Deeptrace
AI agents that automate on-call SRE by investigating and resolving production alerts
Website: https://deeptrace.com/
Cover Block
PUBLIC
| Name | Deeptrace |
| Tagline | AI agents that automate on-call SRE by investigating and resolving production alerts |
| Founded | 2025 |
| Stage | Seed |
| Business Model | SaaS |
| Industry | Other |
| Technology | AI / Machine Learning |
| Growth Profile | Venture Scale |
| Founding Team | Co-Founders (2) |
| Funding Label | Undisclosed (total disclosed ~$5,000,000) |
Links
PUBLIC
- Website: https://deeptrace.com/
- LinkedIn: https://www.linkedin.com/company/deeptrace-ai
- Y Combinator: https://www.ycombinator.com/companies/deeptrace
Executive Summary
PUBLIC Deeptrace is a seed-stage startup building AI agents to autonomously handle the investigation and resolution of production alerts, a high-friction task that currently consumes significant engineering time [Y Combinator, 2025]. Founded in 2025 and backed by Y Combinator, the company aims to automate on-call Site Reliability Engineering (SRE) workflows, moving from alert to pull request without human intervention [Y Combinator, 2025]. The product's stated differentiation lies in its ability to reason across observability data, telemetry, and codebases to not just surface issues but also generate fixes [Deeptrace, Unknown]. The founding team is listed as Andy Lee, Sri Somasundaram, and Arjun Virmani, though their specific professional backgrounds are not detailed in public sources [Crunchbase]. The company has raised an estimated $5 million in an undisclosed seed round from investors including Felicis and Matrix [LinkedIn, 2026]. Over the next 12-18 months, the key milestones to watch are the transition from a YC-backed prototype to a commercially available SaaS product, the signing of initial enterprise customers, and the demonstration of reliable, safe agentic actions in complex production environments. Data Accuracy: YELLOW -- Product claims are sourced from company and YC materials; funding details are based on a single social media post.
Taxonomy Snapshot
| Axis | Value |
|---|---|
| Stage | Seed |
| Business Model | SaaS |
| Technology Type | AI / Machine Learning |
| Growth Profile | Venture Scale |
| Founding Team | Co-Founders (2) |
Company Overview
PUBLIC Deeptrace emerged in early 2025 as a participant in Y Combinator's Winter 2025 batch, positioning itself in the crowded but nascent field of AI for site reliability engineering [Y Combinator, 2025]. The company's founding narrative, as presented on its public profile, centers on automating the manual, time-consuming process of investigating and resolving production alerts, a persistent pain point for engineering teams [Y Combinator, 2025].
Key personnel include founders Andy Lee, Sri Somasundaram, and Arjun Virmani, though their professional backgrounds are not detailed in available public sources [Crunchbase]. The company's headquarters location is not publicly disclosed. The most significant milestone following its YC debut is a $5 million funding round, announced via a founder's LinkedIn post in 2026 and backed by investors including Felicis, Matrix Partners, and Y Combinator [LinkedIn, 2026][Felicis, 2026].
A critical point of clarification for investors is the existence of a namesake entity, DeepTrace Technologies, an EU-based company focused on AI for healthcare and environmental monitoring that was founded in 2017 and raised a separate €1.7 million seed round [PitchBook]. There is no evidence of operational or corporate overlap between the two [Better Stack Community, 2026].
Data Accuracy: YELLOW -- Founding year and YC batch confirmed by Y Combinator; funding round and investor list corroborated by founder post and investor portfolio page. Founder names listed on Crunchbase but without biographical detail. Headquarters and legal entity details remain unconfirmed.
Product and Technology
MIXED
The product is a software-as-a-service platform that deploys AI agents to automate the entire lifecycle of a production alert, from initial triage to resolution. According to the company's own description, Deeptrace "investigates and fixes every alert in production before your customers notice by reasoning across observability, telemetry, and code" [Deeptrace]. The Y Combinator listing elaborates that this is an end-to-end process, moving "from alert to pull request (PR)" [Y Combinator, 2025]. This suggests a workflow where the agent not only diagnoses an issue but also authors a code fix, a level of automation that distinguishes it from simpler alert routing or notification tools.
The core technical proposition hinges on an AI model capable of synthesizing disparate data sources. The company claims its agents use AI to map "logs, traces, metrics, and codebases" to perform investigations [Y Combinator, 2025]. This cross-stack reasoning is the central mechanism for automating debugging tasks that typically require manual correlation by a site reliability engineer. The product is positioned as a direct tool for engineering teams burdened by on-call duties, with the goal of reducing debugging noise and freeing engineers to focus on development work [Y Combinator, 2025].
Specific implementation details and the underlying technology stack are not publicly documented. However, job postings for Founding Engineer roles in Palo Alto and San Francisco list desired experience with "modern AI stacks (OpenAI, Anthropic, etc.)," "vector databases," and "observability tools (Datadog, New Relic, Grafana)" [LinkedIn, 2026]. This strongly suggests a system built on commercial large language models, augmented with retrieval-augmented generation (RAG) techniques over proprietary observability data, and integrated with common DevOps platforms. There is no public information on product maturity, such as a general availability date, specific feature modules, or API documentation.
Data Accuracy: YELLOW -- Product claims are sourced from the company's website and Y Combinator profile. Technical stack is inferred from active job postings.
Market Research
PUBLIC
The market for automated SRE and incident response is expanding as engineering teams face unsustainable alert volumes, a pressure point that creates a clear wedge for AI agents.
Total addressable market figures for AI-driven SRE automation are not yet established in public analyst reports. The broader observability and APM (Application Performance Monitoring) market, which provides the foundational data these agents act upon, is a relevant proxy. According to Gartner, the worldwide APM market was valued at approximately $6.8 billion in 2023 and is forecast to grow to $8.8 billion by 2026 [Gartner, 2023]. This analogous market size suggests a substantial underlying spend on monitoring, which in turn drives demand for tools that reduce the manual toil of interpreting that data.
Demand is propelled by several converging trends. The shift to microservices and cloud-native architectures has exponentially increased system complexity and the frequency of production incidents. Simultaneously, a persistent shortage of experienced site reliability engineers makes manual, 24/7 alert response economically challenging for most companies. The core driver is economic: reducing mean time to resolution (MTTR) directly impacts revenue and customer satisfaction during outages. A secondary driver is talent retention, as automating repetitive debugging tasks can improve engineer quality of life and reduce burnout [Reddit, 2026].
Adjacent and substitute markets include traditional IT service management (ITSM) platforms with AIOps features, such as ServiceNow and PagerDuty, which focus on workflow orchestration and notification rather than autonomous investigation. The broader field of AI for software development, including AI coding assistants, also represents a substitute if teams choose to invest in developer productivity over operations automation. The regulatory environment is currently neutral, though the autonomous nature of AI agents making changes in production could attract scrutiny related to operational risk and compliance in heavily regulated sectors like finance and healthcare.
APM Market 2023 | 6.8 | $B
APM Market 2026 (est.) | 8.8 | $B
The projected growth in the underlying APM market indicates a rising budget pool for solutions that enhance the value of monitoring investments. The absence of a dedicated market size for AI SRE agents, however, underscores the category's nascency.
Data Accuracy: YELLOW -- Market sizing is based on an analogous sector report from a major analyst firm. Demand drivers are supported by industry discussion but lack specific, cited quantitative studies.
Competitive Landscape
MIXED
Deeptrace enters a nascent but rapidly forming market for AI-driven site reliability engineering, where its primary competition comes from a handful of similarly positioned venture-backed startups, not from established observability incumbents.
| Company | Positioning | Stage / Funding | Notable Differentiator | Source |
|---|---|---|---|---|
| Deeptrace | AI agents that autonomously investigate, debug, and resolve production alerts end-to-end. | Seed (est. $5M) | Claims full automation from alert to pull request, reasoning across logs, traces, metrics, and code. | [Y Combinator, 2025] |
| Cleric | AI assistant for on-call engineers to debug and fix issues faster. | Seed ($3.3M) | Focus on collaborative debugging and knowledge capture for human-in-the-loop workflows. | [Better Stack Community, 2026] |
The competitive map breaks into three distinct layers. The first is the direct cohort of AI SRE startups like Cleric, Resolve AI, and Parity. These companies are all targeting the same pain point,alert fatigue and manual triage,with varying degrees of automation. The second layer consists of adjacent substitutes: the major observability platforms (Datadog, New Relic, Grafana) and incident management tools (PagerDuty, Opsgenie). These incumbents own the alerting channel and have begun adding AI features, but their focus remains on surfacing and routing issues, not on autonomous resolution. The third layer is the internal build: large engineering organizations with the resources to construct custom automation atop their existing tooling, a perennial threat to any point-solution SaaS.
Deeptrace's stated edge today is the ambition of its technical scope. Where Cleric positions itself as an assistant, Deeptrace claims an "end-to-end" agent that moves from alert to code fix without human intervention [Y Combinator, 2025]. This is a more aggressive product bet, aiming to own the entire remediation workflow rather than augmenting it. The durability of this edge is entirely contingent on execution. It is a perishable advantage if the underlying AI models cannot reliably perform complex debugging tasks across diverse codebases and tech stacks. The company's early backing from Y Combinator, Felicis, and Matrix provides a capital edge relative to unfunded projects, but it is matched by similarly funded peers like Cleric.
The company's most significant exposure is its narrow focus on a technical workflow that larger platforms can easily decide to automate themselves. Datadog's AI-powered root cause analysis or PagerDuty's automated runbooks could expand into remediation, leveraging their entrenched distribution and existing customer trust. Furthermore, Deeptrace's lack of disclosed customer deployments or case studies makes it difficult to assess real-world performance against Cleric, which has been more visible in community discussions [Reddit, 2026]. The company is also exposed to branding confusion with the unrelated EU-based DeepTrace Technologies, which could complicate search visibility and partnership discussions.
The most plausible 18-month scenario is a consolidation of the AI SRE startup cohort around two divergent philosophies: full automation versus human augmentation. In this view, the "winner" is the company that first demonstrates reliable, high-severity incident resolution at a named enterprise customer, proving the automation thesis. The "loser" is the company that remains in a perpetual state of demo-ware, unable to move beyond triaging low-stakes, repetitive alerts. If the market decides engineers are not ready to cede control of production fixes to an AI, the advantage shifts to tools like Cleric that enhance, rather than replace, the human on-call. Deeptrace's fate hinges on proving its more ambitious technical claim is not just possible, but commercially viable and safe.
Data Accuracy: YELLOW -- Competitor identification and basic positioning from third-party industry lists; funding and stage data for Deeptrace and one competitor are partially corroborated. Differentiation claims are sourced from company materials.
Opportunity
PUBLIC The prize for automating on-call SRE is a foundational layer in the software supply chain, a platform that could capture recurring spend from engineering organizations as a direct, non-discretionary cost of doing business.
The headline opportunity is for Deeptrace to become the default AI layer for production reliability, a category-defining platform that sits between observability data and engineering teams. This outcome is reachable because the core pain point is acute and universal: engineering teams are overwhelmed by alert noise, and the company's stated product vision aims to close the loop from alert to pull request autonomously [Y Combinator, 2025]. The wedge is automating the debugging process, a task that currently consumes senior engineering time and is a primary driver of burnout and operational drag. If the AI agents can reliably handle a meaningful percentage of common, noisy alerts, the platform becomes a non-negotiable component of the modern DevOps stack, akin to how CI/CD or monitoring tools became mandatory over the last decade.
Two concrete growth scenarios illustrate the paths to scale.
| Scenario | What happens | Catalyst | Why it's plausible |
|---|---|---|---|
| Land-and-expand in the mid-market | Deeptrace becomes the standard SRE automation tool for Series B-D SaaS companies, expanding from alert triage into automated remediation, runbook execution, and cost optimization. | A successful public launch and case study with a well-known YC alumni company, demonstrating a 50%+ reduction in mean time to resolution (MTTR) [Y Combinator, 2025]. | The target customer profile (engineering teams at software companies) is clearly defined, and the initial wedge (debugging noise) is a high-frequency, low-stakes entry point that can prove value quickly before moving to more critical workflows. |
| Platformization via observability partnerships | Deeptrace evolves into an embedded AI engine, powering automated remediation features within major observability platforms like Datadog or New Relic, becoming a behind-the-scenes infrastructure provider. | A strategic partnership or technology integration announced with a leading observability vendor, cited as a preferred resolution partner. | The product's stated architecture reasons across logs, traces, and metrics [Y Combinator, 2025], which aligns directly with the data models of incumbent platforms. A partnership model mitigates the go-to-market risk for a young company while leveraging established distribution. |
What compounding looks like is a data and execution flywheel. Each resolved alert generates training data that improves the AI's diagnostic accuracy and remediation success rate. As the model improves, the product can handle more complex, higher-value alerts, which justifies a higher price point and attracts larger, more sophisticated customers. These larger deployments, in turn, generate more diverse and valuable data. Early evidence of this flywheel is not yet public, but the company's hiring of founding engineers in Palo Alto and San Francisco suggests an intent to build the core technical infrastructure required to capture and use this data [LinkedIn, 2026].
The size of the win can be framed by looking at the valuation of companies that have automated adjacent parts of the software development lifecycle. For example, GitLab, which automates the CI/CD pipeline, reached a market capitalization of over $10 billion following its IPO. A more direct, though private, comparable is Harness, an automated CI/CD and GitOps platform that was valued at approximately $3.7 billion in its Series D round in 2023 [Crunchbase]. If the land-and-expand scenario plays out and Deeptrace captures a material portion of the SRE automation budget within its target market, a multi-billion dollar outcome is a plausible scenario, not a forecast. Data Accuracy: YELLOW -- The opportunity framing is extrapolated from the company's stated product vision and the known dynamics of the DevOps tooling market. The growth scenarios are illustrative models based on the available public positioning.
Sources
PUBLIC
[Y Combinator, 2025] Deeptrace: AI agents for on-call | https://www.ycombinator.com/companies/deeptrace
[Deeptrace, Unknown] Deeptrace | https://deeptrace.com/
[Crunchbase, Unknown] Deeptrace | https://www.crunchbase.com/organization/deeptrace-fa3e
[LinkedIn, 2026] Martin Lee - Turing Game | LinkedIn | https://www.linkedin.com/in/mrtle
[Felicis, 2026] Felicis Portfolio | Companies We’ve Invested In | Felicis | https://www.felicis.com/portfolio
[LinkedIn, 2026] Deeptrace hiring Founding Engineer in Palo Alto, CA | https://www.linkedin.com/jobs/view/founding-engineer-at-deeptrace-4376071801
[LinkedIn, 2026] Deeptrace hiring Founding Engineer in San Francisco, CA | https://www.linkedin.com/jobs/view/founding-engineer-at-deeptrace-4376087045
[PitchBook] DeepTrace Technologies 2025 Company Profile: Valuation, Funding & Investors | PitchBook | https://pitchbook.com/profiles/company/279673-03
[Better Stack Community, 2026] 10 Best Cleric Alternatives in 2026 | Better Stack Community | https://betterstack.com/community/comparisons/cleric-alternatives/
[Reddit, 2026] r/devops on Reddit: A growing wave of “AI SRE” tools - Are they production ready? | https://www.reddit.com/r/devops/comments/1m4egqq/a_growing_wave_of_ai_sre_tools_are_they/
[Gartner, 2023] Gartner Forecasts Worldwide Application Performance Monitoring Market to Reach $8.8 Billion in 2026 | https://www.gartner.com/en/newsroom/press-releases/2023-08-28-gartner-forecasts-worldwide-application-performance-monitoring-market-to-reach-8-8-billion-in-2026
Articles about Deeptrace
- Deeptrace's AI Agents Anchor a Bid to End the 3 a.m. Page — The YC-backed startup, with $5 million from Felicis and Matrix, is training AI to debug production alerts from alert to pull request.