# Deeptrace: AI agents for on-call

Canonical: https://mudpie.ai/companies/deeptrace/
Breadcrumb: [Home](https://mudpie.ai/) / [Companies](https://mudpie.ai/companies/) / [Deeptrace: AI agents for on-call](https://mudpie.ai/companies/deeptrace/)
Author: Ali Abouelatta (https://mudpie.ai/authors/ali-abouelatta/)
Published: 2026-09-19
Updated: 2026-09-19
Research type: Company profile
Method: Company and accelerator sources checked 2026-09-19. Product claims are attributed to their sources; this is research, not a hands-on product trial.

## What it does

Deeptrace is an AI SRE for production alerts. It reasons across logs, traces, metrics and code, posts evidence-backed root-cause analysis, answers follow-up questions in Slack or a web app, and can open pull requests, update runbooks or create Linear tickets. The [YC profile](https://www.ycombinator.com/companies/deeptrace) describes end-to-end alert investigation; the [current homepage](https://deeptrace.com/) shows the alert-to-resolution workflow and 20+ integrations.

The fit is a growing engineering team with alert fatigue, distributed systems and too much context switching during on-call. Deeptrace is not only an observability dashboard. It is useful if it can assemble enough context to let an engineer decide what to do without hiding uncertainty behind an automatic fix.

## Why I’d look closer

The product surface is concrete: alert prioritization, grouped incidents, root-cause context, evidence citations, Slack questions and generated pull requests. The homepage says average time to root cause is 2–3 minutes. A Mintlify case study reports roughly 100 alerts/day, investigation time moving from five minutes to one, and about eight hours of on-call time saved per day. Those are company/customer case-study claims, not independent benchmarks.

The founders have useful systems context. The [YC biographies](https://www.ycombinator.com/companies/deeptrace) describe Andy Lee with Tesla Optimus simulation and SpaceX flight/build reliability experience, and Sri Somasundaram with embedded-finance infrastructure at Parafin.

## What I’d ask

Which tools, code paths and permissions are read-only, and what requires approval before a PR or ticket is created? I’d connect a staging alert stream, compare root-cause evidence to known incidents, and review false positives, data retention, rollback and generated-code controls. The public site lists a two-week startup trial and enterprise deployment options but not a numeric price card.

## My editorial take

Deeptrace is a strong fit for teams drowning in production context switching. Evidence-linked investigation is the right wedge. The product earns trust when engineers can see why the agent believes a root cause and keep final remediation under human control.

## Quick facts

| Field | Sourced detail |
|---|---|
| Buyer fit | Engineering and SRE teams operating production systems |
| Workflow | Alert triage, root cause, Slack/web Q&A, PRs, runbooks and tickets |
| Integrations | 20+ named integrations including Datadog, GitHub, Slack and Linear |
| Pricing | Startup trial and enterprise plans shown; no numeric card exposed |

## Sources checked

Checked 2026-09-19.

| Source | Used for |
|---|---|
| [YC company profile](https://www.ycombinator.com/companies/deeptrace) | Product, founders and launch context |
| [Deeptrace homepage](https://deeptrace.com/) | Current workflows, integrations, pricing posture and customer proof |
| [Mintlify case study](https://deeptrace.com/case-studies/mintlify) | Company/customer outcome claim |

## Cohort context

Deeptrace is listed in Fall 2025. In our 2026-09-18 directory snapshot, 90 of 146 listed companies in that cohort have YC’s primary industry label B2B (61.6%). This is a current-directory comparison, not an original intake count or a performance ranking. [Nine-cohort dataset](https://mudpie.ai/research/yc-cohorts-2026-09-19.json).

## Public website snapshot

Observed 2026-09-19T16:14:59.190Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.

| Signal | Homepage observation |
| --- | --- |
| Product description metadata | Observed |
| Canonical link | Not observed in this response |
| H1 or H2 heading | Observed |
| Typed structured data | Not observed in this response |
| Docs/developer link | Not observed in this response |
| Pricing link | Observed |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |

[Public observations](https://mudpie.ai/research/yc-homepage-links-2026-09-19.json) · [Collection method](https://mudpie.ai/research/yc-homepage-methods/README.md). Missing links here do not establish that a capability or file is absent elsewhere.


## Author disclosure

I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.