A managed service that keeps your production AI accurate, available and improving after launch, whether Strongly built it or your team did. It pairs a platform that monitors continuously with data scientists and engineers who do the work only people can do. We do not consider the work done until it runs in production and keeps running.
A model that was accurate in a demo drifts as data changes, costs creep, providers ship new models, and a system that quietly degrades stops moving the metric it was built for. Strongly was built around that problem. The platform watches quality, uptime, drift, cost and usage continuously. Our team watches those alongside the business outcome, and acts when either drifts.
For solutions Strongly built. Day Two follows AI Assembly with no gap, already instrumented and mapped into your context layer.
For solutions your team built. We start with a short onboarding to review the system, instrument it, baseline quality and cost, and map it into the context layer before it goes under management.
More than uptime. We watch the system, watch the business result, keep it secure and governed, and keep improving the solution over time.
Full execution tracing, logs and evaluations on every workflow and agent. Quality, latency, uptime and accuracy watched continuously.
Beyond system health, we track the business metric each solution was built to move. If the commercial result drifts, we act.
Data and concept drift detected automatically. Changes validated with regression evals, champion-challenger and A/B testing before production.
As providers ship and deprecate models, we re-route, re-tune, retrain and update prompts. Routing fails over so one provider outage is not yours.
Attested, versioned policies allow, block or hold each action before it executes, fail closed, and leave a tamper-evident record.
Role-based access, audit logs, encryption, secrets management, patching and penetration testing, with third-party attestations.
Infrastructure, solution and model costs monitored around the clock, with budgets that auto-scale or switch off features when exceeded.
We account for seasonal and event-driven demand in both the models and the infrastructure, so solutions stay accurate and stay up.
Optionally, our team adds new capabilities over time, so solutions keep pace with changing expectations and improving techniques.
The platform handles continuous monitoring and active enforcement at a scale and speed people cannot match. Our data scientists and engineers do the judgment work: deciding what a signal means and what to change.
AI-Gateway analytics, a Model Registry with drift detection, workflow and agent tracing, passive and active governance, and FinOps tagging - continuous observability across quality, uptime, drift, cost and usage.
Re-route, re-tune, retrain and add features. Watch the business outcome, not only system health. Author the policies the platform enforces. Intervene or roll back fast when a model goes commercially wrong.
Dashboards and alerts tell you something went wrong - once it already has. Necessary, but it cannot undo an action.
Where we run the agents, attested, versioned policies allow, block or hold each action before it executes, fail closed, and leave a tamper-evident record. The decision logs become examiner-ready evidence on request.
Both tiers share one incident framework, an uptime dashboard and a monthly report. SLA commitments apply to Strongly-hosted dedicated deployments.
Monthly uptime, backed by tiered service credits. Support during business hours Eastern Time, Monday through Friday, with critical P1 incidents handled 24/7.
Monthly uptime, backed by tiered service credits, with full 24/7 support across all incident priorities and a defined escalation path.
| Priority | Acknowledgment | Resolution target |
|---|---|---|
| P1 · Critical | 1 hour from report | 4 hours |
| P2 · High | 4 hours from report | 24 hours |
| P3 · Low | 1 business day | 5 business days |
Each review summarizes availability and downtime, reports on quality, drift, cost and usage, and ends with concrete recommendations for the next improvement - tied to the business metrics we agreed at the start.
Monitoring and enforcement, around the clock, with a live availability dashboard for your environment.
An uptime report after each month closes - availability and any downtime events in the period.
A business review against agreed metrics, with the next improvement prioritized against that metric.
Each review turns live behavior into the next improvement, and each improvement is captured in your context layer - so the system gets better and the team gets smarter about it over time.
AI Day Two does not end with a handoff. We bring the people, the process and the platform to take a solution into production and keep it running.
Data scientists and engineers stay engaged after launch, doing the judgment work monitoring alone cannot.
Day Two is owned from day one. Reviews tied to business outcomes, changes validated before production.
FinOps, AI-Gateway, Model Registry and workflow monitoring give continuous observability.
Set up AI Day Two and keep your production AI accurate, available and improving - whether we built it or your team did.
or email sales@strongly.ai