Written by: Nimesh Chakravarthi, Co-founder & CTO, Struct | Last updated: June 28, 2026
What Counts as Production Usage for Resolve AI?
Production usage means a platform actively processes live incident alerts, triggers automated investigations, and shapes real engineering decisions on a recurring basis. Sandboxes, pilot channels, and proof-of-concept environments do not qualify. A logo on a vendor’s website does not meet this bar.
Key Takeaways for Engineering Leaders
- Production usage requires live incident processing with measurable outcomes, not just marketing logos or limited pilots.
- Manual alert triage across fragmented tools often consumes 30 to 45 minutes before remediation can begin, which drives alert fatigue and SLA risk.
- Resolve AI’s enterprise sales model creates onboarding friction and lacks independently verified production evidence at the Seed-to-Series C level.
- Struct delivers first automated investigations in under 10 minutes with unlimited users on its Growth plan and direct runbook customization.
- Teams ready to reduce on-call burden can automate their on-call runbook with a live Struct demo today.
Customer Evidence for Resolve AI’s Production Usage
The table below applies a consistent evidence standard to publicly available signals for Resolve AI’s named customers as of June 2026. Evidence levels are defined as: Confirmed (public case study with named engineer or measurable outcome), Partial (press mention or logo listing with no outcome data), and Unverified (logo present on vendor site, with no independent corroboration found). The data shows that every publicly listed customer falls into either Unverified or Partial categories, and none meet the Confirmed threshold of named engineers and measurable outcomes.
| Company Name | Evidence Level | Deployment Depth | Setup Friction |
|---|---|---|---|
| Enterprise A (unnamed) | Unverified, logo only on Resolve.ai marketing page | Unknown, no public runbook or integration detail | Not disclosed |
| Enterprise B (unnamed) | Unverified, no independent press or LinkedIn signal found | Unknown | Not disclosed |
| Mid-market reference (unnamed) | Partial, referenced in PR Newswire funding announcement without outcome metrics | Pilot or limited channel, no full-stack integration confirmed | Reported multi-week onboarding via sales process |
| LinkedIn-sourced signal | Partial, individual engineer posts referencing evaluation, not live deployment | Evaluation stage only | Sales-led, demo required before trial access |
The consistent gap across all publicly available signals is the absence of named engineers, measurable MTTR improvements, or integration-depth disclosures. Marketing pages listing enterprise logos without linked case studies or outcome data should be treated as Partial evidence at best. This evidence gap reflects how Resolve AI sells and onboards customers, not just how it markets itself.
How Resolve’s Enterprise Sales Model Shapes Real Usage
Resolve AI is positioned as an enterprise-grade platform. Its go-to-market motion requires prospective customers to contact sales, book a demo, and complete a structured onboarding process before any live investigation runs. For large organizations with dedicated IT procurement teams and multi-quarter evaluation cycles, that model fits existing processes. For a 40-engineer Series B company trying to reduce on-call burden this sprint, it introduces weeks of friction before a single alert is automatically investigated.
Enterprise sales models also tend to produce deployment patterns where the platform is deeply configured for one or two flagship accounts while the broader customer list remains in extended pilot. This pattern makes verified production usage difficult to confirm from the outside. The vendor has a strong incentive to announce the logo, but little incentive to publish integration depth or the timeline from signed contract to live runbook.
Time-to-Value for Seed-to-Series C Engineering Teams
Fast-growing engineering teams treat time-to-value as a primary decision factor. A platform that requires a six-week onboarding engagement before the first automated investigation runs will not solve the 3 AM incident problem this quarter. The relevant comparison points are clear: how long until the first live alert is automatically investigated, how many engineers can use the platform without seat-license negotiation, and whether the runbook logic can be customized without professional services.
Struct is built for this evaluation path. Setup connects an issue source such as Slack or PagerDuty, a code repository such as GitHub, and an observability layer such as Datadog, AWS CloudWatch, GCP Logs, Sentry, or others in under 10 minutes. The first automated investigation then runs on the next alert that fires in the configured channel. No sales call is required, and no multi-week indexing process blocks the first result.
Resolve AI vs Struct for Incident Investigations
Setup time: Resolve AI requires a sales-led onboarding process with no publicly documented self-serve path. Struct connects all integrations and runs a first investigation in under 10 minutes.
User limits: Resolve AI’s pricing is not publicly listed, and seat access is negotiated through enterprise contracts. Struct’s Growth plan includes unlimited users, and the Startup plan supports up to five users with 30 investigations per month at no cost to start.
Runbook customization: Struct supports composable widgets and direct input of existing on-call runbooks, so the AI investigates as a senior engineer would for each alert type. Resolve AI’s customization depth is not independently documented at the mid-market tier.
Compliance: Struct is SOC 2 and HIPAA compliant, which covers the requirements of the large majority of Seed-to-Series C companies. Resolve AI targets enterprise compliance tiers, which may include capabilities beyond what a 40-engineer team needs, and those tiers are priced accordingly.
Neutral Framework for Evaluating Any Incident Platform
Engineering leaders evaluating any automated incident investigation platform can use the following criteria before treating a vendor’s customer list as evidence of production readiness.
Telemetry readiness: Automated investigation platforms are only as accurate as the telemetry they ingest. If your stack lacks structured logs, trace IDs, or consistent alerting triggers, no platform will produce reliable root cause analysis. Confirm your observability baseline before evaluating vendors, because this foundation determines whether any platform can succeed.
Once telemetry is in place, compliance fit becomes the next filter. SOC 2 Type II covers most Seed-to-Series C requirements. HIPAA coverage is required for health-adjacent data. On-premise or VPC-isolated deployment sits in a separate capability tier, so confirm whether your security policy requires it before shortlisting platforms that do not offer that option.
After compliance, focus on customization without professional services. Runbook logic that requires a vendor’s implementation team to modify becomes a maintenance liability. Prefer platforms where engineering teams can directly encode investigation steps, correlation ID formats, and alert-specific instructions without opening a support ticket.
Maintenance burden then determines long-term fit. As your stack evolves, integrations break. Evaluate how quickly a platform’s integration layer is updated when a connected tool changes its API, and whether that update requires engineering effort on your side.
Finally, apply a verification of production claims check. Request a named reference customer at a comparable company size and stack. Ask specifically about integration depth, onboarding duration, and measured MTTR change since deployment. A vendor unable to provide a referenceable customer at your company stage is signaling pilot-stage adoption, not proven production usage.
Frequently Asked Questions
Is Resolve AI used in production?
Resolve AI has published customer logos and received enterprise funding, but independently verified production deployments with named engineers and measurable outcomes are not publicly available as of June 2026. The platform’s enterprise sales model means most deployments are not publicly documented. Engineering leaders should request direct customer references before treating marketing materials as evidence of production usage.
Who are Resolve AI’s main competitors?
The automated incident investigation category includes Struct, Cleric.ai, and Traversal.com, among others. Struct differentiates on setup speed under 10 minutes, composable runbook customization, a Slack-native interface, and SOC 2 and HIPAA compliance, which makes it well suited for Seed-to-Series C engineering teams. Generic AI tools like Claude or ChatGPT are sometimes used reactively for incident triage but require manual log retrieval and are not purpose-built for telemetry correlation.
How do I verify whether an AI ops platform is in real production use?
Ask the vendor for a named reference customer at a comparable company size. Request specifics on integration depth across tools, time from contract to first live investigation, and a before-and-after MTTR measurement. If the vendor cannot provide a referenceable customer with outcome data, treat the deployment as pilot-stage. Logo listings on marketing pages, press release mentions, and LinkedIn posts from individual engineers evaluating a tool do not constitute production verification.
What telemetry does my team need before adopting an automated investigation platform?
Your stack should produce structured logs with consistent trace or correlation IDs, have an alerting trigger connected to Slack or a ticketing system, and use at least one observability platform such as Datadog, AWS CloudWatch, GCP Logs, or Sentry. Without these foundations, automated investigation tools will not have sufficient signal to produce accurate root cause analysis. Struct’s setup process surfaces these gaps quickly during the 10-minute integration flow.
Can a junior engineer handle on-call with an automated investigation platform?
A junior engineer can handle on-call when the platform is configured with the team’s existing runbooks and alert-specific investigation logic. Struct encodes senior-engineer tribal knowledge directly into the investigation flow, so a new hire receives a contextualized starting point that includes impact scope, root cause hypothesis, and suggested fix before they open their first tool. This configuration reduces the need to escalate to a senior engineer for every unfamiliar alert pattern.
Summary of Trade-offs and Recommended Next Steps
Resolve AI is a legitimate platform in the automated incident investigation category. Its enterprise focus, sales-led model, and funding history are real. What remains unclear is the depth and breadth of its production deployments at the mid-market tier, along with the time-to-value for a team that needs automated triage running this week rather than this quarter.
For engineering leaders at Seed-to-Series C companies where alert fatigue is reducing product velocity or SLA pressure is creating on-call burnout, the evaluation criteria stay simple. You need to know how quickly the platform runs a live investigation, whether runbooks can be customized without professional services, and whether a referenceable customer at a comparable company stage reports measurable MTTR improvement.
Struct meets all three criteria. A Series A fintech with over 40 engineers reduced triage time by roughly 80 percent using the setup process described earlier, and now receives automated investigations before an engineer opens their laptop. Junior engineers handle on-call shifts independently, and SLA response windows are protected by automated blast-radius assessment.
If your team is spending 30 or more minutes per alert on manual triage, the cost of that time is already measurable. The next step is to see a platform prove its value in your own environment, not just on a marketing page. To see how Struct delivers automated investigations in under 10 minutes, automate your on-call runbook with a live demo.
