Stop getting faster
at fixing the same failure.
Recovering faster each time is progress. Never seeing it again is the point. NuPulse predicts the failure, prescribes the fix, and acts inside the limits your team sets.
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, and then removes the class of failure so the work does not come back.
NuPulse runs inside your SRE and operations group. Your team onboards applications, runs the agents, and sets what each one may do, using the operational access the team already has. It sits beside your existing tools and never becomes a system of record.
Models propose.
Your team owns both.
Nothing copied.
Nothing acts until you say so.
not inferred.
not forty tickets.
Maturity
Stage three tells you. Stage four does something about it.
Most enterprises have made real progress to stage three. The step after it is not a data problem.
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 stages, one loop, and it gets smaller each time round. 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. Cross-application paths traced, not guessed.
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 components your team runs.
Not a black box that answers questions. A set of parts your engineers configure, inspect and switch off individually.
Reads and compresses before it reasons
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 keeps cost flat as alert volume rises.
Ownership and dependency, declared
Who owns what, and what depends on what. Added when a question needs more than one hop, not before. Declared rather than inferred, so it does not decay silently.
Plans the run and re-plans
Reads the signal, calls the specialists it needs, re-plans after each step, and produces costed options with cited evidence.
The deterministic verdict
Risk tier, action limits, change window, run budget, separation of duties. Your team writes the limits. Model output cannot overrule them.
Every agent scoped and revocable
One place to see every agent, what it may touch, which version is live, and a kill switch on each. Least privilege by default.
What worked last time
Per application and per class, in your tenant. The reason the tenth occurrence is cheaper to handle than the first.
The trail your auditor asks for
Which rule fired, which evidence was cited, which action ran, what the result was. Retrievable per decision, not reconstructed from logs.
What is running today
The planner, registry, memory, policy chains, identity and access, model routing and audit search are in production on the NuStudio runtime. The application map and per-decision lineage retrieval are being built for NuPulse now.
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 amber services, one upstream of today's order intake. No longer a judgment call.
Objectives, error budgets, burn rate, golden signals, toil, MTTD and MTTR, read from where you already define them.
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 and rule level decision lineage retrieval are specified interfaces rather than shipped capability today.
What runs where
Three techniques, and the expensive one is used least.
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.
Integrations
If it has an API, it connects.
Read in place, through interfaces your platforms already publish. The systems below are what customers ask about most, not a limit.
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. Rules score risk, not the model, and cannot be overruled by it.
- 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, on premises, or air gapped. 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 your existing baseline: MTTD and MTTR, share of incidents closed with nobody involved, repeat rate per class, escalation rate, tickets linked per fault. False positive rate published alongside, not netted out.
Getting there
Wherever you are on the six stages, that is the starting point.
Nobody starts at stage one. Engagement begins from what already exists, measured against the baseline you already hold.
Add the decision and the action
Your signals and models stay as they are. The rules engine, action path, verification and record are what get added.
Move to prescription
Forecasts become costed actions, and classes you already trust start resolving inside limits your teams declare.
Take the components, keep the 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 integrations section lists what customers ask about most often and is not a limit. 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.