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AI & Data Maintenance

Continuous care for your AI systems — monitored, evaluated, updated, and improved long after launch.

AI & Data Maintenance is the continuous support of AI systems and data pipelines in production: monitoring, quality evaluation, model and prompt updates, pipeline care, and iterative improvement. AI systems degrade silently as data, models, and usage evolve — maintenance is what keeps them accurate, safe, and cost-efficient over time.

Who this is for

Organizations with AI solutions in production — built by us or anyone else — that need them to stay reliable without hiring a dedicated team.

What you get

  • AI systems that stay accurate as your data, models, and usage evolve
  • Early detection of quality drift, cost creep, and failure modes
  • Regular improvements driven by real usage data
  • A predictable support arrangement instead of emergency firefighting

What's included

Monitoring and alerting

Continuous tracking of system health, output quality, latency, and cost — with alerts before users notice problems.

Quality evaluation cycles

Scheduled runs of evaluation suites detect drift and regressions when models, prompts, or data change.

Updates and upgrades

Model migrations, prompt updates, dependency patches, and pipeline fixes — tested against evaluations before release.

Continuous improvement

Usage analysis feeds a prioritized improvement backlog, reviewed with you in regular service meetings.

How it works

  1. 01

    Onboard the systems

    We document current state, set up monitoring and evaluation baselines, and agree on service levels.

  2. 02

    Monitor continuously

    Dashboards and alerts track health and quality; issues are triaged and resolved within agreed response times.

  3. 03

    Update proactively

    Model and dependency changes are tested against evaluation baselines and rolled out safely.

  4. 04

    Review and improve

    Regular service reviews cover incidents, quality trends, costs, and the improvement backlog.

Deliverables

  • Monitoring dashboards and alerting
  • Scheduled evaluation reports
  • Tested updates and migrations
  • Quarterly improvement recommendations

Frequently asked questions

Why do AI systems need maintenance at all?

Because everything around them moves: providers deprecate and change models, your documents and data evolve, usage patterns shift, and costs drift. Without monitoring and evaluation, quality degrades silently until users lose trust — which is far more expensive than maintenance.

Can you maintain AI systems that another vendor built?

Yes. We start with a short audit to document the system and establish evaluation baselines, then take over ongoing care. Gaps found during onboarding are flagged with a remediation plan.

How is maintenance priced?

As a monthly service agreement sized to the number and criticality of systems covered — with defined response times, included improvement hours, and no surprise invoices.

Ready to scope this with us?

Tell us about your systems, data, and goals. We reply within one Swiss business day with a concrete next step.

Get a quote Book a discovery call

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