Operations
AI Incident Triage for GitHub
Groups alerts, finds likely causes and drafts incident updates, connected natively to GitHub so it reads recent commits, pull requests, deployments and…
Operations
AI On-Call Assistant
Whoever is on call asks a question in chat and gets the runbook, the recent changes and a next step, mid-incident.
At 3 a.m., the on-call engineer should not have to hunt across five tabs for the right runbook. AI On-Call Assistant answers questions in your team chat the moment an incident starts. It pulls the relevant runbook, recent pull requests and similar past incidents, and replies in plain language with a suggested next step. Your engineer stays in control of every action.
- Runbooks and recent changes answered in chat, mid-incident
- Similar past incidents surfaced with what fixed them
- Junior engineers handle on-call with more confidence
$2,900
20–30 working days
Optional maintenance $249/mo
What it is
AI On-Call Assistant is an agent that whoever is on call can question, in chat, the moment an incident starts. It pulls the relevant runbook, recent pull requests and past similar incidents, and answers in plain language instead of sending someone hunting across five tabs. It never changes production itself.
Why businesses buy it
On-call is stressful, and it is hardest for newer engineers who do not yet know where everything lives. Every minute spent searching runbooks and deploy history extends the outage and wakes up more senior people. AI On-Call Assistant puts that context in one chat reply, so incidents resolve faster, fewer people get paged, and on-call duty becomes something the whole team can share. The Scope Card fixes the repository, connected systems, runbook knowledge and trial before you order, so you know exactly what it can see and answer.
Problems this solves
The on-call engineer hunts across five tabs while an incident runs
Whoever is on call can question the assistant in chat the moment an incident starts. It pulls the relevant runbook, recent pull requests and past similar incidents into one plain-language answer.
Junior engineers dread being on call for unfamiliar systems
They can ask simple questions, like what changed recently or what fixed this last time. Answers come from your own runbooks and history, which makes a first rota less daunting.
Lessons from past incidents are forgotten by the next one
Past similar incidents are part of what it searches. The fix that worked before is surfaced when the same symptoms return, and it never changes production itself.
A good fit if
Your on-call rotation spends the first ten minutes of every incident just finding the runbook and the last related deploy.
You already keep runbooks and past incident notes somewhere searchable, like a wiki, a repo or a tracker, and want them surfaced automatically.
You want an engineer to be able to ask a follow-up question mid-incident and get an answer, not just a static document.
Your team pages into Slack, and you want the assistant to work where the incident conversation already happens.
Not the right fit if
You want alerts grouped and triaged automatically before anyone asks a question. See AI Incident Triage, which does that first pass.
You want ongoing pattern analysis across weeks of logs, not answers during a live incident. See AI Log Analysis, built for that.
You need more than two connected systems, for example a separate wiki, tracker and chat tool all at once. Add a note at checkout for a quote, or use our Custom AI enquiry form.
You run more than one language or stack and need on-call context across all of them together. This build covers one stack; a second is priced separately.
How it works
- An engineer asks a question in chat. "What changed in checkout in the last day?" or "do we have a runbook for this" both work as a starting question.
- It searches your runbooks and docs first. Answers come only from the coding standards, runbooks and documents you approved, so nothing is improvised.
- It checks recent pull requests and deploys. If the timing lines up with the incident, the relevant commit and its author are named in the answer.
- It surfaces similar past incidents. Where a comparable incident has happened before, it links the ticket and what fixed it last time.
- It answers, then keeps listening. A follow-up question in the same thread gets a follow-up answer, so the conversation stays useful as the incident develops.
- It never takes action itself. It answers and points to context; a person still restarts services, rolls back deploys and closes the incident.
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 this build reads, as an app install or a limited token.
- Your CI/CD configuration, so recent deploys can be matched to an incident's timing.
- Your coding standards or runbooks, so answers reflect how your team actually handles an incident.
- Access to the chat tool your on-call rotation pages into, such as 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, repository and chat access confirmed.
Weeks 1–2
Runbook indexing, deploy correlation and chat responses 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 on-call assistant available in your team chat
- Connection to your Git host and one tracker or chat tool
- Your runbooks loaded into its configuration, with guardrails
- Test report from a trial on 20 runs or incident scenarios
- Admin guide and recorded 45-minute handoff session
- 30 days of post-launch support
Expected benefits and how to measure them
- Can shorten the time from page to first useful action.
- Helps less experienced engineers handle on-call with more confidence.
- Time to first action
- Compare time from page to the first recorded mitigation step in your incident notes, before launch and after.
- Escalations to senior engineers
- Count incidents where on-call needed to wake or call a senior colleague, per month, before and after.
- Answer usefulness
- After each incident, ask the responder whether the assistant's answer helped, and track the tally.
Before you start: Review recent incidents and note time to first action and how often senior engineers were pulled in.
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 how often the on-call rotation asks it questions.
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
Optional monthly maintenance covers monitoring, fixes, model and platform updates, a monthly health report and monthly change hours, enough to add new runbooks or retune which incidents count as similar. It 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 knowledge-base chatbot charges per seat regardless of how often on-call actually uses it | A custom agency quotes after discovery calls | Ours is one fixed price, shown before you buy |
| Scope | A knowledge-base chatbot is a general search tool you configure yourself | An agency agrees scope during the project | Ours is written on the Scope Card before purchase |
| Delivery | A knowledge-base chatbot is live once you finish indexing your documents | An agency usually takes several weeks, set per project | Ours has a fixed delivery time shown before you buy, with faster options |
| Incident context | A knowledge-base chatbot answers from documents alone, without checking recent deploys | An agency build depends on what was agreed | Ours also checks recent pull requests and past similar incidents |
Questions about this product
What does the AI On-Call Assistant build include?
Everything on the Scope Card: a chat assistant for your on-call rotation, runbook and recent pull request lookup for one repository, past incident matching, and answers posted in your team chat, 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 On-Call Assistant each month?
Roughly $20 to $200 a month, covering AI model usage and CI minutes in your own accounts, not ours. The figure depends on how many questions the on-call rotation asks each month. Monthly maintenance is separate and optional, and its price is shown on this page.
When does AI On-Call Assistant 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 runbooks and chat access are confirmed and your intake is complete, not the day you place the order, so sending them early keeps the date.
What does AI On-Call Assistant need access to?
Your repository or pipeline, your CI/CD configuration, and the runbooks or docs you want it to answer from, plus the chat tool your rotation pages into. It never needs production database access. Every credential is shared by adding our team as a user or creating a key you control during intake, not by email.
Can I add more runbooks to AI On-Call Assistant later?
Yes. Adding new runbooks or updating existing ones fits inside the monthly change hours included with maintenance. Connecting a second chat tool or repository is a bigger change and gets a Change Quote before any work starts. Those hours also cover retuning which incidents count as similar, and maintenance can be cancelled any month.
Can I cancel AI On-Call Assistant maintenance?
Yes, in any month. Plans bill monthly with no lock-in, and the build itself keeps answering questions either way; cancelling only stops the monitoring, fixes and included hours. The 30 days of post-launch support that come with every order continue regardless.
Who owns the AI On-Call Assistant setup once it is built?
You do. The indexing, prompts and rules built for you belong to your business, and every connected account runs under your own credentials, not ours. If maintenance lapses, the build keeps working; you simply lose the included monthly changes and support.
Will AI On-Call Assistant ever restart a service or make a change itself?
No. It answers questions and surfaces context during an incident; it never restarts a service, rolls back a deploy, or closes a ticket on its own. Every action beyond answering a question needs a person on your team to carry out separately, exactly as before.
Give whoever is on call an answer, not five tabs.
Order AI On-Call Assistant and on a fixed delivery date it starts answering runbook and deploy questions in chat, mid-incident.
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