Patterns in logs and metrics.
Work on event representations, sequence modelling and anomaly detection provides the starting point. A signal identifies behaviour to examine.
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.
Anomaly detection is the foundation. The connected workflow is in development.
Your operational signalsLogs Metrics
Synaptore engineThe intended path to an investigation brief
- 01
Detect.
Find departures from learned patterns.
Database eventsUnusual pattern - 02
Connect.
Add timing and service relationships.
DatabaseTimeouts appear→APIRetries appear→CheckoutResponses slow - 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.
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 Synaptore02 / FROM PATTERN TO LEAD
A concrete example
The order of events
can change the picture.
Logs record system events. Metrics track measurements over time. Looking at behaviour gives an investigation more context than a single observation.
ILLUSTRATIVE DATABASE SEQUENCES
What changed?
Reference pattern
Query starts → completes → connection released
Changed pattern
Query starts → times out → repeated retry
The changed sequence is a clue. The next question is whether other observations help explain it.
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 approachA connected investigation workflow.
Grouping related observations, adding service context and shaping an investigation brief are the direction of the integrated engine.
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.
- 01
A service that matters.
Choose a digital service whose disruption affects customers or operations. Where does investigation get difficult?
- 02
Signals you already collect.
Discuss your logs, metrics and existing monitoring, including what can be accessed and what context is missing.
- 03
A useful comparison.
Identify the question an evaluation would need to answer. Any follow-on scope depends on the data, constraints and fit.
Let’s make the use case concrete
Which service would
you start with?
A high-level description is enough: what you operate, the signals you have, and what your team struggles to investigate.
Discuss your use case info@synaptore.ai