate AI applications supporting content workflows, enterprise knowledge, audience engagement, and automation.
Explore MediaRecognitions and Awards
Our enterprise AI, data, and technology work has been recognized by independent industry organizations and technology communities.
AI Managed Services Questions
A partner that operates enterprise AI systems in production: keeping them available, monitoring whether outputs are still accurate, responding to incidents, controlling run cost, maintaining governance evidence and continuously improving performance against an agreed baseline.
Traditional managed services measure whether the system is up. AI can be fully available and quietly wrong. This service adds continuous evaluation against golden sets, drift detection, model and prompt release management, guardrail monitoring and run cost control, none of which exist in conventional application support.
Yes. We start with a four-week onboarding: a health check in week one, findings and risks in week two, stabilisation of critical gaps in weeks two to four, then steady-state operation. If the system is not safe to operate as built, we say so before anything is signed.
The operating discipline for agentic systems: release management and versioning for agents, prompts and tools, regression gates before changes go live, incident response for agents taking actions, and rollback when a release makes things worse. It is what MLOps and LLMOps become once the system can act rather than only answer.
Availability, output quality against a maintained golden set, severity-based response and resolution times, escalation rate, cost per transaction and audit readiness. Specific targets are agreed per workflow based on risk tier, volume and business criticality.
Cost per workflow and per transaction are reported monthly. Optimisation through model routing, right-sizing, semantic caching, retrieval tuning and prompt efficiency is part of the service, with budget ceilings and anomaly alerting in place.
Scheduled evaluation runs against maintained golden sets, plus monitoring of escalation rate, override rate and retrieval quality. A rising escalation rate is usually the earliest signal that something upstream has changed.
Access is scoped to what the service requires and operates under your identity provider, your approval process and your security controls. You retain ownership of data, agents, permissions and deployment choices throughout.
You get the runbooks, evaluation sets, architecture documentation and operating history. The service is designed so you are not locked in by ignorance, and transition assistance is part of the agreement.
No. A significant part of this service is taking over AI systems built by others that are live but under-monitored, degrading, or too expensive to run. In most of those cases the problem is evaluation, integration or governance rather than the model.