Application Maintenance Services

Application maintenance services for the AI systems we build — a named escalation point after we leave.

On a check-in call, days after we handed over their AI tool, a multi-company middle-market contractor asked the question every buyer eventually asks: “Is there an escalation point if we need some work on it — can we put in some sort of maintenance structure?” Yes. That is what this is: we stay on for monitoring, model changes, and patching — monthly, cancelable, no lock-in.

  • AI implementation services
  • A named escalation point
  • Monitoring, model changes, patching
  • Monthly — cancel anytime

Built with the leading AI platforms

NVIDIAAnthropic
Peter Enestrom, founder of Zaigo

Led by Peter Enestrom and the Zaigo AI & engineering team

YaleColumbia UniversityMicrosoft

After handover

The build has a plan. The maintenance structure usually doesn’t.

From a check-in call with a multi-company middle-market contractor: their purpose-built AI tool was deployed and paid for, with rollout pending to around 30 users — and the one open question was who owns it from here.

  • “Who owns this after you leave?”

    The demo lands, the keys are in your pocket, and then the real question surfaces. If the answer is “call us if something breaks,” you don’t have support — you have a phone number. The buyer’s own words: is there an escalation point, someone who is actually supporting the actual tool.

  • The model underneath your tool changes

    Model vendors retire versions on a published schedule. The version your AI system was tuned on has an end date — and if nobody is watching the calendar, you find out when output drifts or a deprecated endpoint stops answering, not before.

  • The scanner never stops

    Vulnerability findings keep arriving after go-live — new CVEs in the libraries your tool depends on, new findings on the AI stack itself. Who patches, who remediates, how fast: if that isn’t agreed before launch, it becomes an argument after it.

  • Prepare support for rollout

    The group described a planned rollout to roughly thirty people. User questions, unexpected answers, and export problems need an agreed support owner before a wider rollout.

  • Nobody wants the 24/7 contract

    Classic application support is priced and staffed for a whole estate of legacy software — multi-year terms, a ticket queue, a team that has never seen your system. That is the opposite of what a delivered AI tool needs.

  • Handover that isn’t real

    If the only documentation lives in the builder’s head, you didn’t take delivery — you took custody. Real maintenance starts with docs and training your team can actually run with, so staying with us is a choice, not a dependency.

What the unsupported way costs

Without a maintenance structure, the tool decays quietly.

What it looks like when a delivered AI system has no named owner. Your numbers will differ — the maintenance agreement we write first puts figures on yours before you commit.

~30 usersThe rollout size on the real call — from a handful of hands to a team that has to be ready to go when it comes time
80% paidWhat the buyer volunteered before the final iteration — the tool was real, valuable, and worth protecting with a support structure
0 lock-inMulti-year terms in our maintenance arrangements — monthly, cancelable; the documentation and training make leaving possible

From an anonymized engagement — a multi-company middle-market contractor; one purpose-built AI tool, delivered with one iteration outstanding

How it works

A maintenance structure you can describe in one sentence.

The same team that built the system stays on to run it — with a named escalation point and an agreement written before the first invoice. Three steps, fixed order.

  1. 01

    Write the maintenance agreement

    Before anything starts: what we monitor and where it alerts, response times by severity, how patches and model upgrades are scheduled, what the monthly cost covers, and the named escalation point when something matters. Plain terms, monthly billing, cancelable.

    Written before it starts
  2. 02

    Monitor, patch, fix the drift

    Ongoing support can include health and cost monitoring, security patches, and model changes. Agree regression checks, release approval, and user communication before changes reach the team.

    Steady state
  3. 03

    Make the handover real

    Documentation your team can follow, admin and user training, and a runbook for the common cases. The point is that you could take it over — which is exactly why the arrangement is cancelable. Staying is a choice you make every month.

    Docs + training
The AMS intercept

Software maintenance services, rebuilt for systems with AI inside.

Search for software maintenance services and you get the classic AMS pitch: a ticket queue, a team rotating through your estate, a multi-year contract. That model was built for legacy software that changes slowly. A purpose-built AI system doesn’t — the model underneath it has a published retirement schedule, the libraries patch continuously, and the people using it notice output drift before any dashboard does.

So the shape is different. The people who built your system are the people who answer when you escalate. The scope is one system, not your whole estate. And because the documentation and training make it yours, the monthly arrangement is cancelable — support you choose, not a dependency someone engineered.

Peter Enestrom, founder of Zaigo
Who builds it

Led by Peter Enestrom.

Co-Founder — leads AI & Engineering

Pete Enestrom

Every engagement is led personally by Pete, working with the Zaigo AI & engineering team from the two-week audit through the production handover. The person who scopes the work is the person who builds it.

Education
Yale & ColumbiaGraduate
Background
Microsoft & IntelFormer
Experience
Exited FounderVenture-Backed

Background

Questions

Application support and maintenance services, answered straight.

What buyers ask after the build is done — in their words, not ours.

What’s holding your business back?

A workflow ready for automation. An AI product you want to build. A problem that has sat on the roadmap for years. Let’s talk about what it would take to solve it.

Talk to Zaigo