Managed IT services for software and AI. Launch is day one.

Managed IT services are the ongoing running of your systems by an outside partner: monitoring, incident response, maintenance, security upkeep and improvement, under one agreement with response times written into the contract. DigyAi provides them for the software layer: applications and APIs, data platforms, cloud infrastructure, and AI models and agents in production.

Application managed services, also called application management services, are the support, maintenance and improvement of business applications by an engineering partner after launch. The partner fixes defects, keeps frameworks and dependencies current, answers incidents by severity, ships small enhancements and reports every month. Larger new work is scoped as its own phase.

Monitoring against service levels, on-call incident response in the hours your contract sets, bug fixes and small changes, dependency and security updates, backups proven by restore drills, access reviews, cost reviews, evaluation and model upkeep for any AI, and a report every month. The contract names each system covered and the response time for each severity.

We provide L2 and L3: the engineers who diagnose and fix the application, the data, the infrastructure and the AI. First-line support usually stays with your service desk, which escalates to us through your ticketing system. Where there is no service desk, alerts and user reports come to our engineers directly.

Support answers what happens today: alerts reach a person, incidents are handled by severity and users get answers. Maintenance prevents the next incident: dependency and security updates, framework upgrades, small fixes, performance tuning and restore drills. Both sit inside the same monthly agreement and the same report.

LLMOps is the practice of running large language model applications in production. Ours covers evaluation suites run on every prompt, model or retrieval change, quality graded on live traffic, drift and cost per task watched against budgets, guardrail and prompt-injection monitoring, and model migrations planned before a provider retires the model you use. For agents it adds action tracing, approval queues and spend caps.

Site reliability engineering as a service runs your production systems to agreed service level objectives. Each service has an objective and an error budget, alerts fire on how fast that budget burns, incidents end in blameless reviews, and repeated manual work is automated away. When a budget runs low, reliability work takes priority over new features until it recovers.

Response and update times are set per severity in your contract, agreed at onboarding for each system and the hours it needs covering. Service level objectives for the systems themselves are written into the operations repository, measured continuously and reported every month. Systems that cannot wait until morning get an out-of-hours rotation, staffed for that system before cover starts.

The least that each task needs, granted through your own identity provider. Routine work runs through pipelines and read-only dashboards; elevated access is requested just in time, approved, time-limited and logged, with a break-glass route for emergencies. Access is reviewed every month, and removing it at handback is one step in HANDBACK.md.

We run the controls and collect the evidence as the work happens: patch records, access reviews, change approvals, incident reviews and restore drills, filed in your repository for your auditors. For firms under DORA, the contract includes the exit terms and transition support the regulation requires, and HANDBACK.md is the tested exit plan.

We migrate before the deadline. Every model in production has its retirement date tracked; when a notice arrives, candidate models are graded against your own cases on quality, grounding, latency and cost, run in shadow on live traffic, then released behind a canary with a one-line rollback. Your AI owner approves the switch.

Yes, after an audit of one to two weeks. We read the code, infrastructure, alerts and incident history, write down the risks and say what must be fixed before cover can start. Then we shadow your team or current provider, run it while they check our work, and take over when the runbooks are signed off.

Choose by who owns the outcome. With staff augmentation you rent engineers and manage the work yourself; an in-house team gives you full control at the cost of hiring for every skill and every rotation. Managed services make us accountable for agreed service levels across application, data, cloud and AI, while your own engineers stay on the product.

Four things set the cost: how many systems we cover and how complex they are, the hours of cover each one needs, the response times per severity, and how much improvement work you want each month beyond keeping things running. The audit turns those into a written proposal, and each phase is approved before it starts.

Service levels met and missed, the error budget left for each service, every incident with its review, changes shipped and any rolled back, security updates and open findings, AI quality and model changes, running cost against the month before, and what we recommend next. It is filed in your repository as well as sent.

Yes. After the first term the service runs month to month with no exit fee. Everything we operate already lives in your environment and repositories: service levels, alert rules, runbooks, dashboards, evaluation suites and every review and report. At handback we walk your team or your next provider through it and remove our access.