You spent INR 30 lakh building an AI system. It's been degrading for three months and nobody noticed.
AI tools don't maintain themselves. Models drift, APIs change, data pipelines break silently, and performance degrades without anyone raising a flag. We provide the ongoing operations layer (monitoring, maintenance, enhancements, and strategic reviews) that keeps your AI investment delivering returns.
Deploying AI is the beginning, not the end.
Silent degradation
Your AI system worked beautifully at launch. Accuracy was 92%. Six months later, it's 74%, but nobody knows because there's no monitoring in place. The model was trained on data from Q3 last year, your product catalog has changed, customer behavior has shifted, and the system is making decisions based on a reality that no longer exists. By the time someone notices the outputs look wrong, the damage (bad recommendations, missed classifications, incorrect automations) has been compounding for weeks.
The developer who built it moved on
The team or vendor who built your AI system delivered it and moved to the next project. When an API provider changes rate limits, a data source format shifts, or the system throws an error nobody recognizes — there is no one to call. Your operations team has inherited a system they can watch on a dashboard but can't debug or extend.
Enhancement requests that never ship
Your AI system works, but your team has a growing list of improvements: 'Can we add this data source?' 'Can it handle this edge case?' 'Can we get a weekly summary instead of daily?' These requests sit in a backlog that never gets prioritized because your engineering team is focused on the core product and nobody has dedicated capacity for AI system work. Month by month, the tool falls further behind what the business actually needs.
No structured review cadence
Nobody is asking: 'Is this AI system still aligned with what we're trying to do?' Without a regular review that covers business alignment alongside performance metrics, your AI investment becomes a static tool in a moving business. The system that gave you an edge a year ago quietly becomes something that 'kind of works.'
Monitoring. Maintenance. Enhancement. Review. Every month.
We operate your AI systems with the same discipline we build them. Structured processes, defined SLAs, and a monthly rhythm that catches degradation before it becomes a problem.
Onboarding & Baseline
Week 1
Whether we built the system or took it over from another team, the first week is about establishing operational baselines. We audit the current system state, document all components and dependencies, configure monitoring and alerting, establish performance baselines for every key metric, and define the escalation path and SLA framework. If we built the system, this phase is fast because we already know the architecture. If we're inheriting, we conduct a thorough technical review and flag any immediate risks.
Deliverable: System audit report, monitoring and alerting configuration, baseline performance metrics, SLA framework document
Weekly Health Checks
Every week
Every week, we run a structured health check across your AI systems: model performance metrics (accuracy, latency, throughput), data pipeline integrity (completeness, freshness, schema compliance), API and integration status (uptime, rate limits, error rates), infrastructure utilization (compute, storage, costs), and alert review (any triggered alerts and their resolution status). Issues are classified by severity and addressed according to SLA. You don't need to chase us. We surface anything that needs attention before you ask.
Deliverable: Weekly health check summary with status, issues identified, and actions taken or scheduled
Bug Fixes & Maintenance
Ongoing per SLA
When something breaks (an API changes, a data pipeline fails, a model starts producing unexpected outputs) we respond according to defined SLAs. Critical issues (system down, data corruption): 4-hour response, 24-hour resolution. High priority (significant degradation): 8-hour response, 48-hour resolution. Medium (partial impact): 24-hour response, 72-hour resolution. Low (cosmetic, minor): 48-hour response, next sprint. Every fix is documented, root cause is analyzed, and preventive measures are implemented to avoid recurrence.
Deliverable: Issue resolution with root cause analysis, preventive measures, and updated documentation
Enhancement Sprints
Monthly (Standard & Premium tiers)
Your AI system needs to evolve with your business. Standard tier includes 8 hours of enhancement work per month; Premium includes 20 hours. Enhancements are prioritized in a shared backlog and executed in monthly sprints. Typical enhancements: adding new data sources, improving model accuracy for specific edge cases, building new reports or dashboards, optimizing processing speed, extending automation coverage to new workflows. We track enhancement velocity so you can see the ongoing evolution of your system.
Deliverable: Completed enhancements deployed to production, updated documentation, enhancement velocity report
Monthly Report & Review Call
Monthly
Every month, you receive a comprehensive report covering system performance vs. baselines, uptime and SLA compliance, issues resolved and their root causes, enhancements completed, and recommendations for the coming month. We conduct a 60-minute review call to walk through the report, discuss priorities, and align on the next month's enhancement backlog. Premium tier clients additionally receive a Quarterly Business Review (QBR) that assesses strategic alignment, ROI performance, and identifies new opportunities.
Deliverable: Monthly operations report, review call recording and notes, updated enhancement backlog
Onboarding & Baseline
Week 1
Whether we built the system or took it over from another team, the first week is about establishing operational baselines. We audit the current system state, document all components and dependencies, configure monitoring and alerting, establish performance baselines for every key metric, and define the escalation path and SLA framework. If we built the system, this phase is fast because we already know the architecture. If we're inheriting, we conduct a thorough technical review and flag any immediate risks.
Deliverable: System audit report, monitoring and alerting configuration, baseline performance metrics, SLA framework document
Weekly Health Checks
Every week
Every week, we run a structured health check across your AI systems: model performance metrics (accuracy, latency, throughput), data pipeline integrity (completeness, freshness, schema compliance), API and integration status (uptime, rate limits, error rates), infrastructure utilization (compute, storage, costs), and alert review (any triggered alerts and their resolution status). Issues are classified by severity and addressed according to SLA. You don't need to chase us. We surface anything that needs attention before you ask.
Deliverable: Weekly health check summary with status, issues identified, and actions taken or scheduled
Bug Fixes & Maintenance
Ongoing per SLA
When something breaks (an API changes, a data pipeline fails, a model starts producing unexpected outputs) we respond according to defined SLAs. Critical issues (system down, data corruption): 4-hour response, 24-hour resolution. High priority (significant degradation): 8-hour response, 48-hour resolution. Medium (partial impact): 24-hour response, 72-hour resolution. Low (cosmetic, minor): 48-hour response, next sprint. Every fix is documented, root cause is analyzed, and preventive measures are implemented to avoid recurrence.
Deliverable: Issue resolution with root cause analysis, preventive measures, and updated documentation
Enhancement Sprints
Monthly (Standard & Premium tiers)
Your AI system needs to evolve with your business. Standard tier includes 8 hours of enhancement work per month; Premium includes 20 hours. Enhancements are prioritized in a shared backlog and executed in monthly sprints. Typical enhancements: adding new data sources, improving model accuracy for specific edge cases, building new reports or dashboards, optimizing processing speed, extending automation coverage to new workflows. We track enhancement velocity so you can see the ongoing evolution of your system.
Deliverable: Completed enhancements deployed to production, updated documentation, enhancement velocity report
Monthly Report & Review Call
Monthly
Every month, you receive a comprehensive report covering system performance vs. baselines, uptime and SLA compliance, issues resolved and their root causes, enhancements completed, and recommendations for the coming month. We conduct a 60-minute review call to walk through the report, discuss priorities, and align on the next month's enhancement backlog. Premium tier clients additionally receive a Quarterly Business Review (QBR) that assesses strategic alignment, ROI performance, and identifies new opportunities.
Deliverable: Monthly operations report, review call recording and notes, updated enhancement backlog
Continuous operations with monthly cadence.
Onboarding (Week 1)
- System audit with architecture documentation and dependency mapping
- Monitoring and alerting configuration across all system components
- Performance baseline establishment for all key metrics
- SLA framework with escalation paths and response commitments
Ongoing (Monthly)
- Weekly health check summaries covering performance, pipelines, APIs, and infrastructure
- Bug fixes and maintenance per SLA with root cause analysis
- Enhancement sprints (8 or 20 hours/month depending on tier)
- Monthly operations report with performance vs. baselines
- Monthly review call (60 minutes) with recorded notes and action items
Quarterly (Premium Tier)
- Quarterly Business Review (QBR) assessing strategic alignment and ROI
- Model retraining recommendations based on data drift analysis
- Technology review with upgrade or migration recommendations
- Updated roadmap for system evolution aligned with business priorities
We operate and enhance. Major rebuilds are separate.
The retainer covers monitoring, maintenance, and incremental enhancements. Significant new functionality is scoped as a build engagement.
New AI system development or major feature builds
If the enhancement you need exceeds the monthly hours or requires new architecture, we scope it as a build engagement. Since we already know your system, scoping and delivery go faster.
Custom AI Tool DevelopmentWorkflow automation and process redesign
If your operational needs have evolved and you need new automations across different systems, beyond the AI tools we're managing, that's a separate automation engagement.
Business AutomationAI strategy and new opportunity assessment
The QBR may surface new AI opportunities beyond the systems we currently manage. A full diagnostic maps those opportunities systematically and builds the business case for the next investment.
AI Strategy & DiagnosticIs this the right fit?
Right for you if
- You have one or more AI systems running in production — built by us or another team — and you need a dedicated operations partner to keep them performing and aligned with the business as it changes.
- Your engineering team is focused on the core product. You don't want them context-switching to manage model drift, API integrations, and AI-specific infrastructure that requires a different skill set.
- You've watched an AI system degrade silently, or sat on an enhancement backlog that never moved. You want a structured rhythm with defined SLAs and clear accountability.
- You're not looking for a one-time fix. You want someone who gets progressively better at operating your system over time.
Not right if
- You haven't built or deployed an AI system yet. You need our AI Strategy & Diagnostic or Custom AI Tool Development service first. This retainer is for post-deployment operations.
- You need a one-time fix or audit for an existing AI system, we can scope that as a one-time engagement instead of a monthly retainer. Get in touch to discuss.
What AI operations looks like across industries.
Problem
An NBFC had deployed an AI-powered document verification system that was processing 500+ loan applications weekly. Six months post-launch, approval accuracy had dropped from 92% to 78% because the model was trained on pre-COVID financial documents, and post-COVID income patterns looked fundamentally different. The internal team had no process for detecting or correcting model drift.
What we did
Onboarded the system onto our operations framework. Set up drift detection monitoring that flags accuracy degradation within 48 hours instead of months. Retrained the model on updated financial document patterns during the first enhancement sprint. Established a quarterly retraining cadence aligned with economic cycle data. Monthly reports now include accuracy trending, processing volume, and flagged edge cases.
Outcome
Accuracy restored to 94% within the first month. Drift detection now catches degradation before it impacts business outcomes. The quarterly retraining cadence has kept accuracy above 90% consistently. The operations team focuses on underwriting decisions, not system babysitting.
Problem
A logistics SaaS company had built an AI support triage system that auto-classified and responded to 60% of incoming tickets. After the build team moved on, the system slowly fell behind: new product features weren't in the knowledge base, response templates became outdated, and the classification model couldn't handle ticket types that didn't exist when it was trained. Customer satisfaction scores for AI-handled tickets dropped from 4.2 to 3.1 out of 5.
What we did
Took over operations on a Standard retainer. First month: updated the knowledge base with 6 months of product changes, retrained the classification model on recent ticket data, and refreshed all response templates. Ongoing: monthly knowledge base updates synchronized with the product release cycle, continuous model refinement based on misclassification reports, and a monthly review call with the Head of Support to align priorities.
Outcome
Customer satisfaction for AI-handled tickets recovered to 4.4 within 90 days. Auto-resolution rate increased from 60% to 71% as the knowledge base and model caught up. The 8 hours of monthly enhancements consistently deliver measurable improvements to support efficiency.
Problem
A diagnostic chain had deployed an AI-assisted preliminary report generation system across 30+ centres. The system worked well for the original 12 test types it was trained on, but the chain had since added 8 new test types through expansion. Reports for these new tests were generating errors or nonsensical results, and the radiologists had started ignoring AI-generated prelims entirely, defeating the purpose of the system.
What we did
Onboarded on a Premium retainer. First quarter: extended the system to cover all 20 test types using the 20-hour monthly enhancement budget. Established automated quality scoring that compares AI prelims against final radiologist reports to detect accuracy drift. Implemented a structured feedback loop where radiologist corrections are captured and used for monthly model refinement. QBRs now include clinical accuracy metrics alongside operational efficiency data.
Outcome
System coverage expanded from 12 to 20 test types within one quarter. Radiologist trust restored: 85% of AI prelims are now reviewed and accepted (up from 40% when adoption had collapsed). Report turnaround time reduced by an additional 20% beyond the original deployment gains.
What an AI operations retainer looks like over 12 months.
Accuracy maintained above 90% for 12 consecutive months, zero undetected outages
A lending platform that had deployed an AI document verification system through our build service transitioned to a Standard operations retainer immediately after the 2-week post-launch support period. Month 1: We established performance baselines: 92% accuracy, 4-hour average processing time, 500+ applications per week. Set up comprehensive monitoring with alerts for accuracy drops, pipeline failures, and processing delays. Months 2-4: Two critical issues caught by monitoring before they impacted operations. First, an API rate limit change by a third-party document verification provider that would have caused a 6-hour processing backlog, resolved within 3 hours. Second, an accuracy drop to 86% on a specific document type caused by a bank changing their statement format, model retrained within 48 hours. Months 5-8: Enhancement sprints focused on business-requested improvements. Added support for 3 new document types, built a weekly exception report for the underwriting team, and optimized processing speed by 30% through pipeline improvements. Months 9-12: Quarterly model retraining kept accuracy above 90% despite changing economic conditions affecting financial document patterns. The monthly review call surfaced a new opportunity: extending the system to handle commercial loan documents, which was scoped as a build engagement. Total downtime over 12 months: 47 minutes (two incidents, both resolved within SLA). Enhancement velocity: 14 improvements shipped. The system now processes 700+ applications weekly with higher accuracy than at launch.