Resolve AI

Name: Resolve AI
Brand: Resolve AI

AI NativeLeader

AI for prod: Used by Coinbase, MSCI, and Zscaler to resolve incidents, optimize costs, and code with production context

Est 2024|Valuation $1.5B|Raised $200M|18 Ref Customers

LinkedIn 19K|Web traffic 32K|FTEs 168|HQ San Francisco, CA

MarketsIncident Management & On-Call

Published Oct 25, 2026

01Common complaints

1. The primary need is incident paging and on-call scheduling. In such cases, a traditional incident management solution may be more cost-effective. 2. The organization cannot grant auditable, scoped access to production context. If security or compliance policies prevent the product from accessing telemetry, code, or infra context, investigation quality will be constrained. 3. The main problem is alert noise rather than root-cause analysis. Noise reduction capabilities in existing AIOps/observability platforms may provide required outcomes without adding a new agent layer. 4. The procurement organization requires extensive third-party peer reviews and a long vendor track record. Public independent review coverage for resolve.ai is limited; this may not match risk tolerances that require long-standing vendor references. 5. Transparent, self-serve pricing is required. The vendor's pricing is not public; organizations that require predictable self-serve pricing may prefer alternatives with published plans.

Deal disqualifiers (poor fit)

Very small teams with low incident volume.
Environments that prohibit AI access to production data or tools for compliance or policy reasons.
Minimal observability maturity where traces/logs/events are insufficient to support evidence-based investigations.

Publicly acknowledged limitations

Vendor Terms of Service note that AI outputs may be incomplete or inaccurate and require user validation prior to execution.

Gaps / not publicly documented (as of research)

Detailed data residency and hosting region options.
ISO certification coverage beyond vendor claims.
Independent benchmarks comparing investigation accuracy or MTTR improvements versus competitors.
A broad corpus of third-party reviews specifically attributable to resolve.ai (not to be conflated with other companies named "Resolve").

Roadmap signals

Case studies and vendor materials indicate potential expansion into additional workflows such as capacity planning and supervised remediation; publicly available roadmap dates and feature timelines are limited.

02Why they buy

Differentiators (as described by vendor and press)

Vendor framing of a product focus on production operations ("AI for prod") distinct from developer coding assistants and some AIOps tools.
Multi-agent investigation workflow concept with concurrent hypothesis exploration (vendor description).
Generation of actionable artifacts (pull request drafts, operational commands) and incident artifacts (postmortems) in addition to investigative summaries (vendor description).

Where competitors may have the advantage

Established vendors often provide more published customer references, clearer procurement processes, and documented enterprise readiness.
Some customers already have deep investments in PagerDuty or ServiceNow for incident lifecycle and process governance; introducing another system can create overlap and integration considerations.

03Field reports

Reality check: limited third-party end-user review coverage for resolve.ai

Resolve AI is relatively new and focused on enterprise deployments; mainstream review platforms often lag in coverage for new B2B categories. Many accessible reviews found under the name "Resolve" refer to a different company, creating risk of misattribution when consulting public review sites.

Vendor-published testimonials and claims

The vendor publishes customer quotes and outcome claims on its site. These materials describe reduced investigation times, fewer engineers involved in incidents, and substantial reductions in investigation effort. The measurement methodologies (baselines, sample sizes, and time windows) are generally not disclosed in detail on public pages.

Community / social proof and third-party coverage

Public social channels and forums contain limited substantive, independent user reviews of the vendor specifically; some posts relate to funding announcements rather than product experience.
Where review platforms include items for organizations named "Resolve," confirm the product identity before relying on those entries.

Interpretation guidance

Vendor-published testimonials can indicate productive deployments but should be validated through reference checks and pilots. Request methodological details and anonymized incident walkthroughs where possible.

Large cybersecurity vendor (vendor case study)

- Reported outcomes (vendor-published): large reductions in investigation time and fewer engineers involved per incident, with claims of substantial engineering hours saved annually. - Environment described: a globally distributed hybrid environment with many data centers and high transaction volume. Critical evaluation: - The case study aligns with scenarios of high incident cost and operational complexity. - Metrics are presented as headline figures; the methodology (time windows, sample size, and measurement approach) is not fully disclosed publicly. Buyers should request methodological details and an anonymized incident walkthrough.

Growth-stage platform (vendor case study)

Reported outcomes (vendor-published): productivity and MTTR improvements and a reported reliability increase.

Critical evaluation:

Metric definitions for "productivity" and "reliability" are not defined on public pages; buyers in regulated industries should verify data handling and auditability controls used in the engagement.

Recognizable customer quotes (vendor testimonials)

Vendor-published quotes reference faster root-cause identification and reduced incident engagement.

Critical evaluation:

The vendor references recognizable customer logos; however, public detail levels are limited. Request specific reference calls and anonymized walkthroughs to validate accuracy and access patterns.

04Best fit

Company sizes

Typical fit: large organizations or smaller companies with outsized reliability needs and high incident cost.

Industries

High-availability software and platform businesses: fintech/crypto, marketplaces, SaaS, cybersecurity, advertising platforms, and infrastructure providers.
Organizations with 24/7 uptime requirements, frequent incidents/pages, and complex distributed systems (e.g., microservices and Kubernetes).

Roles/personas

Decision makers: VP Engineering, Head of SRE/Platform, CTO/CIO (for operations tooling), Reliability leadership.
End users: on-call engineers, incident commanders, SREs, and platform engineers.

Pain points addressed

Slow time-to-evidence and root-cause analysis.
Large war-room sizes and frequent escalations.
Knowledge gaps for early responders or junior engineers.
Fragmented tooling and tribal knowledge that impede rapid RCA.

Qualification criteria (good fit)

Adequate telemetry signals (logs/metrics/traces) and clear service ownership.
Willingness to integrate and grant scoped access to production context under controlled governance.
High incident cost where measurable MTTR improvements yield material benefit.

05The pitch

What the product does (core value proposition)

Resolve AI describes itself as an "AI for prod" product: a multi-agent AI system intended to assist with production operations by investigating alerts/incidents, proposing root-cause hypotheses with supporting evidence, and assisting with remediation and documentation (e.g., postmortems, PR drafts). Vendor-stated objectives include reducing mean time to resolution (MTTR), decreasing on-call workload, and improving reliability by integrating telemetry, infrastructure, code, and collaboration tools. Vendor claims include: - Autonomous investigation of incidents/alerts prior to or concurrent with human engagement, producing initial hypotheses and supporting evidence. - Parallel hypothesis testing. - Generation of remediation artifacts (code snippets, pull request drafts, kubectl commands/scripts) and incident artifacts (postmortems). - A production-context Q&A capability to support engineers during incidents and development. Vendor-stated impact metrics are reported on vendor pages and marketing materials.

Target market and market positioning

- Primary target: mid-market to large enterprises operating complex distributed systems (microservices, Kubernetes, hybrid cloud), with established SRE/DevOps practices and a material incident volume. - Typical buyer personas: SRE leaders, DevOps/Platform Engineering leadership, VP Engineering/CTO, reliability/production operations owners. - Positioning: the vendor frames the product as a distinct category focused on production operations (labelled by the vendor as "AI for prod"), differentiated from coding assistants and from traditional observability/AIOps tools that emphasize detection and noise reduction rather than end-to-end investigation and action.

Get a comprehensive analysis of Resolve AI including market position, competitive landscape, adoption trends, and peer benchmarks.

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Published Oct 25, 2026

01Common complaints

The primary need is incident paging and on-call scheduling. In such cases, a traditional incident management solution may be more cost-effective.
The organization cannot grant auditable, scoped access to production context. If security or compliance policies prevent the product from accessing telemetry, code, or infra context, investigation quality will be constrained.
The main problem is alert noise rather than root-cause analysis. Noise reduction capabilities in existing AIOps/observability platforms may provide required outcomes without adding a new agent layer.
The procurement organization requires extensive third-party peer reviews and a long vendor track record. Public independent review coverage for resolve.ai is limited; this may not match risk tolerances that require long-standing vendor references.
Transparent, self-serve pricing is required. The vendor's pricing is not public; organizations that require predictable self-serve pricing may prefer alternatives with published plans.

Deal disqualifiers (poor fit)

Very small teams with low incident volume.
Environments that prohibit AI access to production data or tools for compliance or policy reasons.
Minimal observability maturity where traces/logs/events are insufficient to support evidence-based investigations.

Publicly acknowledged limitations

Vendor Terms of Service note that AI outputs may be incomplete or inaccurate and require user validation prior to execution.

Gaps / not publicly documented (as of research)

Detailed data residency and hosting region options.
ISO certification coverage beyond vendor claims.
Independent benchmarks comparing investigation accuracy or MTTR improvements versus competitors.
A broad corpus of third-party reviews specifically attributable to resolve.ai (not to be conflated with other companies named "Resolve").

Roadmap signals

Case studies and vendor materials indicate potential expansion into additional workflows such as capacity planning and supervised remediation; publicly available roadmap dates and feature timelines are limited.

02Why they buy

Differentiators (as described by vendor and press)

Vendor framing of a product focus on production operations ("AI for prod") distinct from developer coding assistants and some AIOps tools.
Multi-agent investigation workflow concept with concurrent hypothesis exploration (vendor description).
Generation of actionable artifacts (pull request drafts, operational commands) and incident artifacts (postmortems) in addition to investigative summaries (vendor description).

Where competitors may have the advantage

Established vendors often provide more published customer references, clearer procurement processes, and documented enterprise readiness.
Some customers already have deep investments in PagerDuty or ServiceNow for incident lifecycle and process governance; introducing another system can create overlap and integration considerations.

03Field reports

Reality check: limited third-party end-user review coverage for resolve.ai

Vendor-published testimonials and claims

Community / social proof and third-party coverage

Public social channels and forums contain limited substantive, independent user reviews of the vendor specifically; some posts relate to funding announcements rather than product experience.
Where review platforms include items for organizations named "Resolve," confirm the product identity before relying on those entries.

Interpretation guidance

Vendor-published testimonials can indicate productive deployments but should be validated through reference checks and pilots. Request methodological details and anonymized incident walkthroughs where possible.

Large cybersecurity vendor (vendor case study)

Reported outcomes (vendor-published): large reductions in investigation time and fewer engineers involved per incident, with claims of substantial engineering hours saved annually.
Environment described: a globally distributed hybrid environment with many data centers and high transaction volume.

Critical evaluation:

The case study aligns with scenarios of high incident cost and operational complexity.
Metrics are presented as headline figures; the methodology (time windows, sample size, and measurement approach) is not fully disclosed publicly. Buyers should request methodological details and an anonymized incident walkthrough.

Growth-stage platform (vendor case study)

Reported outcomes (vendor-published): productivity and MTTR improvements and a reported reliability increase.

Critical evaluation:

Metric definitions for "productivity" and "reliability" are not defined on public pages; buyers in regulated industries should verify data handling and auditability controls used in the engagement.

Recognizable customer quotes (vendor testimonials)

Vendor-published quotes reference faster root-cause identification and reduced incident engagement.

Critical evaluation:

The vendor references recognizable customer logos; however, public detail levels are limited. Request specific reference calls and anonymized walkthroughs to validate accuracy and access patterns.

04Best fit

Company sizes

Typical fit: large organizations or smaller companies with outsized reliability needs and high incident cost.

Industries

High-availability software and platform businesses: fintech/crypto, marketplaces, SaaS, cybersecurity, advertising platforms, and infrastructure providers.
Organizations with 24/7 uptime requirements, frequent incidents/pages, and complex distributed systems (e.g., microservices and Kubernetes).

Roles/personas

Decision makers: VP Engineering, Head of SRE/Platform, CTO/CIO (for operations tooling), Reliability leadership.
End users: on-call engineers, incident commanders, SREs, and platform engineers.

Pain points addressed

Slow time-to-evidence and root-cause analysis.
Large war-room sizes and frequent escalations.
Knowledge gaps for early responders or junior engineers.
Fragmented tooling and tribal knowledge that impede rapid RCA.

Qualification criteria (good fit)

Adequate telemetry signals (logs/metrics/traces) and clear service ownership.
Willingness to integrate and grant scoped access to production context under controlled governance.
High incident cost where measurable MTTR improvements yield material benefit.

05The pitch

What the product does (core value proposition)

Vendor claims include:

Autonomous investigation of incidents/alerts prior to or concurrent with human engagement, producing initial hypotheses and supporting evidence.
Parallel hypothesis testing.
Generation of remediation artifacts (code snippets, pull request drafts, kubectl commands/scripts) and incident artifacts (postmortems).
A production-context Q&A capability to support engineers during incidents and development.

Vendor-stated impact metrics are reported on vendor pages and marketing materials.

Target market and market positioning

Primary target: mid-market to large enterprises operating complex distributed systems (microservices, Kubernetes, hybrid cloud), with established SRE/DevOps practices and a material incident volume.
Typical buyer personas: SRE leaders, DevOps/Platform Engineering leadership, VP Engineering/CTO, reliability/production operations owners.
Positioning: the vendor frames the product as a distinct category focused on production operations (labelled by the vendor as "AI for prod"), differentiated from coding assistants and from traditional observability/AIOps tools that emphasize detection and noise reduction rather than end-to-end investigation and action.

Get a comprehensive analysis of Resolve AI including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →