Your AI tool shipped. Now who keeps it working?

AI operations support and managed services for mid-market companies. Monitoring, bug fixes, enhancements, and quarterly reviews so your AI tools keep performing after deployment. $5-$15K/mo retainer.

The Problem

Deploying AI is just the beginning.

Silent degradation: Your AI system launched at 92% accuracy. Six months later it's 74%, but nobody knows because there's no monitoring. The model was trained on Q3 data. Your product catalog changed, customer behavior shifted, and the system is making decisions based on a reality that no longer exists. By the time someone notices, weeks of damage have already piled up.

The developer who built it moved on: The team or vendor who built your AI system delivered it and moved on. When an API provider changes rate limits, a data source format shifts, or the system throws an error nobody recognizes, there's 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 improvement requests keep piling up. 'Can we add this data source?' 'Can it handle this edge case?' These sit in a backlog that never gets prioritized because your engineering team is busy with the core product. Month by month, the tool falls further behind what the business needs.

No structured review cadence: Nobody is asking: 'Is this AI system still doing what we need it to do?' Without a regular review that covers business goals alongside performance metrics, your AI investment becomes a static tool in a moving business. What gave you an edge a year ago quietly turns into something that 'kind of works.'

Our Approach

Monitoring. Maintenance. Enhancement. Review. Every month. We operate your AI systems with the same discipline we use to build them. Structured processes, defined SLAs, and a monthly rhythm that catches degradation before it becomes a problem.

Phase 1 — Onboarding & Baseline (Week 1): Whether we built the system or inherited it, week one is about operational baselines. We audit current system state, document components and dependencies, configure monitoring and alerting, set performance baselines, and define the SLA framework. If we built the system, this goes fast. If we're taking over, we run a full technical review and flag immediate risks. Deliverable: System audit report, monitoring and alerting configuration, baseline performance metrics, SLA framework document

Phase 2 — Weekly Health Checks (Every week): Every week, we run a structured health check across your AI systems: model performance (accuracy, latency, throughput), data pipeline integrity (completeness, freshness, schema compliance), API and integration status (uptime, rate limits, error rates), infrastructure usage (compute, storage, costs), and alert review (any triggered alerts and their resolution). Issues are classified by severity and handled per SLA. We flag anything that needs attention before you ask. Deliverable: Weekly health check summary with status, issues identified, and actions taken or scheduled

Phase 3 — Bug Fixes & Maintenance (Ongoing per SLA): When something breaks (an API changes, a data pipeline fails, a model starts producing bad outputs) we respond per 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 with root cause analysis, and we put preventive measures in place to avoid recurrence. Deliverable: Issue resolution with root cause analysis, preventive measures, and updated documentation

Phase 4 — 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 shipped in monthly sprints. Typical work: adding new data sources, improving model accuracy for specific edge cases, building new reports or dashboards, speeding up processing, extending automation to new workflows. We track enhancement velocity so you can see how the system is improving over time. Deliverable: Completed enhancements deployed to production, updated documentation, enhancement velocity report

Phase 5 — Monthly Report & Review Call (Monthly): Every month, you get a detailed report covering system performance vs. baselines, uptime and SLA compliance, issues resolved with root causes, enhancements completed, and recommendations for the coming month. We run a 60-minute review call to walk through the report, discuss priorities, and agree on the next month's enhancement backlog. Premium tier clients also get a Quarterly Business Review (QBR) that assesses strategic fit, ROI performance, and spots new opportunities. Deliverable: Monthly operations report, review call recording and notes, updated enhancement backlog

Deliverables

Who This Is For

Right for you if: You have one or more AI systems in production (built by us or another team) and need an operations partner to keep them performing as the business changes.. Your engineering team is busy with the core product. You don't want them switching between product work and managing model drift, API integrations, and AI-specific infrastructure.. 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 named accountability.. You want a partner who gets better at operating your system over time — someone invested in the long run..

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..

Use Cases

Financial Services: 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. — Onboarded the system onto our operations framework. Set up drift detection that flags accuracy drops within 48 hours instead of months. Retrained the model on updated financial document patterns during the first enhancement sprint. Set a quarterly retraining cadence timed to 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 problems before they hit business outcomes. Quarterly retraining has kept accuracy above 90% consistently. The operations team focuses on underwriting decisions instead of system babysitting.

B2B SaaS: 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. — 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 synced with the product release cycle, model refinement based on misclassification reports, and a monthly review call with the Head of Support to sync on 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 monthly enhancement hours consistently deliver measurable improvements to support efficiency.

Healthcare: A diagnostic chain had deployed an AI-assisted preliminary report generation system across 30+ centres. It 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. — Onboarded on a Premium retainer. First quarter: extended the system to cover all 20 test types using the 20-hour monthly enhancement budget. Added automated quality scoring that compares AI prelims against final radiologist reports to detect accuracy drift. Built a feedback loop where radiologist corrections feed into 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 dropped by another 20% beyond the original deployment gains.

Results

What 12 months of AI operations looks like.

Financial Services — Document Verification System: Accuracy maintained above 90% for 12 consecutive months, zero undetected outages. A lending platform that deployed an AI document verification system through our build service moved to a Standard operations retainer right after the 2-week post-launch support period. Month 1: We set performance baselines (92% accuracy, 4-hour average processing time, 500+ applications per week). Set up monitoring with alerts for accuracy drops, pipeline failures, and processing delays. Months 2-4: Two critical issues caught by monitoring before they hit operations. First, an API rate limit change by a third-party 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 sped up processing 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, scoped as a build engagement. Total downtime over 12 months: 47 minutes (two incidents, both resolved within SLA). Enhancement velocity: 14 improvements shipped. The pipeline now processes 700+ applications weekly with higher accuracy than at launch.

Frequently Asked Questions