// for engineering teams

Engineering velocity shouldn't be blocked by ops overhead

The engineering coordinator you hire will scale the team better if we automate the triage layer first. PR reviews, incident routing, ticket classification - handled by the system. Your team ships, not sorts.

From $2,000·2-4 weeks to ship·80+ automations built·Official Anthropic Partner
See what we'd automate in your stack
48 hrs
average PR review time cut to under 8 hours
1 hr
incident report drafted vs half-day manual
80%
of support tickets routed correctly on first assignment
2-4 wks
to build and ship the full system
// what we fix

The four problems that kill engineering ops

PRs sit in review queue for days
New PR opened? Automatically assigned based on file ownership and reviewer load. Reminder sent if no review in 24 hours. Stale PRs surfaced weekly. Queue managed without a process meeting.
Incident reports are always late
Incident resolved? Timeline pulled from your monitoring and Slack, root cause structured, report drafted and sent to stakeholders within the hour. Post-mortem template pre-filled.
Support tickets go to the wrong person
Ticket in with technical detail? Classified by type, severity, and component automatically. Routed to the right squad. Priority set. No manual triage, no Slack back-and-forth.
Documentation is always out of date
API change merged? Relevant docs section flagged for update, draft generated from the diff, assigned to the owning team. Documentation debt tracked and surfaced weekly.
// what we build

A full engineering ops layer, built in 2-4 weeks

Not a tool subscription. Not a consultant with a deck. An actual system running in your stack, doing the work.

PR assignment and review reminder system from file ownership rules
Incident report generation from monitoring data and Slack history
Support ticket classification and routing by type, severity, component
Documentation update triggers from merged PRs
Weekly engineering ops digest: open PRs, unresolved incidents, doc debt
On-call handoff summary generated automatically at shift change
// from ivan
"The engineers I've worked with lose 4-6 hours a week to workflow overhead that isn't engineering. PR reminders, incident write-ups, ticket triage. It's not hard work - it's repetitive work. The kind of work that, when automated, gets the most senior people back to the problems that actually need them."

Ivan Bolonikhin - Founder, 2pizza.team

// related

Eng stack, consultants, industries

// dev stack
// related industries
// hire by stack
// questions

Engineering AI questions, answered

How is this different from Copilot or Cursor?

Copilot and Cursor are coding assistants - they help individual developers write code faster. We build everything around the code: PR triage, reviewer assignment, doc generation from merged PRs, incident response copilot, bug triage, tech debt reporting. The ops layer that engineering managers care about, not the editor layer.

Can you work with our existing GitHub / Linear / PagerDuty setup?

Yes - GitHub, GitLab, Bitbucket for code. Linear, Jira, Asana, Notion for tickets. PagerDuty, Opsgenie for incidents. Slack, Discord for comms. We add an AI layer on top of your existing tools via webhooks and APIs. No forced tool migrations.

Will AI auto-merge PRs?

No - hard rule. AI suggests, categorises, routes, drafts responses. Humans approve all merges. The value is removing the busywork around code review (finding the right reviewer, summarising changes for them, drafting changelogs) not the review itself.

What about code security and IP?

We respect your existing access patterns. AI reads what your service account has access to, no more. No-training contractual defaults with Anthropic - your code never trains models. Self-hosted LLM option available if your security policy requires it. Full audit log on every AI request.

How long until first workflow ships?

Two weeks for a focused workflow (e.g., PR triage). Four to six weeks for a multi-workflow eng-ops system. Your team sees real AI suggestions within the first 10 days. Champion engineers test on their team during build phase.

What does this cost?

Engineering AI workflows fall in our Standard tier - $2,000-4,000 fixed price delivered in 2-4 weeks. Active retainer at $1,500/month covers ongoing additions. LLM costs are typically $300-1,000/mo depending on PR/incident volume. Full pricing on the Pricing page.

See what we'd automate in your stack

4 questions. Personalized audit. No sales pitch.

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2 min - 2pizza.team