How much does a managed AI services retainer cost?

It's a fixed monthly fee, scoped on a free call. The price depends on how many systems are in scope, how critical they are, and the response times you need — you get a written proposal with the tier, the responsibilities, and the fee before anything starts.

What happens when a model provider retires or changes a model?

We test the replacement against your evaluation suite before switching, adjust prompts where behavior changed, and roll it out through a reversible release, so a provider change doesn't become a surprise for your users.

Do we keep ownership of our code and data?

Yes. We work in your repositories and accounts with access you control. The code, prompts, evaluation data, and documentation remain yours if the retainer ends.

Managed AI services

Managed AI services: we run your AI systems so you don't have to

Shipping an AI system is the start, not the finish. Models change underneath you, data drifts, prompts that worked last quarter degrade, and API costs creep up. Our managed AI services retainer keeps your chatbots, agents, and LLM features healthy month after month — monitored, evaluated, updated, and cost-controlled — without you hiring a dedicated AI operations team.

Start your project

Why AI systems need ongoing operation

Traditional software mostly stays the way you left it. AI systems don't. Model providers release new versions and retire old ones on their own schedule, and even a minor version change can shift how a prompt behaves. The documents your retrieval pipeline depends on get updated, moved, or deleted. Users find inputs nobody tested for. And because usage-based pricing scales with every token, a small inefficiency becomes a large bill as adoption grows.

None of that shows up as an outage. It shows up as answers that are slightly worse, an agent that escalates more often, or an invoice that is higher than last month. A managed service exists to catch those changes early and act on them.

What the retainer covers

Every retainer is built from the same core responsibilities, scaled to how many systems you run and how critical they are.

Monitoring and alerting

Dashboards and alerts for error rates, latency, tool-call failures, escalation rates, and user feedback, plus sampled review of real conversations and agent runs so problems are caught before customers report them.

Evaluations

A versioned evaluation suite that runs on a schedule and before every change, scoring accuracy, groundedness, safety, and task success so you can see whether the system is getting better or worse — and prove it.

Model and prompt updates

We track provider releases and deprecations, test new model versions against your evaluation suite before switching, tune prompts and retrieval as your content changes, and roll updates out behind a documented, reversible release process.

Cost control

Monthly spend reporting by feature, model right-sizing, prompt caching, context trimming, and budgets with alerts — with every optimization checked against the evaluations so savings never quietly cost quality.

Service tiers

Retainers come in three broad shapes. They're described qualitatively here because the right level depends on your systems, usage, and response-time needs — the fee is fixed monthly and scoped on a free call.

Monitor — for a single, lower-risk AI feature: monitoring, alerting, a monthly evaluation run, a monthly cost report, and provider-change tracking
Operate — for business-critical systems: everything in Monitor plus continuous evaluations, proactive model and prompt updates, cost optimization, and a small monthly block of improvement work
Embedded — for teams running several AI systems: a named AI engineer or a small pod working in your stack, priority response, a quarterly roadmap review, and ongoing feature work alongside operations

How onboarding works

If we built the system, onboarding is short: the handover documentation from our AI agent development process becomes the operating manual. If someone else built it, we start with a review of the code, prompts, tools, and costs — often a lighter version of our AI security audit — so we know what we're taking on, set up monitoring and a baseline evaluation, and agree the response times, change process, and escalation contacts in writing.

You keep ownership of everything: the code, the prompts, the evaluation data, and the cloud and model-provider accounts. We work in your repositories and your accounts, with access you control and can revoke.

Is a managed service the right fit?

It suits teams whose AI systems matter to customers or operations but who don't have — or don't want to hire — specialists to watch them full time. If you'd rather build that capability in-house, we can place AI engineers with you instead, or run a managed retainer while your team ramps up and then hand over. Every engagement starts with a free discovery call, where we learn your goals, systems, and constraints before proposing a scope.

Frequently asked questions

An ongoing monthly retainer where we operate your AI systems — monitoring, scheduled evaluations, model and prompt updates, and API cost control — so they keep performing after launch without a dedicated in-house AI operations team.