Know what broke, what it cost, and what's next.
SRE consulting for teams that run critical systems. We connect your logs, ServiceNow and monitoring so every outage comes with its impact, its root cause and a postmortem — and so the next one is spotted before it happens.
The questions nobody can answer mid-outage.
The data usually exists, spread across logs, tickets and dashboards. What's missing is the connection between them. That's what we build.
Which services, which customers, which regions. Not just "something is red".
When it really started, how many transactions failed and how long users felt it.
An estimated financial impact, live, so management gets a number instead of a guess.
Which signals moved before it broke, and how to alert on them next time.
What we help you set up.
Each of these can be a standalone project. Most teams start with one or two and grow from there.
AI agent for operations
An AI agent that works across your integrations. It watches ServiceNow, opens and assigns tasks, checks the related logs, looks up how similar incidents were solved before and suggests a fix to the team.
Microsoft Foundry, ServiceNow, Log Analytics, Elasticsearch
Impact analysis
Map services, dependencies and business transactions, so an outage instantly shows what's affected, for how long and how many transactions failed.
Log Analytics, KQL, service maps
Financial impact of outages
Agree with the business on what a failed transaction or a minute of downtime costs, and show that number live during incidents and in reports.
Cost models, live dashboards
Root cause analysis
Structured RCA practice and log queries that get from symptom to cause faster, with findings written up the same way every time.
Log Analytics, runbooks, RCA templates
ServiceNow and postmortems
Incidents in ServiceNow enriched with log data, timelines built automatically and postmortems that write their first draft for you.
ServiceNow, Log Analytics
Dashboards people actually use
One view for engineers, one for management. Health, SLOs, impact and trends that answer real questions.
Grafana, Azure Workbooks, Azure dashboards
Alerting in Teams and Slack
Fewer, better alerts that arrive where your team works, with the impact and a link to the right dashboard already attached.
Microsoft Teams, Slack, Azure Monitor
Anomaly detection and early warning
Baselines on the metrics that matter, so you're warned when something starts drifting, not after it has already failed.
Log Analytics, Azure Monitor, Grafana
Every error grouped, counted and talked through.
Outages get attention. The thousands of small errors in between usually don't, until one of them becomes the next outage. We set up a routine that turns them into a short list your team actually works on.
Group similar errors
Thousands of log lines are clustered into a handful of error groups by message pattern, service and stack trace, so one bug shows up as one row, not 10,000.
Log AnalyticsKQLGrafanaSpot anomalies early
Every error group and key metric gets a baseline that knows your weekly rhythm. When something starts drifting, or a new group appears, it's flagged before customers notice.
Azure MonitorDynamic thresholdsNotify in Slack and Teams
One alert per error group, not one per log line. It lands in the right channel with the impact, the owner and a dashboard link, so on-call starts with context instead of noise.
SlackMicrosoft TeamsOn-call routingFind the root cause together
Deploys, metrics and error groups are lined up on one timeline. In a short review with your engineers we agree on the cause, assign an owner and track the action in ServiceNow or your backlog.
RCAServiceNowError reviewMeasure MTTR and verify
Each week shows time to detect, time to resolve and error trends, and whether last week's fixes really made the errors go away.
MTTDMTTRWeekly reportDrill into any error
Click an error and follow one real request step by step: which services it passed, where it slowed down and where it failed. Next to it, what the gateway saw one minute before and after, or five minutes when you need more context.
Distributed tracingApplication InsightsAPI gateway logs
Repeated every week or sprint
Production, last 7 days
- TimeoutException: payments-dbpayments, checkout61,204
- HTTP 502 from address-serviceorder-api38,770
- NullReference in InvoiceMapperbilling12,935
- Token expired: hr-sync jobintegration4,118
Errors per minute against a 7-day seasonal baseline
Routed by service owner, with impact attached
- Impact
- 3,418 failed transactions · €41,200 at risk
- Group
- #1 · 61,204 events, +340% vs baseline
- Services
- payments, checkout
- Owner
- Platform team · @platform-oncall
Timeline reviewed in Thursday's error review
- 13:52Deploy payments-api v4.12Root causeChange CHG-8810 · DB pool size lowered from 50 to 20
- 13:58DB connection pool at 100%Metric · Azure Monitor
- 14:03TimeoutException rate climbsError group #1 · payments, checkout
- 14:07Anomaly alert in #payments-oncallAlert · acknowledged 14:09
- 14:29Rolled back, errors back to baselineResolved · 22 min after detection
- Agreed
- Pool sizing check added to the release pipeline
- Owner
- Platform team · CHG-8812 in ServiceNow
Week 40, production, prepared for Thursday's error review
- Token expired: hr-sync job4,118 → 0Fix verified
- TimeoutException: payments-db61,204 → 9,880Improving
Click an error to see what happened around it
What you have when we're done.
Consulting, not a product. Everything is built inside your own environment and handed over to your team.
Working setup
Queries, dashboards, alert rules, integrations and AI pipelines, running in your tenant.
Documentation and runbooks
How it works, how to change it and what to do when an alert fires.
A team that can run it
Hands-on sessions so your engineers can extend it without us.
No licences, no lock-in. We don't sell a platform. We use what you already pay for, plus open-source where it helps.
How an engagement works.
Small steps with something useful at the end of each one.
- Week 1
Intro and review
We look at your monitoring, logs, ServiceNow setup and a few recent incidents.
- Week 2
Plan
A short proposal: what to build first, what it will answer and what it takes.
- Weeks 3 to 8
Build
We build it with your team, in your environment, and show progress every week.
- At the end
Hand over
Documentation, training and a final review. Follow-up support if you want it.
Questions teams ask us.
Do we have to buy a product or platform?
No. We're consultants. We build on the tools you already have, such as Azure, ServiceNow and Grafana, and you own the result.
Does our log data leave our environment?
We work within your existing security and compliance setup. The AI parts are built on Microsoft Foundry, and we agree with your team up front which data the models can access.
How do you calculate the financial impact?
Together with your business owners we agree on a value per transaction or per minute of downtime for each service. We then combine that with real failure counts from your logs.
What happens in an error review meeting?
A 30 to 45 minute session, weekly or once per sprint. We go through the top error groups from the report, agree on likely causes, assign owners and check whether last week's fixes worked. We run the first few sessions and then hand the routine over to your team.
We're not on Azure. Can you still help?
Yes. Log Analytics and Azure Workbooks are common, but the same approach works with Grafana, Prometheus, Loki and other stacks.
How big does a project need to be?
It can start small, for example one Teams alert with impact attached, or one postmortem template in ServiceNow. There's no minimum contract.
Let's look at your last outage together.
Bring one recent incident to a free 45-minute call. We'll show what your data could already tell you about it, and what it would take to get there.
- Engineer to engineer, no sales pitch
- A short written summary afterwards
- Free, with no obligation