Stop getting faster
at fixing the same failure.
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, then predicts the failure, prescribes the fix, acts inside your policy, and verifies it landed.
Overview
Detection got good.
Nothing after it did.
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.
Speed is not the target. NuPulse prescribes the specific fix rather than describing the problem, carries it out inside the limits your team sets, then identifies the class behind it and raises the structural fix with the evidence to get it funded.
NuPulse runs inside your SRE and operations group. Your team onboards the systems and services it already operates, runs the agents, and sets what each one may do, using the access the team already has. It sits beside your existing tools and never becomes a system of record.
Maturity
Most teams stall at stage three.
The block is permission, not data.
Permission means a rule that can decide, an agent that may act, a check that proves it landed, and a record of all three. That is what the last three stages need, which makes the step an operating change rather than a data project.
Someone reports it first.
Anomalies detected, causes described.
You know hours ahead. A person still acts.
The specific action, costed and timed.
Carried out across teams, inside policy.
Known classes close with nobody paged.
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.
The loop
Predict. Detect. Diagnose. Decide.
Remediate. Verify. Learn.
Seven steps, one loop, and it gets smaller each time around. The three highlighted are where most teams have nothing at all, which is why their loop never shrinks however fast the middle runs.
The incident that never opens
Saturation, resource drift, certificate expiry and dependency degradation forecast against the error budget, and releases scored against the incidents they historically cause.
One problem, not a queue
Alert storms collapse into a single problem with a ranked cause set. MTTD falls without new instrumentation.
Which component, which change, which owner
Evidence cited, so an engineer checks the reasoning instead of trusting it. Multi-hop reasoning traces a downstream symptom to an upstream change in another application, rather than guessing at it.
What lets the fix run unattended
A deterministic verdict against limits your team declared: risk tier, action limits, change window, run budget. Without it, every fix needs a person in the middle.
An action, not a recommendation
The agent runs the matched runbook: scale, restart, fail over, roll back, clear the queue. This is the step that moves MTTR.
Proof, not hope
Checked for whether it landed and whether anything downstream moved, and rolled back if not. Nothing counts as done until this passes.
Remove the class, not the instance
Occurrences clustered, the underlying class identified, a structural fix raised with evidence. Never seeing it again is the point.
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 is what 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.
What you get
Seven parts. Your team owns every one.
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.
You use every tool you already own
Pulls from your platforms through their own interfaces, then indexes and compresses so an investigation costs a fraction of reading raw telemetry. This is what stops cost rising in step with alert volume.
You see who owns it and what breaks next
A knowledge graph of who owns what and what depends on what. Declared by your teams rather than inferred from telemetry, so it does not decay silently and route work to the wrong team. Added when a question needs multi-hop reasoning, not before.
You get costed options, not a diagnosis
Reads the signal, calls the specialists it needs, re-plans after each step, and produces costed options with cited evidence.
You decide what may run unattended
Risk tier, action limits, change window, run budget, separation of duties. Your team writes the limits, per application and per environment.
You can kill any agent from one screen
One place to see every agent, what it may touch, which version is live, and a kill switch on each. Least privilege by default.
Your tenth incident costs less than your first
Per application and per class, in your tenant. The reason the tenth occurrence is cheaper to handle than the first.
You answer an auditor in minutes
Which rule fired, which evidence was cited, which action ran, what the result was. Retrievable per decision, not reconstructed from logs.
- Reliability planner with re-planning and costed options
- Policy engine, deterministic verdict in milliseconds
- Agent registry with per-agent scope and kill switches
- Resolution memory per application and per class
- Identity and access control, least privilege by default
- Model routing per task, hosted or self hosted
- Decision lineage and audit search, retrievable per decision
- Declared knowledge graph for multi-hop reasoning across owners and dependencies
- Cross system entity resolution, one identity for a service across observability, ITSM and delivery
- A2A interoperability, so NuPulse agents work alongside the agents your teams and vendors already run, under one policy
Roadmap, not shipped capability. Everything on the left runs today.
Business outcomes
Every dashboard was green.
The shipment still missed the cutoff.
Uptime measures a component. Your customers experience a process. NuPulse orchestrates along the chain between them.
Scroll the diagram sideways to read it.
Two services degrade at once. The one upstream of today's orders is fixed first, and nobody has to guess.
MTTD and MTTR, 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.
How it works
Read in place. Reason. Decide. Hand to the owner.
Scroll the diagram sideways to read it.
Architecture diagrams are illustrative. Cross system entity resolution is a specified interface rather than shipped capability today.
Worked example
The same seven steps, on one real fault.
A shared integration service, from the release that caused it to the check that stops the next one. Illustrative, built only from systems in the integrations list below.
Flagged before it shipped
Change risk scoring rates the release elevated two days out, because this integration service has caused three incidents in a year. The team ships inside the window anyway. That release is now watched.
Four tools, one problem
Dynatrace flags rising response time. Kafka shows failed integration messages. Azure reports delayed jobs. ServiceNow opens fourteen tickets across three teams. They collapse into one problem with a ranked cause set.
Two hops to the real cause
Multi-hop reasoning traces the symptom to the deployment made two hours earlier, on a service owned by a different team than the one being paged.
Ranked by cost, cleared by rule
Order capture is slowing and today's shipment cutoff is at risk, so this outranks the two other open problems. Rules check risk tier, change window, blast radius and rollback policy. Rolling back inside the window is in policy, so it runs unattended.
The owning team's agent, not ours
The rollback runs through Azure DevOps under the owning team's credentials, through access that team already had. NuPulse holds none of its own.
Proof, then close
Order throughput recovers and downstream inventory updates resume. The fourteen tickets close against one fault. Had throughput not recovered, the rollback would have been reversed.
The last time it happens
The class is recorded with its evidence, and a pre-deploy check on that integration service is raised so the next release cannot repeat it.
What runs where
Language models only where the ambiguity is real.
Most of the work across hundreds of applications is high volume classification and forecasting, which needs no language model at all. Running one there would be slower, costlier and no more accurate.
Scroll the diagram sideways to read it.
Cost per resolved incident does not scale linearly with alert volume, because the volume is handled by models that cost a fraction of a language model call.
Rules return the verdict. Reproducible, auditable, and identical on the same inputs every time. Nothing probabilistic sits between diagnosis and action.
Large hosted, mid tier, or self hosted inside your own environment where data cannot leave the region. Routed per task, not chosen once.
Operating model
People and agents, side by side. No approval queue.
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.
A person asks, it answers
Question an application, a dependency or an objective in plain language and get a grounded answer with its evidence. No query in four tools first. Then delegate the work and keep watching it.
The person is in charge.
It decides and acts
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.
It hands over, with context
Novel, ambiguous or out of limits. The engineer gets a diagnosis and options, not a raw alert.
An exception path, not the main path.
Target systems
Built to read what you already run.
Read in place, through interfaces your platforms already publish. The target systems below are the ones customers ask about most; each is wired up per engagement, not shipped as a named-vendor integration.
Observability and logging
Service management and on call
Delivery and infrastructure
Applications and data
REST and GraphQL, MCP tools and servers, OpenTelemetry, Prometheus query, webhooks, syslog, JDBC and ODBC, queues, file drop. Adding a platform is configuration, not a build.
Mainframe, midrange, thick client and packaged apps with no API at all, reached through a governed adapter inside your environment, under the same policy and record.
Rate limits, not model quality. NuPulse indexes and compresses before it reasons, and carries a cost budget per run, so the economics hold at hundreds of applications.
Governance
Built so an automated action can be explained.
- Deterministic gating. Risk is scored by rule, in milliseconds, before any action is offered.
- Autonomy per agent, action and environment. Set by your team per application, never uniform across all of them.
- Separation of duties. An agent that proposes a financially controlled change cannot approve it.
- Decision lineage. Which rule, which evidence, which approver, which system wrote what.
- Read only first. Proposals graded against what the team actually did.
- Rollback and kill switches. Revocable instantly, per agent, by whoever runs it.
- Least privilege identity. Every agent scoped and revocable in one place.
Your cloud or on premises. Signals queried in place, residency kept in region. Model choice per task, including a self hosted model 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.
Against the baseline you already hold, on the measures listed under business outcomes. No number is claimed here that your own tooling cannot verify.
Getting there
Whichever stage you are on now, that is where we start.
Most teams are at or just below stage three. We begin from what you already run, measured against the baseline you already hold.
Get ahead of the failure
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.
Turn forecasts into action
You already know hours ahead. Forecasts become costed actions, and the classes you already trust start resolving inside the limits your team declares.
Take the parts, keep your product
Take the planner, rules engine, registry, memory and audit trail behind your own interface. Your platform keeps its name, roadmap and data.
Forward deployed engineers stand it up inside your SRE group and work alongside your team. Every application starts read only, with proposals graded against what your engineers actually did.
Recurring incidents close without anyone being paged. Novel ones arrive already diagnosed. Engineering time goes into removing classes of failure.
FAQ
Common questions
Does NuPulse replace our observability platform?
No. It sits above and beside the tools you already run, reads them in place, and does not become the system of record.
Who runs NuPulse, and who acts?
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.
Do application teams have to do anything?
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.
Which systems can it read from?
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 target systems section lists what customers ask about most often; each is wired up per engagement. The only case needing different handling is a system with no API at all, reached through a governed adapter.
Is this all language models?
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, such as root cause on a novel incident, reading runbooks and change notes, weighing options, and answering questions in plain language. That split is what keeps cost per resolved incident from scaling with alert volume.
What happens when an agent gets something wrong?
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.
How does NuPulse relate to NuStudio?
NuPulse is a product on the NuStudio governed 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.
Which industries is it built for?
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. That standard is useful everywhere, and the same product runs in consumer packaged goods, retail and distribution, manufacturing, logistics, energy, telecommunications, travel and the public sector. What matters is whether business processes cross more applications than one team owns.
One product on the NuStudio runtime.
The platform pages cover the semantic layer, the agent harness, governance at the reasoning level, and how forward deployed engineers work inside your environment.