Recovering faster each time is progress. Never seeing it again is the point.
NuPulse reads across the observability, service management, infrastructure and enterprise systems you already run, predicts the failure, prescribes the specific fix, acts inside your policy, and verifies it landed.
Your tools can see the problem. Nothing decides what to do about it, nothing is allowed to act, and nothing removes the reason it happened. So the same failure comes back next month, and your team handles it a little faster than last time.
of organizations report outages caused by alerts that were seen and ignored
Splunk, State of Observability 2025
of engineering time lost to manual repetitive work
Catchpoint, SRE Report 2025
tickets a month per organization, on average
HDI, State of Tech Support 2025
Most teams run two to ten monitoring tools and nothing reasons across them. The block is permission, not data: a rule that can decide, an agent that may act, a check that proves it landed, and a record of all three. That is an operating change rather than a data project, which is why most teams stall at stage three.
Before anything runs unattended, four things have to hold.
Rules return the verdict. Model output cannot overrule it.
Executed by the owning team’s agent, under their credentials.
Not done until checked, and rolled back if it did not land.
Which rule, which evidence, which system, who or what.
Seven steps, one loop, and it gets smaller each time around. Three of them are where most teams have nothing at all, which is why their loop never shrinks however fast the middle runs.
Detect, diagnose, remediate and verify are the incident, and most teams run them well. Predict stops the loop starting, decide is what allows it to run without a person in the middle, and learn removes the reason it ran at all. A loop without those three can only ever get faster. With them it gets shorter, then rarer, then gone.
Not a black box that answers questions. Each part does one job, your engineers configure it, and any of them can be switched off without breaking the rest.
Marked in development and not shipped capability: the declared knowledge graph for multi-hop reasoning across owners and dependencies, cross system entity resolution so a service carries one identity across observability, service management and delivery, and A2A interoperability so NuPulse agents work alongside the agents your teams and vendors already run, under one policy.
Your engineers and your agents share one context and one record. Autonomy is set once per application rather than approved action by action, because a queue does not scale and policy does.
Question an application, a dependency or an objective in plain language and get a grounded answer with its evidence, instead of querying four tools first. Then delegate the work and keep watching it.
Inside limits your team declared, it resolves the class and records what it did. Nobody is paged, because the limits were agreed once rather than per action. This is where self-healing lives.
Novel, ambiguous or out of limits. The engineer gets a diagnosis and options, not a raw alert. An exception path, not the main path.
Uptime measures a component. Your customers experience a process. Here is the same seven steps on one fault in a shared integration service, from the release that caused it to the check that stops the next one. Illustrative, and built only from systems in the target list below.
Two services degrade at once. The one upstream of today’s orders is fixed first, and nobody has to guess.
Time to detect and time to restore, repeat rate per class, share of incidents closed with nobody involved, escalation rate, tickets linked per fault, cost per resolved incident. Read from where you already define them, with the false positive rate published alongside rather than netted out.
Availability of a process, not only uptime of a component.
Read in place, through interfaces your platforms already publish. The systems below are the ones customers ask about most. The real constraint at scale is rate limits rather than model quality, so NuPulse indexes and compresses before it reasons, and carries a cost budget per run.
Each is wired up per engagement rather than shipped as a named-vendor integration, through REST and GraphQL, MCP tools and servers, OpenTelemetry, Prometheus query, webhooks, syslog, JDBC and ODBC, queues or file drop. Adding a platform is configuration, not a build. Mainframe, midrange, thick client and packaged applications with no API at all are reached through a governed adapter inside your environment, under the same policy and the same record.
Risk is scored by rule, in milliseconds, before any action is offered. Nothing probabilistic sits between diagnosis and action.
Set by your team per application, never uniform across all of them.
An agent that proposes a financially controlled change cannot approve it.
Which rule, which evidence, which approver, which system wrote what.
Every application starts in read only mode, with proposals graded against what your engineers actually did.
Revocable instantly, per agent, by whoever runs it. Least privilege identity, scoped in one place.
Classification and forecasting across hundreds of applications run on models that cost a fraction of a language model call. Language models are used for root cause on a novel incident, reading runbooks and change notes, weighing options and answering in plain language. That split is what keeps cost per resolved incident from scaling with alert volume.
Your cloud or on premises, with signals queried in place and data kept in region. Model choice per task, large hosted, mid tier, or self hosted inside your own environment where data cannot leave.
Every model call from every agent passes the same governance path, so there is no route by which a model reaches a system of record without a rule having decided first.
Progress is proven against the baseline you already hold, on the measures listed above. No number is claimed here that your own tooling cannot verify.
Most teams are at or just below stage three. We begin from what you already run, measured against the baseline you already hold. Every application starts read only, with proposals graded against what your engineers actually did.
You see anomalies and describe causes, but a person still decides and acts. Prediction, the rules engine, the action path, verification and the record are what get added.
You already know hours ahead. Forecasts become costed actions, and the classes you already trust start resolving inside the limits your team declares.
Engineers stand it up inside your SRE group and work alongside your team, starting from the systems you already run and the baseline you already hold.
The planner, rules engine, registry, memory and audit trail can sit behind your own interface. Your platform keeps its name, roadmap and data.
The target state: recurring incidents close without anyone being paged, novel ones arrive already diagnosed, and engineering time goes into removing classes of failure.
No. It sits above and beside the tools you already run, reads them in place, and does not become the system of record.
Your SRE and operations group runs it. The team onboards applications, builds and tunes the agents, and declares what each one may do, acting through the operational access the team already holds. NuStudio does not operate it for you and does not hold credentials on your systems. Where an application team has built its own automation, NuPulse calls that instead of duplicating it.
Not to begin with. NuPulse reads what applications already emit through your existing observability, logging and ticketing platforms, so onboarding an application does not require its team to build anything. Application owners get involved when an action would touch something outside what the SRE group already operates, and at that point they set the limit once rather than approving each action.
Anything that exposes an API, through REST and GraphQL, MCP tools, OpenTelemetry, Prometheus query, webhooks, syslog, JDBC and ODBC, message queues or file drop. The only case needing different handling is a system with no API at all, reached through a governed adapter.
No, and deliberately so. Deterministic rules make every decision about whether an action may run. Statistical and classical machine learning handle the high volume work: anomaly detection, saturation and burn rate forecasting, alert clustering, deduplication, ticket classification and change risk scoring. Language models are used only where there is genuine ambiguity and unstructured evidence to reason over.
Read only mode first, deterministic limits on what any action may touch, verification after every action, rollback, and a full record so a bad action is explained in minutes rather than argued about for a week. Because execution is owned locally, no single component holds enough privilege to affect every application at once. We do not claim the risk is zero.
NuPulse is a product on the NuStudio runtime, using the same agent execution layer, semantic layer, memory, identity and access control, model routing and decision lineage as every other NuStudio product. Choosing NuPulse does not mean adopting a separate platform.
Any application portfolio large enough that no single team can see across it. NuPulse was engineered to the standard regulated sectors require, including healthcare, defense, financial services and insurance, because an automated action there has to be explainable to an auditor and not merely effective. The same product runs in consumer packaged goods, retail and distribution, manufacturing, logistics, energy, telecommunications, travel and the public sector.
It is usually the one that pages the most people and explains itself the least, which makes it the most useful place to begin.