SynaptoreAI lab

Operational Resiliency Engine

Find the change worth investigating.

Synaptore is operational investigation software in development for teams running critical digital services. We’re building it to learn patterns in logs and metrics, connect unusual behaviour across services, and give your team a lead with evidence and a next check.

Discuss your use case Explore use cases

Anomaly detection is the foundation. The connected workflow is in development.

Your operational signalsLogs Metrics
Synaptore engineThe intended path to an investigation brief
  1. 01

    Detect.

    Find departures from learned patterns.

    Database eventsUnusual pattern
  2. 02

    Connect.

    Add timing and service relationships.

    DatabaseTimeouts appearAPIRetries appearCheckoutResponses slow
  3. 03

    Inform.

    Present evidence and a next check.

    A lead for your operatorA lead to test: database timeouts.Check database health, recent changes and demand against the related API retries.
Your team investigates and decides how to respond.
Illustrative workflow · integrated engine in development

For operations and reliability teams
responsible for critical digital services.

Existing logs and metricsA service to investigatePeople who can act
01 / THE PROBLEM

The data is there.
The investigation is still yours.

A service slows down. You have logs, charts and alerts, but still need to work out what changed, which observations belong together, and where to begin.

Synaptore’s focus is that investigation gap: learning what usual behaviour looks like, finding a departure, and developing the context that makes it useful to an operator.

Why we’re building Synaptore

Logs 12,480 lines / min

  1. INFOauth session refreshed
  2. WARNpayments request timeout 3000ms
  3. ERRORapi-gw GET /orders 500
  4. WARNinventory retrying db call
  5. INFOdeploy config reloaded
  6. ERRORcheckout upstream 503
  7. WARNpayments pool wait 1200ms
  8. INFOcache evicted 2,301 keys

Latency p95 1.8 s

Alerts 3 firing

  • Latency p95 above 1 sFiring
  • 5xx rate above 2%Firing
  • Checkout errors risingFiring

Missing: the observation that ties them together

  • What changed?
  • Which observations belong together?
  • Where to begin?
Illustrative signals from a slowing service.
02 / FROM PATTERN TO LEAD

A concrete example

The order of events
can change the picture.

Logs record system events. Metrics track measurements over time. When several services fail together, the order in which things changed is often the best clue.

ILLUSTRATIVE CHECKOUT INCIDENT

Four services fail. One change came first.

  • Checkout503 for customers
  • API gateway5xx errors
  • Auth service312 failed logins/min
  • PaymentsTimeouts
  • InventoryCannot reach DB
  • DatabasePool 100/100
  1. Failed logins surge
  2. Database pool fills
  3. Services fail
  4. Checkout returns 503

Invented observations showing the intended workflow. Timing and service relationships suggest a lead; they do not prove its cause.

Explore this use case in more detail Explore more use cases
03 / FOUNDATION & DIRECTION

How the work fits together

Learn the behaviour.
Make the finding useful.

Our technical work starts with representations of log events, event sequences and metrics. Models use these to look for departures from a reference for usual behaviour.

The wider engine is being developed to connect observations and present the supporting context, so people can test a lead and decide how to respond.

Explore the technical approach
Technical foundation

Patterns in logs and metrics.

Work on event representations, sequence modelling and anomaly detection provides the starting point. A signal identifies behaviour to examine.

Log event sequence learned templates

Metric reference for usual behaviour

Illustrative: two departures flagged for review.
In development

A connected investigation workflow.

Grouping related observations, adding service context and shaping an investigation brief are the direction of the integrated engine.

  • Detected anomalies
  • Runbooks & docs
  • Service topology
  • Change records
  • Past incidents
Investigation agentLanguage models gather and connect the context

Draft investigation brief

Lead
Database pool exhaustion
Evidence
Logs, metrics, recent change
Next check
Pool usage by service
Illustrative direction of the integrated engine, in development.
How we would evaluate usefulness

Compare with existing monitoring and simple baselines: missed events, alert burden, useful investigation leads and integration effort. A more complex model must earn its place in the workflow.

04 / START WITH ONE SERVICE

The conversation we can have today

Bring one operational problem.

Start with a technical discovery conversation. We’ll discuss the service, available signals and investigation problem before considering an evaluation.

  1. 01

    A service that matters.

    Choose a digital service whose disruption affects customers or operations. Where does investigation get difficult?

  2. 02

    Signals you already collect.

    Discuss your logs, metrics and existing monitoring, including what can be accessed and what context is missing.

  3. 03

    A useful comparison.

    Identify the question an evaluation would need to answer. Any follow-on scope depends on the data, constraints and fit.

We’re looking for design partners

Help shape Synaptore.
Start with your service.

We’re looking for operations and reliability teams to develop and evaluate Synaptore with us around a real service and a concrete investigation challenge.

You bring knowledge of your systems, relevant logs and metrics, and feedback from your team. We work directly with you to shape the investigation workflow and assess its usefulness against your existing approach.

Together, we agree on scope, data access and what success would look like. Start with a high-level description of your service and what your team struggles to investigate.

Discuss a design partnership info@synaptore.ai