DCT

Turning alerts into AIOps you can act on

We build the layer that reads across the monitoring you already run and hands an engineer a probable cause with its evidence - not another alert.

Talk to a DCT AI EngineerSee an incident get diagnosed ↓

  • Why

    not just what, and not just when

  • One

    picture across cloud, containers, databases and apps

  • Zero

    monitoring tools replaced

  • 24/7

    governance and compliance visibility

One incident, fourteen alerts

Recovery time is mostly diagnosis time

A well-monitored environment that recovers slowly is not an alerting problem. The signal arrived on time; the reasoning between signal and action was a person, awake at 2 am, opening five tools in sequence. Adding more monitoring makes that worse, because more alerts produce less attention. The work is doing the correlation first and sending a conclusion.

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14 ALERTS · ONE INCIDENTEvery layer fires. None ofthem names the cause.Learn patternsper service, per environmentCorrelateacross layers and timeRank causeswith confidence and evidenceRecommendthe next action, not an alertIT ACTS ONLY WHEREYOU AUTHORIZED ITPROBABLE CAUSE · CONFIDENCE 0.86Connection pool exhausted - db-primary, since 02:1402:11 deploy #2291 · pool size 20 → 802:13 pool saturation 100% · queue depth rising02:14 checkout-api p99 breaches its objective02:14 13 downstream alerts fire · all symptomsResolvedoutcome tunesthe baselineWhat was actually wrong last time sharpens what counts as normal
Active stage

Learn normal

What counts as anomalous has to be learned, not guessed. Baselines are built per service and per environment, including the shape of a normal week, and re-learned as the service changes - which matters most on container platforms where the topology is different by the afternoon.

01 - Signal quality

Alert volume is a design failure, not a thoroughness metric

Google's own reliability guidance points out that you can receive 144 alerts a day, act on none of them, and still be meeting your reliability target. An alert is only worth a person's attention if it represents something a person needs to do. Judge alerting the way you would judge any classifier - how many of the things it fires on matter, how many things that mattered it missed, how fast it fires and how fast it stops.

  • Page on what customers feel, not on causes
  • Fewer signals as an explicit design goal
  • Symptoms attached as evidence, not sent separately
02 - Learn patterns

A threshold is a guess somebody made once

Most thresholds are set on the day a service launches and never revisited, which is why they fire during every Monday morning peak and stay silent through the slow degradation that actually matters. Normal is learned per service and per environment, including the shape of a week, and re-learned as the service changes - which matters most on container platforms where the topology is different by the afternoon.

  • Baselines per service and environment
  • Weekly and seasonal shape accounted for
  • Re-learned as topology and traffic change
03 - Evidence

A conclusion you cannot check is just a louder alert

Every probable cause arrives with the timeline that produced it - the change, the saturation, the moment the objective broke - so an engineer can confirm or discard it in seconds rather than taking it on faith. And a confidence number that is never wrong is a confidence number nobody is measuring: hypotheses are scored against what the incident actually turned out to be.

  • The timeline that produced the conclusion
  • Confidence measured against outcomes
  • Automatic action only where authorized
Same signals · A different discipline entirely

Watching a system, and understanding one

  • An alert says a threshold was crossed
    A hypothesis says what probably caused it
  • Fourteen alerts for one incident, all sent to a person
    One incident, with the other thirteen attached as evidence
  • Diagnosis starts with opening five dashboards
    Diagnosis starts from a ranked cause and its timeline
  • Thresholds guessed at launch and never revisited
    Normal learned per service, and re-learned as it changes
  • Slow degradation stays under the threshold until it isn't
    Emerging patterns raised while there is still time to act
  • Cloud spend rises and nobody can attribute the increase
    Consumption anomalies tied back to the change that caused them
  • Real signals get dismissed because most of them are noise
    Fewer things reach a person, so the ones that do get read
  • Compliance evidence assembled in the week before a review
    Visible continuously, as a property of running the environment
Claude Preferred Partner

Claude partner Network

In the reasoning step, not the detection step. Anomaly detection is statistics and should stay statistics - it is cheaper, faster and easier to defend. What a model is good at is reading a change description, a log excerpt and a dependency path together and proposing what connects them. DCT builds on Claude.

  • Detection stays statistical

    Baselines and anomaly scoring are maths, not inference - cheap enough to run on everything, all the time.

  • Reasoning across the evidence

    The deploy note, the log excerpt and the dependency path read together, which is the part a person was doing at 2 am.

  • Written for the person on call

    A hypothesis and a next action in plain language, with the timeline behind it - not a chart to interpret.

  • Cost per incident

    Inference runs on correlated incidents, not on every metric, so cost tracks incidents rather than telemetry volume.

Case Study

Well monitored, and still slow to recover

  • Enterprise technology

    From dashboard archaeology to a ranked cause with its evidence

    The environment produced continuous operational data across infrastructure, applications, cloud platforms, databases and services. That was never the problem. Reactive monitoring could establish that something had gone wrong; understanding why meant a manual investigation across several tools, and recovery time was dominated by diagnosis rather than by the fix.

    DCT built a layer that continuously analyses signals across the estate, correlates them to identify probable cause, raises emerging risk before it escalates, and returns a recommended action to the teams who have to take it - with governance and compliance visibility running continuously rather than assembled for a review.

    • One

      correlated picture across the estate

    • Zero

      monitoring tools replaced

    • Cause

      surfaced with its evidence, not just symptoms

    The change was direction, not volume. Adding monitoring to a complex estate produces more alerts, and more alerts produce less attention. Doing the correlation first and pushing conclusions is what converts observability spend into recovered engineering hours - and moves an operations function from firefighting toward prevention.

Common questions

What platform and reliability leaders ask first

DCT ObserveIQ is delivered by DCT AI engineers working inside your team, on top of the telemetry you already collect. These are the questions that come up before anyone signs anything.

Does DCT ObserveIQ replace my existing monitoring tools?

No, DCT ObserveIQ sits on top of the monitoring you already run: cloud telemetry, container platforms, database and application monitoring, log aggregation, tracing. Your dashboards keep working and your team keeps the tools they know.

Replacing a monitoring stack is a year of work with no outcome at the end of it. The gap DCT ObserveIQ closes is not collection; it is the reasoning between a signal and an action.

Will AIOps increase our alert volume instead of reducing it?

No, DCT ObserveIQ is built to send fewer alerts, not more. Fourteen alerts from one incident become one incident with thirteen pieces of evidence attached. Reducing what reaches a person is a design goal, measured the same way accuracy is.

If a deployment increased the volume of things demanding attention, it would have failed, regardless of how accurate the underlying analysis was.

How does DCT ObserveIQ determine root cause, and how accurate is it?

DCT ObserveIQ produces a ranked hypothesis with the evidence behind it: the change that preceded the incident, the saturation that followed, the moment the customer-facing metric broke. An engineer confirms or discards it in seconds because the reasoning is visible, not asserted.

Accuracy is measured, not claimed. Every hypothesis is scored against what the incident turned out to be, that is the number DCT is held to. A confidence score nobody checks is decoration.

Does DCT ObserveIQ take automated action, or only recommend?

DCT ObserveIQ only acts where you have explicitly authorized that specific action on that specific service. The default is recommend and stop.

Most teams start with zero automated actions, move a small number of well-understood, reversible actions to automatic once recommendation quality has been observed, and never automate anything they couldn't trivially undo.

Does dynamic baselining work in Kubernetes and other constantly-scaling environments?

Yes, DCT ObserveIQ learns baselines per service rather than per instance, and re-learns them as topology moves, so a service scaling from four replicas to forty reads as a normal event, not an anomaly. Static thresholds are what break in this environment, which is much of why they get ignored.

Dynamic topology is also where correlation earns its cost: the relationship between a pod restart, a saturated connection pool, and a customer-facing latency breach isn't visible in any single tool.

Can DCT ObserveIQ identify the cause of cloud cost spikes, not just incidents?

Yes, the same correlation engine applies to consumption. A cost increase is treated as a signal like any other, and the output isn't a chart showing spend went up, it's the specific deployment, environment change, or scaling event responsible.

That's the difference between a cost report and something an engineer can act on.

Start by splitting your recovery time in three

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