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AI Log Analysis
Weeks of scattered logs turn into a short list of the error patterns and regressions actually worth fixing.
Logs hold early warnings, but nobody has time to read weeks of them. AI Log Analysis reads your application and pipeline logs on a schedule and surfaces the error patterns, spikes and regressions worth a human look. It ties each new error to the recent commit that likely caused it, and posts findings to your tracker, so problems get fixed before customers notice.
- A short list of error patterns worth fixing
- New errors tied to the commit that likely caused them
- Findings posted to your tracker on a schedule you set
$2,900
20–30 working days
Optional maintenance $249/mo
What it is
AI Log Analysis is an agent that reads your application and pipeline logs on a schedule, and surfaces the error patterns, spikes and regressions worth a human look. It correlates what it finds against recent commits, so a new error is tied to the change that likely caused it. Findings go to your tracker for review.
Why businesses buy it
Most outages announce themselves in the logs first, as a rising error or a new warning after a deploy. Without someone watching, those signals turn into customer complaints and emergency fixes. AI Log Analysis does the watching for you and points to the likely commit, so your team fixes regressions early, while the change is fresh and the fix is small. It is an affordable first step toward better reliability. The Scope Card fixes the log source, connected systems, rules and trial before you order, so the scope is clear.
Problems this solves
Nobody has time to read application logs until something breaks
The agent reads your application and pipeline logs on a schedule. It surfaces the error patterns, spikes and regressions worth a human look, so problems show up before customers report them.
A new error appears and nobody knows which change caused it
Findings are correlated against recent commits, so a new error is tied to the change that likely caused it. Engineers start their investigation from a named commit rather than a guess.
Log alerts are either too noisy or too quiet to trust
Instead of alerting on every line, it reports the patterns that stand out. Findings go to your tracker for review, where your team decides what needs action.
A good fit if
Your logs pile up across AWS, GCP or Azure faster than anyone reads them, and real regressions get lost in the noise.
You want a standing report of recurring errors and slow patterns, not just an alert the moment something is already on fire.
You already ship through GitHub, GitLab or Bitbucket and want new error patterns tied back to the commit that likely introduced them.
You want findings posted as a comment or ticket your team reviews on its own schedule, not another dashboard nobody opens.
Not the right fit if
You need alerts grouped and triaged the moment they fire, not analyzed on a schedule. See AI Incident Triage, built for that.
You want someone to ask it questions live during an active incident. See AI On-Call Assistant, which is built for that conversation.
You need more than one language or stack covered by the same analysis pass. This build covers one stack; a second is priced separately.
You need more than two connected systems, for example three separate log sources feeding one report. Add a note at checkout for a quote, or use our Custom AI enquiry form.
How it works
- Logs are pulled on a schedule. It reads from the repository, pipeline or cloud logs you connect, on the cadence you agree during setup.
- Patterns are surfaced, not just single errors. Recurring exceptions, latency creep and rising error rates are grouped so one trend reads as one finding.
- Each pattern is checked against recent commits. When an error's timing lines up with a deploy, the likely commit is named in the finding.
- Regressions are ranked by how much they changed. A pattern that tripled overnight is flagged ahead of one that has always been there.
- Findings are posted for review. A written summary lands as a comment or ticket in the tool your team already uses.
- Nothing is changed in production. It reads and reports; a person decides what, if anything, gets fixed and when.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- Your business information (company basics, hours and brand voice), shared during intake, or confirmation we can reuse what you gave us on an earlier order.
- Scoped access to the repository or pipeline whose logs this build reads, as an app install or a limited token.
- Your CI/CD configuration, so the analysis understands what a normal build or deploy looks like.
- Your coding standards or runbooks, so surfaced patterns are described the way your team already talks about them.
- Access to the tracker or chat tool where findings should be posted, such as Jira, Linear or Slack.
- Admin access and API keys for every connected system, shared by adding our team as a user or creating a key you control, never by email.
- One person on your side who can approve decisions during setup and sign off at launch.
- An API key for your own AI model account (for example OpenAI or Google Gemini), created for this build with a spending limit.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
Intake verified, log sources and tracker access confirmed.
Weeks 1–2
Pattern detection, ranking and commit correlation built.
Week 3
Tracker and chat connections wired in.
Weeks 3–4
Trial run on 20 pull requests or pipeline runs, results shared with you.
Week 4
Your review and two rounds of changes.
Go-live
Live, recorded handoff session, support starts.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $2,900 |
| Express | 15–25 working days | $3,400 |
| Rush | 10–20 working days | $3,900 |
What you receive at handover
- The log analysis agent reading logs from one repository or pipeline on a schedule
- Connection to your Git host and one tracker or chat tool
- Configuration of what counts as a finding, based on your runbooks
- Test report from a trial on 20 pipeline runs
- Admin guide and recorded 45-minute handoff session
- 30 days of post-launch support
Expected benefits and how to measure them
- Can surface regressions earlier, while the change is still fresh.
- Helps engineers spend less time scrolling raw logs.
- Issues found before customers report them
- Compare tracker issues raised from log findings with customer-reported errors, two months before and after launch.
- Time to link an error to a change
- Record how long it takes to name the causing commit for new errors, before launch and after.
- Findings acted on
- Count the agent's tracker findings each month and how many led to a fix.
Before you start: List the errors customers reported last month and note how each was traced to its cause.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
The build price is one-time. Running it costs roughly $20 to $200 a month in your own accounts, covering AI model usage and CI minutes, and it scales with your log volume.
AI usage during the build and testing is also billed to your accounts, so you keep ownership of every provider relationship. Nothing runs through us.
Ask about running costsCustom requirements
If you need something outside the Scope Card, add a note at checkout. You get a written Change Quote, and that work only starts once you accept it. Your original order runs on its normal timeline either way. Larger or bespoke projects go through the Custom AI request form.
Request a custom versionKeep it running
We recommend monthly maintenance: monitoring, fixes, and monthly change hours, enough to add a new log source or retune what counts as a regression. Maintenance starts after the 30 days of support, bills monthly, and can be cancelled any month.
$249/mo (the minimum fee, includes 2 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A log analytics SaaS charges by ingest volume, and costs climb as you log more | A custom agency quotes after discovery calls | Ours is one fixed price, shown before you buy |
| Scope | A log analytics SaaS is a general platform you configure yourself | An agency agrees scope during the project | Ours is written on the Scope Card before purchase |
| Delivery | A log analytics SaaS is live once you finish onboarding and dashboards | An agency usually takes several weeks, set per project | Ours has a fixed delivery time shown before you buy, with faster options |
| Commit correlation | A log analytics SaaS shows you the spike, not the likely cause | An agency build depends on what was agreed | Ours names the commit that likely lines up with the pattern |
Questions about this product
What does the AI Log Analysis build include?
Everything on the Scope Card: scheduled log reading for one repository or pipeline, error pattern and regression detection, links to the likely commit, and findings posted to your tracker, the trial run, two rounds of changes and the recorded handoff session. The price is fixed and shown on this page before you order, and it does not change once your Scope Card is confirmed. AI usage is billed to your own accounts.
How much does it cost to run AI Log Analysis each month?
Roughly $20 to $200 a month, covering AI model usage and CI minutes in your own accounts. The figure depends on how much log volume it processes. Monthly maintenance is separate and optional if you want ongoing changes handled for you.
When does AI Log Analysis go live?
Delivery runs on a fixed schedule, and the delivery panel shows the standard date plus express and rush options before you order. The clock starts once your log sources and tracker details are confirmed and your intake is complete, not the day you place the order, so sending them early keeps the date.
What log data can AI Log Analysis actually read?
Whatever your repository or pipeline already produces on AWS, GCP or Azure, plus your CI/CD configuration so it recognizes a normal build. It reads existing logs; it does not add new logging to your application. Every credential it needs is shared by adding our team as a user or creating a key you control, never by email.
Can AI Log Analysis add a second log source later?
Yes, as an add-on scoped and priced separately, since the standard build covers one repository or pipeline as its source. Small tuning, like adjusting what counts as a regression, fits inside the monthly change hours that come with maintenance. Maintenance also covers monitoring and fixes, starts after post-launch support, and can be cancelled any month.
Can I cancel the AI Log Analysis maintenance?
Yes, in any month. Plans bill monthly with no lock-in, and the build itself keeps running whether or not you have one; cancelling only stops the included monitoring, fixes and hours. The 30 days of post-launch support that come with every order continue regardless.
Who owns the findings AI Log Analysis produces?
You do. The reports, configuration and correlation rules built for you belong to your business, and the logs themselves never leave your own connected accounts. If maintenance lapses, the build keeps analyzing; you just lose the included monthly changes and support.
Does AI Log Analysis change anything in my application automatically?
No. It reads logs, groups patterns and posts findings as a comment or ticket; it never edits code, redeploys, or changes configuration on its own. A person reviews every finding and decides what, if anything, to fix and when to ship it.
Turn weeks of logs into a short list of what matters.
Order AI Log Analysis and on a fixed delivery date it starts surfacing error patterns and regressions, each tied to the likely commit.
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