Built for Engineering Teams

AI Teammates for Engineering Teams

Engineering velocity is blocked by toil — incident triage, ticket routing, documentation, and repetitive code reviews. SphereIQ AI Teammates eliminate the toil so your engineers focus on the work that matters.

What Engineering AI Teammates Handle

Deploy AI teammates that work alongside your engineers — handling the operational burden so your team focuses on building great products.

Incident Response Triage

When an alert fires at 2am, an AI Teammate is already analyzing logs, correlating errors, and routing the incident to the right engineer with full context.

30 days to pilot

When an alert fires at 2am, an AI Teammate is already analyzing logs, correlating errors, and routing the incident to the right engineer with full context.

KPIs Improved
Incident MTTR
60% faster
Alert noise reduction
−55%
Mean time to acknowledge
−70%
Systems Integrated
PagerDuty / OpsGenie Datadog / Splunk / New Relic Slack / Microsoft Teams Jira / Linear
Deployment
30 days to pilot

Discovery → Configuration → Pilot → Scale

Deploy This Teammate
Code Review Preparation

AI Teammates pre-screen pull requests against coding standards, flag security patterns, and summarize changes for reviewers — cutting review time by 40%.

30 days to pilot

AI Teammates pre-screen pull requests against coding standards, flag security patterns, and summarize changes for reviewers — cutting review time by 40%.

KPIs Improved
Code review cycle time
−40%
PRs reviewed per day
+60%
Security issues caught early
+80%
Systems Integrated
GitHub / GitLab / Bitbucket SonarQube / Snyk Jira / Linear / Shortcut Confluence / Notion
Deployment
30 days to pilot

Discovery → Configuration → Pilot → Scale

Deploy This Teammate
Documentation Generation

API docs, runbooks, and onboarding guides generated automatically from code, comments, and system behavior. Documentation that stays current without engineering hours.

30 days to pilot

API docs, runbooks, and onboarding guides generated automatically from code, comments, and system behavior. Documentation that stays current without engineering hours.

KPIs Improved
Documentation lag
−85%
Docs coverage
+200%
Onboarding ramp time
−40%
Systems Integrated
GitHub / GitLab / Bitbucket Confluence / Notion Swagger / OpenAPI Slack / MS Teams
Deployment
30 days to pilot

Discovery → Configuration → Pilot → Scale

Deploy This Teammate
Ticket Triage & Routing

Inbound engineering tickets classified, prioritized, and routed to the right squad — with relevant context, prior tickets, and suggested resolution paths attached.

30 days to pilot

Inbound engineering tickets classified, prioritized, and routed to the right squad — with relevant context, prior tickets, and suggested resolution paths attached.

KPIs Improved
Ticket triage time
−75%
L1 auto-resolution rate
70%
Misrouted tickets
−90%
Systems Integrated
Jira / Linear / Shortcut GitHub Issues Slack / Microsoft Teams Zendesk / ServiceNow
Deployment
30 days to pilot

Discovery → Configuration → Pilot → Scale

Deploy This Teammate
On-Call Knowledge Base

AI Teammates surface relevant runbooks, previous incident resolutions, and system documentation in real-time during incidents — eliminating the "I don't know where that's documented" problem.

30 days to pilot

AI Teammates surface relevant runbooks, previous incident resolutions, and system documentation in real-time during incidents — eliminating the "I don't know where that's documented" problem.

KPIs Improved
Time to find relevant docs
−80%
Incident resolution quality
+45%
Runbook accuracy
99%+
Systems Integrated
Confluence / Notion PagerDuty runbooks Datadog / Splunk GitHub wikis
Deployment
30 days to pilot

Discovery → Configuration → Pilot → Scale

Deploy This Teammate

Why This Beats Traditional Approaches

A side-by-side look at what changes when AI handles the operational work.

Traditional / Manual
SphereIQ AI Teammates
Cost
Senior engineers spending 30–40% of time on operational toil and documentation.
AI handles triage, docs, and ticket routing. Engineers focus 100% on building.
Speed
Incident acknowledgment delayed by on-call availability. Manual triage takes 20–60 minutes.
Instant triage. Context delivered before engineer opens laptop. MTTR cut by 60%.
Scalability
Adding engineers doesn't reduce toil. Operational burden scales with team size.
AI Teammates absorb operational load. Engineering team scales without proportional toil growth.
Auditability
Tribal knowledge in Slack threads. Incident history scattered across tools.
Every incident logged, every decision documented. Searchable history across all systems.
Knowledge
Knowledge lost with engineer turnover. On-call rotation rebuilds context every time.
AI Teammates retain all context permanently. New engineers inherit full institutional memory.

Frequently Asked Questions

Does the AI Teammate replace our on-call engineers?

No. It handles the first 10–15 minutes of every incident automatically — correlating alerts, gathering context, and routing to the right engineer with a full briefing. Engineers still own the resolution; the AI eliminates the blind scramble.

How does it integrate with our existing monitoring and ticketing stack?

SphereIQ integrates with PagerDuty, OpsGenie, Datadog, Splunk, New Relic, Jira, Linear, and GitHub via secure API connections. Setup typically takes 1–2 weeks with no code changes to your existing tools.

What happens when the AI Teammate encounters an incident type it hasn't seen before?

It escalates immediately with all available context — logs, related tickets, system topology, and suggested investigation paths. It never blocks resolution; it always hands off with more information than the engineer would have had otherwise.

Can it generate documentation for legacy codebases with minimal existing docs?

Yes. The Documentation AI Teammate infers structure and behavior from code, comments, git history, and observed system behavior — generating initial documentation even for undocumented systems, then continuously updating it as code changes.

How does SphereIQ protect sensitive code and infrastructure data?

SphereIQ is SOC 2 Type II certified. Data is encrypted in transit and at rest with AES-256. Access controls are role-based, and no training is performed on customer code or data.

Limited Availability

30-Day Engineering AI Pilot

Deploy one AI teammate on real production data. See measurable results before committing.

Fixed Scope

One AI teammate, one workflow, clear boundaries. No scope creep.

Fixed Price

Transparent pricing with no hidden fees or surprise overages.

Clear Success Metrics

Pre-defined KPIs with weekly dashboards so you see ROI from day one.