Resolve.AI
AI agents for production operations — autonomous incident investigation and resolution for enterprise SRE teams
Founded by ex-Splunk engineers with deep production operations DNA, Resolve.AI deploys autonomous AI agents that investigate and resolve incidents without human intervention — not just alert on them.
SWOT Analysis
- AI agents perform autonomous incident investigation: query logs, traces, dashboards, and runbooks
- Founded by ex-Splunk leadership with deep enterprise credibility; proven go-to-market motion
- Notable customers (Coinbase, DoorDash, Salesforce, Zscaler) validate enterprise readiness
- April 2026 funding ($1.5B valuation) signals strong investor and market conviction
- Integrates with existing stacks: PagerDuty, Datadog, Splunk, Slack — no rip-and-replace
- Massive greenfield: most enterprises still rely on on-call humans for incident response
- Platform extension into change management, capacity planning, and SRE workflow automation
- Ride the AI infrastructure wave: enterprises are actively funding autonomous operations projects
- Partnership with Datadog, PagerDuty, or ServiceNow as a natural upsell integration
- Very early revenue stage — autonomous AI ops is unproven at enterprise scale
- Narrow initial footprint: production operations focus may limit cross-sell into other IT domains
- Dependent on quality of existing observability instrumentation; gaps reduce AI effectiveness
- Enterprise security and compliance review for an AI agent with production access is a long sales cycle
- Dynatrace Davis, PagerDuty Copilot, and ServiceNow AIOps expanding autonomous remediation
- Well-funded incumbents (Splunk/Cisco, Datadog) adding autonomous agent capabilities natively
- Customer data privacy concerns around giving AI agents production system access
- Market education required — enterprises are still defining what 'autonomous incident response' means
User Sentiment
Synthesized from G2, Gartner Peer Insights, and analyst review data.
- AI agent investigates incidents end-to-end and proposes (or takes) remediation — massive time savings
- Deep integration with existing Datadog / PagerDuty / Slack workflows — minimal change management
- Founders' operational credibility makes the product feel purpose-built, not VC-hyped
- Dramatically reduces mean-time-to-resolution (MTTR) for common production failure patterns
- Early-stage maturity: edge-case incident types still require human escalation
- Onboarding requires detailed runbook documentation for the AI to act autonomously
- Security review process is extensive before granting agents production access
- Pricing model still evolving — TCO comparisons to incumbent tools are hard
Pricing & TCO
Analyst-synthesized pricing signals — directional only, contact vendor for current terms.
Typical ACV (Mid-Enterprise)
$200K–$500K for enterprise AI ops agents
Market Segments
Deployment
Key Cost Drivers
- Number of AI agent integrations and runbooks
- Incident volume processed autonomously
- Enterprise connector and data source count
Enterprise AI ops pricing still emerging; expect significant investment.
Full comparisonCustomer Profile
Typical segments
Typical buyer
VP Engineering, Head of SRE, or Director of Platform Engineering
- 1Autonomous incident triage and root cause analysis replacing on-call escalation
- 2Runbook execution and remediation actions without waking engineers at 3am
- 3Post-incident analysis and pattern detection across historical incidents
Future Focus Areas
Autonomous change risk assessment: AI agent pre-validates deploys before they cause incidents
Multi-agent orchestration: coordinating specialized agents across infra, app, and data layers
SRE workflow automation beyond incidents: capacity planning and reliability scoring
Enterprise compliance mode: full audit trail of every autonomous action taken
Expanding into the ITSM layer: creating and resolving ServiceNow/Jira tickets autonomously