Unclear Ownership & Inventory
Teams may know that an AI application exists without a current record of its owner, model, prompt, retrieval sources, tools, vendor dependencies or approval state.
Managed Services · AI Operations
AI Managed Services for Reliable, Governed Production AI
Move beyond launch-day success. DataConsultant can help operate selected production AI systems through defined ownership, monitoring, evaluation, incident and change processes, evidence, reporting and continual improvement—without assuming unsupported uptime or response commitments.
AI system inventory, ownership and configuration visibility
Quality, grounding, safety and control evaluation where applicable
Incident, request, problem and controlled-change workflows
Operational reporting, evidence and improvement backlog
Service hours, service levels, response objectives, roles and platform boundaries are confirmed during scoping and documented in the agreed operating model.
System inventoryOwner & dependency mapped
Evaluation baselineTests & thresholds agreed
Issue workflowTriage & escalation defined
Change controlRe-test before release
Operational signals
Output quality checksBaseline set
Grounding & retrieval checksMonitored
Vendor/model change watchReview due
Control evidenceOwner assigned
Managed workstreams
MonitorSignals, drift, performance
EvaluateQuality, safety, grounding
OperateIncidents, requests, changes
ImproveBacklog, evidence, controls
Know What Is Running
Maintain a current operational view of systems, owners, versions, dependencies and control scope.
Detect Material Change
Use telemetry, evaluation and change signals to identify issues that need triage or re-testing.
Operate With Evidence
Connect controls, tests, incidents, approvals and operational decisions to a defensible evidence trail.
Improve Continuously
Prioritise recurring defects, control gaps, quality issues, cost drivers and operational improvements.
1
Why Production AI Needs an Operating Model, Not Just Monitoring
Production AI changes through data, prompts, models, retrieval sources, tools, vendors, user behaviour and policy decisions. Managed operations provide the repeatable ownership and evidence needed to act on those changes.
Quality Changes After Release
Grounding, output quality, safety, latency and user behaviour can change after go-live even when application code appears unchanged.
Incidents Without Evidence
When outputs fail or controls are challenged, teams need logs, test results, approvals, change history and escalation records to understand what happened.
RAG & Data Dependencies Move
Source freshness, access, chunking, indexing, metadata and upstream quality can affect AI behaviour and may need operational ownership beyond the model itself.
Model & Vendor Changes Accumulate
Provider releases, model retirement, configuration changes and new capabilities can alter cost, risk or behaviour and should trigger an appropriate review path.
Cost and Capacity Drift
Usage, token consumption, model selection, evaluation overhead and platform demand can change materially as adoption grows, requiring visibility and prioritisation.
Turn AI Production Risk Into an Operable Service
Start with the systems, owners, support boundaries and operational outcomes that matter most. We can shape the right monitoring, evaluation and service-management coverage around that scope.
2
What AI Managed Means in Practice
The service combines AI-specific technical operations with service management, governance and evidence. It is designed around explicit responsibility boundaries rather than a generic “we manage everything” promise.
Ongoing Operations Around Selected Production AI
DataConsultant can take responsibility for agreed operational tasks across production AI systems and their assurance lifecycle. Coverage may span monitoring, evaluation, incident and request handling, controlled change, operational evidence, governance reporting and improvement planning.
Service scopeNamed systems, environments, processes and operational responsibilities.
Operational baselineInventory, ownership, evaluation, controls, runbooks and monitoring state.
Decision rightsWho can approve change, accept risk, release, escalate and remediate.
EvidenceRecords that link observations, tests, incidents, decisions and changes.
3
AI Managed Operating Model: From Transition to Continual Improvement
A repeatable service flow gives product, engineering, risk and business teams a shared way to understand what is monitored, who acts, what requires approval and how evidence is retained.
4
AI Managed Service Scope and Operational Capabilities
Choose the capability depth that matches system criticality, existing internal ownership and the evidence your governance process needs. Not every system requires the same managed coverage.
AI Inventory & Ownership
- System and use-case register
- Owner and support mapping
- Model, prompt and tool configuration
- Vendor and dependency record
Monitoring & Observability
- Operational telemetry
- Performance and latency signals
- Drift or change indicators where supportable
- Monitoring baseline and exceptions
Evaluation & Output Quality
- Evaluation suites and datasets
- Grounding and citation checks
- Quality and safety criteria
- Human review where required
RAG & AI Data Operations
- Retrieval-source dependencies
- Freshness and access checks
- Indexing/ingestion coordination
- Grounding evidence and issue routing
Incidents, Requests & Problems
- Intake and categorisation
- Evidence-led triage
- Escalation and ownership
- Root-cause and recurring-issue backlog
Change & Release Control
- Model and vendor updates
- Prompt, tool and agent changes
- Impact assessment and re-test
- Approval and release evidence
Governance & Control Evidence
- Control monitoring
- Risk and exception registers
- Decision and approval records
- Governance reporting cadence
Cost, Capacity & Reporting
- Usage and cost visibility
- Service trend reporting
- Operational review packs
- Prioritised improvement backlog
5
Operational Deliverables You Can Govern and Hand Over
The service is designed to leave a clear operational record—not only dashboards. Deliverables are tailored to the systems, controls and responsibilities actually in scope.
01
Service Definition
Scope, boundaries, roles, support model, intake, escalation and governance cadence.
02
AI System Inventory
Current systems, owners, models, prompts, data, tools, vendors and dependencies.
03
Monitoring Baseline
Defined signals, thresholds, evaluation methods, known limitations and ownership.
04
Operational Runbooks
Repeatable procedures for alerts, triage, requests, change, evidence and escalation.
05
Issue Register
Incidents, defects, control exceptions, root causes, ownership and remediation status.
06
Evaluation Evidence
Test scope, results, exceptions, trends and decision context for agreed AI behaviours.
07
Change Records
Impact assessment, approvals, re-test evidence, release notes and follow-up monitoring.
08
Service Reporting
Operational trends, evaluation results, issues, risks, costs and improvement priorities.
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Improvement Backlog
Prioritised reliability, quality, control, automation, performance and cost improvements.
10
Transition Pack
Current-state knowledge, open issues, dependencies, records and handover material.
Define the Right Managed Coverage Before You Commit
Map the AI systems, operating pain points, internal responsibilities, evaluation needs and governance evidence first. That creates a service scope that is defensible and commercially clearer.
6
AI Operations Scorecard: What the Service Can Track
Metrics should reflect the system’s actual purpose and risk. The managed service can maintain agreed measures and evidence without inventing universal pass marks for every AI application.
| Dimension | Operational question | Possible evidence | Managed treatment |
|---|---|---|---|
| Reliability | Is the AI-enabled service functioning as intended in production? | Availability signals, errors, dependency events, run history | Monitor & triage |
| Output quality | Are responses or predictions meeting defined business criteria? | Evaluation results, sampled review, defect trends | Evaluate & improve |
| Grounding | Are RAG outputs supported by the expected sources? | Citation checks, retrieval traces, source freshness | Test & investigate |
| Safety & security | Are defined misuse, leakage, injection or harmful-output controls working? | Control tests, security findings, blocked/escaped cases | Escalate exceptions |
| Performance | Are latency, throughput and dependent services within agreed operating expectations? | Telemetry, traces, service reports | Monitor & optimise |
| Cost & usage | Is consumption changing in a way that requires action or redesign? | Usage reports, model mix, token/API/cloud consumption | Review & prioritise |
| Incident health | Are recurring incidents, defects or requests being reduced? | Incident trends, root-cause records, backlog | Problem management |
| Control readiness | Can owners show current evidence for agreed AI controls and decisions? | Approvals, tests, exceptions, change history, control evidence | Evidence & review |
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Technical Assurance Architecture for Managed AI Operations
AI operations need visibility across the complete service path—from input and retrieval dependencies through model behaviour, guardrails, evaluation and production telemetry.
Inputs & Data
User input, enterprise data, context and data policy.
AI Application
Prompt logic, workflow, agent behaviour and application code.
Model Layer
Model/provider choice, version, parameters and orchestration.
Retrieval & Tools
RAG sources, APIs, tools, functions and external dependencies.
Output
Response, action, structured output, citation or downstream effect.
Evaluation & Controls
Quality, grounding, safety, policy and risk checks.
Telemetry
Traces, outcomes, performance, drift and production signals.
Service Management
Incidents, requests, change, evidence, reporting and improvement.
Cross-cutting: access control · privacy · security · versioning · traceability · cost visibility · human oversight · issue management
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Governance and Decision Rights for AI Managed Operations
A managed service should not blur accountability. Product, risk, security, engineering and service teams need explicit authority for approvals, risk acceptance, release, escalation and evidence.
AI Managed
Operations
Operations
Business / Product OwnerUse case, value, business acceptance
AI / Model OwnerModel behaviour, evaluation, model decisions
Risk / ComplianceRisk treatment, exceptions, regulatory context
Security / PrivacySecurity, data protection and access controls
Data / Platform EngineeringData, retrieval, integrations and platform dependencies
Service OperationsMonitoring, triage, runbooks, evidence and reporting
Decision Rights to Agree Before Go-Live
RACI alone is not enough. The operating model should state who can make consequential decisions and which evidence is required.
Approve releaseWho accepts the evidence for a production change?
Accept riskWho can approve an exception or residual risk?
Pause or restrictWho can limit service behaviour when risk or quality crosses a threshold?
Change model/vendorWhat review is required for a model or provider change?
Close an incidentWhat evidence supports closure and recurrence prevention?
Prioritise remediationHow are quality, control, security, cost and reliability work ranked?
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Risk → Control → Test → Evidence Map
For higher-risk systems, operational evidence should connect the issue being managed to the control, test method, owner and resulting record.
| Operational risk | Control approach | Test / check | Evidence retained |
|---|---|---|---|
| Ungrounded outputs | Approved retrieval sources and grounding criteria | Grounding/citation evaluation | Evaluation results and exceptions |
| Prompt injection / misuse | Input filtering, tool restrictions and security controls | Adversarial checks where applicable | Test records, incidents and remediation |
| Sensitive-data exposure | Access, redaction, data and privacy controls | Leakage and permission checks | Security/privacy evidence and issue record |
| Model or vendor change | Version control and change approval | Impact-based regression evaluation | Change record, approval and test pack |
| Quality degradation | Evaluation baseline and review threshold | Periodic or event-triggered evaluation | Trend report, defects and improvement actions |
| Cost escalation | Usage visibility and model-selection governance | Consumption and demand review | Cost trend, decision record and backlog item |
Make AI Operations Defensible With Traceable Evidence
Connect monitoring, tests, controls, approvals, incidents and changes so operational decisions can be reviewed by product, risk, security and audit stakeholders.
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Continuous Assurance and Improvement in Production
Managed operations should turn production evidence into action. Changes, incidents and evaluation results can feed a controlled improvement cycle rather than remain isolated tickets.
Monitor
Track agreed production signals and service conditions.
Detect
Identify material quality, risk, performance or cost change.
Investigate
Analyse evidence, dependencies and probable root cause.
Remediate
Fix issues, controls, prompts, data or operating procedures.
Re-test
Validate the intended improvement before or after release.
Approve & Release
Use agreed decision rights for material production change.
Update Evidence
Refresh baseline, runbooks, records and improvement backlog.
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Reference Frameworks That Can Inform the Managed Control Model
Frameworks help structure risk and evidence, but the service remains requirements-led. Applicability depends on your organisation, system and regulatory context.
NIST AI Risk Management Framework
NIST AI RMF can provide a voluntary risk-management structure for governing, mapping, measuring and managing AI risk across the lifecycle.
View NIST AI RMF →ISO/IEC 42001
ISO/IEC 42001 provides an AI management-system structure that includes governance, risk, performance evaluation and continual improvement considerations.
View ISO/IEC 42001 →OWASP GenAI Security Guidance
For LLM and GenAI services, OWASP guidance can inform threat-led testing and control review for issues such as prompt injection, data exposure and unsafe tool use.
View OWASP GenAI guidance →12
Custom Scope and Pricing for AI Managed Operations
DataConsultant does not publish a fixed fee for this AI Managed service. Pricing should follow the actual production estate, support boundaries, assurance depth and operating responsibilities.
Request a Scoped Proposal
Custom pricing based on scopeA proposal can define the systems covered, service window, operating procedures, reporting cadence, transition work, responsibilities, assumptions and any agreed service objectives.
- Number and criticality of AI systems
- Production environments and regions
- Support window and stakeholder coverage
- Evaluation frequency and test depth
- Models, vendors, agents and tool integrations
- RAG and data-operations responsibilities
- Monitoring and observability tooling
- Security, privacy and governance requirements
- Incident, request and change process
- Reporting, evidence and audit-readiness needs
- Transition and knowledge-transfer effort
- Onsite or specialist support requirements
Public India-Market Guidance for Comparable Managed AI Support
Approx. ₹2.5 lakh/month entry · ₹6–13 lakh/month broader coverageCurrent public India-facing examples for managed AI operations and support show entry-level production coverage around ₹2.5 lakh per month, while broader multi-system monitoring, evaluation, quality and operational-support scopes can be around ₹6–13 lakh per month. Large enterprise scopes are commonly custom.
Important: This is external market guidance for scoping only, not an official published DataConsultant fee and not a commitment to a specific staffing level, SLA, response time or uptime. Comparable services vary materially in systems covered, service hours, evaluation depth, cloud consumption and transition effort.
Third-party model, cloud, observability, software and licence consumption should be treated separately from consulting or managed-service fees unless explicitly included in the proposal.
13
When AI Managed Is the Right Next Step—and When It Is Not
The service is strongest when there is a real production operating responsibility to own. A different engagement may be more efficient if the need is primarily strategy, build, one-off assessment or major remediation.
Good fit for AI Managed
- You have production AI systems that need sustained operational ownership.
- Monitoring exists, but incidents, evaluation, change and evidence are fragmented.
- Internal teams need help operating a repeatable governance and assurance cadence.
- Model, vendor, RAG or agent changes require controlled review and re-testing.
- Leadership needs clear service reporting, risk visibility and an improvement backlog.
Consider another service first
- You are still deciding whether an AI use case is viable or valuable.
- You need a new AI strategy, architecture or product build rather than ongoing operations.
- The immediate need is a one-off independent assessment or assurance review.
- A major data/platform failure must be remediated before stable operations can begin.
- You need legal advice, statutory audit or certification rather than managed operations.
Build a Commercial Model Around the Systems You Actually Need Managed
Share the production estate, support window, evaluation depth, governance needs and existing operating model. We can use that to shape a scope and quote without inventing generic service tiers.
14
Why DataConsultant for AI Managed Operations
The value is in joining AI-specific evaluation and controls with the practical mechanics of production operations, data dependencies, reporting and handover.
Architecture-to-Operations Continuity
Manage the full service path across application, model, retrieval, tools, data, controls and operational dependencies rather than treating the model as an isolated component.
Governance by Design
Make ownership, decision rights, controls, evidence and escalation part of normal service operations instead of a separate compliance exercise after incidents occur.
Evaluation-Aware Operations
Connect production monitoring with quality, grounding, safety and risk evaluation where those checks can be defined and measured reliably for the use case.
Data and RAG Context
Recognise that many AI incidents originate in data freshness, retrieval, access, metadata or upstream platform dependencies—not only in the model itself.
Controlled Change
Use impact assessment, re-testing, approvals and post-change monitoring to manage evolving models, prompts, agents, vendors and retrieval components.
Knowledge Retention and Transition
Maintain inventories, runbooks, issue history, evidence and open actions so the service can be governed, improved and transitioned without losing operating knowledge.
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AI Managed Service FAQs
Answers to common enterprise questions about scope, support boundaries, evaluation, RAG operations, changes, governance, deliverables, pricing, transition and handover.
What is AI Managed?
AI Managed is an ongoing operational service for production AI systems, including GenAI, LLM, RAG, machine-learning and agent-based applications. The scope can cover inventory, monitoring, evaluation, incident and request handling, controlled change, governance evidence, operational reporting and continual improvement. Exact responsibilities are defined during service scoping.
What can DataConsultant operate within an AI Managed engagement?
Scope can include selected AI applications, models, prompts, retrieval components, agent workflows, evaluation suites, telemetry, control checks, operational evidence and service-management processes. Ownership boundaries with internal teams, cloud providers, software vendors and systems integrators are agreed before transition.
Does AI Managed include 24/7 support or a guaranteed uptime?
No support window, response target, staffing level or uptime commitment is assumed. Any service hours, escalation paths, response objectives or availability commitments must be explicitly agreed in the scoped proposal and operating model.
How are AI quality, hallucination and grounding issues monitored?
Where relevant, the service can use agreed evaluation datasets, outcome checks, grounding and citation checks, policy tests, human review, production telemetry and issue thresholds. The exact evaluation method depends on the application, risk level, available evidence and whether outputs can be assessed reliably.
Can you manage RAG and AI data operations as part of the service?
Yes, when included in scope. Managed activities can cover retrieval-source changes, indexing or ingestion dependencies, access controls, grounding evidence, data-quality checks, freshness concerns and incident coordination. Underlying data-platform engineering or large-scale remediation may require a separately scoped service.
How do you handle model, prompt, agent and vendor changes?
Changes can be routed through an agreed change process with impact assessment, test evidence, approvals, release records and post-change monitoring. Material vendor, model, configuration, prompt, retrieval or tool changes can trigger re-evaluation when they may alter quality, risk, cost or operational behaviour.
How are security, privacy and responsible-AI controls handled?
The service can operationalise agreed controls for access, data handling, prompt and tool use, model or vendor changes, output review, evidence retention and escalation. Reference frameworks such as NIST AI RMF, ISO/IEC 42001 and OWASP GenAI security guidance can inform the control approach where relevant, but the managed service does not itself guarantee legal compliance or certification.
What deliverables and operational evidence do we receive?
Typical outputs can include a service catalogue and responsibility model, AI system inventory, monitoring and evaluation baseline, runbooks, incident and request records, change evidence, evaluation reports, control evidence, operational dashboards, governance packs, improvement backlog and transition documentation. Final deliverables depend on the agreed scope.
How is AI Managed pricing calculated?
DataConsultant pricing is custom and confirmed after scoping. Important factors include the number and criticality of AI systems, environments, support window, evaluation frequency, model and vendor diversity, RAG or data-operations scope, monitoring tooling, incident and change processes, security and privacy requirements, reporting cadence, transition effort and required documentation.
How long does it take to transition into AI Managed operations?
A reliable transition timeline is confirmed after scoping and evidence review. Timing depends on system inventory quality, access, existing runbooks, monitoring maturity, evaluation assets, vendor dependencies, control requirements, stakeholder availability and the number of services moving into the operating model.
Can DataConsultant work with our internal AI team and existing vendors?
Yes. The operating model can divide responsibilities across product owners, internal engineering or MLOps teams, data teams, security and risk functions, cloud providers, model vendors and systems integrators. Decision rights, evidence access, escalation routes and handoffs should be explicit before service commencement.
When is AI Managed not the right first service?
If the primary need is to design a new AI strategy, assess an unproven use case, build an initial prototype, perform a one-off assurance review or remediate a major platform problem, a strategy, implementation, assessment or assurance engagement may be a better first step. AI Managed is best suited to ongoing operational responsibilities around systems that need sustained oversight.
Can the service be transitioned back to our team or another provider?
Yes. Transition-out can be planned as part of the operating model, including current inventory, runbooks, open issues, known risks, monitoring baselines, evidence packs, configuration records, access dependencies and knowledge transfer. The exact exit activities are agreed in scope.
AI Managed Enquiry
Request an AI Managed Scope Review
Share your contact details and requirement. DataConsultant can review the likely operating boundaries, evidence needed, transition considerations and appropriate commercial next step.