# Evaluateroot

*/Startups/Evaluateroot*

## Startup Overview

This observability diagnostic engine links production system telemetry directly to the specific code commits that cause performance regressions. Instead of generating isolated dashboards of CPU spikes and memory leaks, the system traces anomalous signals back to the exact pull request, line of code, and developer responsible for the change.

Site reliability and software engineering teams waste critical uptime deciphering broad alerting thresholds across distributed environments. When an incident occurs, responders typically pivot between metric dashboards, log aggregators, and version control histories to hunt for the root cause. This engine collapses that diagnostic workflow by automatically annotating production alerts with the precise commit hash that triggered the system failure.

Legacy application performance monitors like Datadog and New Relic charge based on the sheer volume of telemetry data ingested, leaving teams to perform manual log analysis to actually execute a fix. This platform operates strictly on an outcome-based model, charging only per remediated incident. It discards the tax of indexing terabytes of idle logs and focuses exclusively on delivering the code-level precision required to close the ticket.

## Startup Founding Hypothesis

**Approach**: that maps telemetry spikes directly to offending code commits
**Competitors**:
- [Datadog](/Competitors/Datadog)
- [New Relic APM](/Competitors/New_Relic_APM)
- [Manual Log Analysis](/Competitors/Manual_Log_Analysis)
**Differentiator2x2**: code-level precise and priced per remediated incident

## Startup Solution Coordinate

**Solution**: [Commit Trace Engine](/Software/Commit_Trace_Engine)

## Startup Position2x2

```mermaid
quadrantChart
  x-axis Subscription Pricing --> Priced per Remediated Incident
  y-axis Broad Infrastructure --> Code-Level Precise
  quadrant-1 Specialized Remediation
  quadrant-2 Traditional APM Giants
  quadrant-3 Manual & General Monitoring
  quadrant-4 Outsourced General Ops
  Datadog: [0.15, 0.60]
  New Relic APM: [0.20, 0.75]
  Manual Log Analysis: [0.10, 0.15]
  Evaluateroot: [0.85, 0.90]
```

## Startup Offer

**Proof**:
- Mid-market SRE teams: aiming for <3 minute mean-time-to-identification for core microservices.
- DevOps engineers: targeting a 90% reduction in manual log grepping during Sev-1 outages.
- Engineering managers: designed to achieve zero false-positive commit blame in production rollouts.
**Tiers**:
- Name: On-Demand Resolution · Price: ~$100–$250 per remediated incident · Inclusions: Unlimited telemetry ingest and developer seats; billed solely when a telemetry spike is successfully mapped to a specific Git commit.
- Name: Platform Retainer · Price: ~$3k–$6k/month · Inclusions: Up to 50 guaranteed root-cause commit mappings per month across unlimited repositories, plus prioritized webhook processing.
**Guarantee**: If Evaluateroot fails to identify the exact offending code commit for a tracked telemetry spike, the analysis for that incident is completely free.
**Business Function**: ProvideService
**Objection Handlers**:
- We cannot expose our proprietary source code. -> Evaluateroot is designed to parse only Git metadata and deployment hashes, never your raw source code.
- We already pay for New Relic and Datadog. -> Evaluateroot acts as an intelligence layer designed to ingest your existing APM webhooks, not replace them.
- What stops you from billing us for minor noise? -> Billing triggers only when an incident is actively closed in PagerDuty or Jira using our identified commit.
**Pricing Architecture**: UsageMeter
**Agent Checkout Support**:
- agentic-commerce-protocol

## Startup Brand

**Voice**: Direct technical register characterized by an urgent diagnostic precision.
**Tagline**: Trace telemetry spikes to the exact broken code commit.
**Icon Concept**: needle
**Palette Intent**: electric-signal
**Visual Identity**: A stark dark-mode canvas punctuated by neon chartreuse highlights and monospaced typography evokes the high-stakes environment of terminal-based incident response.
**Archetype Reference**: the-sage

## Startup Buyer Chain

**Chain**: Evaluateroot → VP Engineering → Site Reliability Engineers
**Gtm Motion**: Acquisition relies on a self-serve integration where DevOps teams connect their Git repositories and APM instances to analyze a recent outage without upfront cost. Expansion occurs as teams route active production alerts through the platform, incurring fees strictly on a per-remediated-incident basis.
**Agent Channel**: Designed to list in the LangChain tool registry and the OpenAI plugin directory as a structured API, allowing autonomous debugging agents to query the telemetry-to-commit mapping during automated incident triage.
**Primary Channel**: Organic search for technical long-tail queries (e.g., 'correlate Datadog CPU spike to GitHub commit') and intended marketplace listings in the GitHub App Directory where developers search for observability integrations.

## Startup Customer Journey

```mermaid
flowchart LR; A[Technical Search Query] --> B[GitHub App Directory]; B --> C[Git Repository]; C --> D[Initial Outage Analysis]; D --> E[APM Webhook]; E --> F[Production Incident]; F --> G[OpenAI Plugin Directory];
```

## Startup Proof Points

_Illustrative — target and order-of-magnitude estimate figures, not an achieved track record (this Thing is concept-stage)._

**Pilot Goals**:
- 14-day shadow deployment alongside an existing SRE team: aim to successfully map all triggered PagerDuty alerts to their corresponding Git deployment hashes without false positives.
- 30-day APM integration pilot: validate that the system processes telemetry spikes and appends the exact offending commit directly to Jira tickets before the engineer begins manual investigation.
**Target Metrics**:
- Target: <3 minute mean-time-to-identification for core microservice outages
- Aim: 90% reduction in manual log grepping during Sev-1 incidents
- Target: 0 false-positive commit blame assignments during production rollouts
**Target Case Studies**:
- Mid-market SaaS engineering team operating 50+ microservices: validate the transition from manual log grepping to automated commit-level blame during Sev-1 outages.
- Enterprise DevOps group managing daily deployments: demonstrate a reduction in rollback investigation time by linking APM alerts directly to offending Git commits.
**Testimonial Targets**:
- SRE Lead: validates that the automated commit mapping saves hours of frantic log-searching during high-severity pages.
- Engineering Manager: expresses confidence in the system's ability to pinpoint offending deployment hashes without ever requiring access to proprietary source code.
- VP of Engineering: confirms that paying only when a telemetry spike is successfully mapped to a closed incident aligns perfectly with their operational budget.

## Startup Top Risks

**Risks**:
- Severity: existential · Description: Pricing per remediated incident creates a revenue trap if customers dispute the definition of a remediation or delay pushing fixes to production. · Mitigation Status: unmitigated
- Severity: high · Description: Incumbents like Datadog or New Relic replicate the commit-mapping feature and bundle it into their existing agents at no additional cost. · Mitigation Status: unmitigated
- Severity: high · Description: Mapping telemetry spikes to the wrong code commits generates false positives, immediately destroying developer trust during high-stress outages. · Mitigation Status: in-progress
- Severity: moderate · Description: Enterprise security teams refuse to grant an external observability tool read-access to internal source code repositories. · Mitigation Status: in-progress

## Startup Competitors

- [Datadog](/Competitors/Datadog) — Incumbent Observability
- [New Relic APM](/Competitors/New_Relic_APM) — Incumbent APM
- [Manual Log Analysis](/Competitors/Manual_Log_Analysis) — Status Quo
- [Sentry Error Tracking](/Competitors/Sentry_Error_Tracking) — Developer RCA
- [Honeycomb Observability](/Competitors/Honeycomb_Observability) — High-Cardinality APM

## Startup Solution Stack

- [Incident Resolution Service](/Services/Incident_Resolution_Service) — Service-as-Software
- [Spike Triage Agent](/Agents/Spike_Triage_Agent) — Agent
- [Commit Blame Worker](/Agents/Commit_Blame_Worker) — Agent
- [Commit Trace Engine](/Software/Commit_Trace_Engine) — Software
- [Telemetry Hook SDK](/Software/Telemetry_Hook_SDK) — Software

## Startup Story Brand

**Hero**:
- **Need**: to be the architect of resilient systems, not a professional log-grepper
- **Want**: to trace production telemetry spikes to the exact broken code commit
- **Identity**: the SRE lead managing microservices in a mid-market engineering org
**Plan**:
- Step: Connect webhooks · Detail: Integrate your existing PagerDuty alerts and APM telemetry streams in under five minutes.
- Step: Verify mapping · Detail: Watch as the system cross-references deployment hashes to surface the exact commit that broke production.
- Step: Approve resolution · Detail: Close the incident using the identified commit and pay only for the successful remediation.
**Guide**:
- **Empathy**: Does your incident response still stall during the hunt for the offending deployment hash?
**Problem**:
- **Villain**: manual log analysis
- **External**: On-call engineers spend hours grepping through Datadog traces and New Relic APM dashboards while Mean Time to Identification (MTTI) climbs.
- **Internal**: You feel the suffocating pressure of a Sev-1 outage while everyone waits for you to find the needle in the haystack.
- **Philosophical**: Technical expertise belongs in building system resilience, not in scrolling through endless terminal logs.
**Success**: Production regressions are traced to the source in under three minutes, letting you ship code with the certainty that every spike has an immediate explanation.
**One Liner**: Every Sev-1 outage, SRE leads waste hours on manual log analysis. Evaluateroot maps telemetry spikes to offending code commits so you can resolve production incidents in minutes.
**Positioning**:
- **So That**: trace telemetry spikes to the exact broken code commit
- **Unlike**: Manual Log Analysis
- **For Whom**: SRE leads at mid-market engineering firms
- **Category**: Incident Root Cause Analysis Service
**Call To Action**:
- **Direct**: Map your first commit
- **Transitional**: Review sample commit-level traces
**Failure Stakes**:
- Extended service downtime during outages
- Developer burnout from prolonged on-call rotations
- Missed deployment windows due to manual triage
**Transformation**:
- **To**: the engineering's reliability architect
- **From**: a developer buried in Datadog logs
**Controlling Idea**: Root cause analysis should be a deterministic map, not a manual search.

## Startup Token Hero

**Genre**: founding-hypothesis
**Rendered**: Every Sev-1 outage, SRE leads waste hours on manual log analysis. Evaluateroot maps telemetry spikes to offending code commits so you can resolve production incidents in minutes.
**Mechanism**: spine-derived-v1
**Template Id**: spine-founding-hypothesis
**Vocab Fingerprint**: 6e07c0fa94397bba

## Startup Token Positioning

**Genre**: moore-positioning
**Rendered**: Incident Root Cause Analysis Service for SRE leads at mid-market engineering firms. Unlike Manual Log Analysis — trace telemetry spikes to the exact broken code commit.
**Mechanism**: spine-derived-v1
**Template Id**: spine-moore-positioning
**Vocab Fingerprint**: 75786d2cb2b64685

## Startup Token Pitch Deck

**Genre**: pitch-deck
**Rendered**: Problem: On-call engineers spend hours grepping through Datadog traces and New Relic APM dashboards while Mean Time to Identification (MTTI) climbs.
Solution: Every Sev-1 outage, SRE leads waste hours on manual log analysis. Evaluateroot maps telemetry spikes to offending code commits so you can resolve production incidents in minutes.
Customer: SRE leads at mid-market engineering firms
Unlike: Manual Log Analysis
**Mechanism**: spine-derived-v1
**Template Id**: spine-pitch-deck
**Vocab Fingerprint**: fdac7aa136cc139f

## Startup Token M E D D P I C C

**Pain**: On-call engineers spend hours grepping through Datadog traces and New Relic APM dashboards while Mean Time to Identification (MTTI) climbs.
**Metrics**: Target: Production regressions are traced to the source in under three minutes, letting you ship code with the certainty that every spike has an immediate explanation.
**Rendered**: Pain: On-call engineers spend hours grepping through Datadog traces and New Relic APM dashboards while Mean Time to Identification (MTTI) climbs.
Economic buyer: VP Engineering
Metrics: Target: Production regressions are traced to the source in under three minutes, letting you ship code with the certainty that every spike has an immediate explanation.
Competition: Manual Log Analysis
**Mechanism**: spine-derived-v1
**Competition**: Manual Log Analysis
**Economic Buyer**: VP Engineering
**Vocab Fingerprint**: 4efba1f31d4284c3

## Startup Token Cold Email

**Genre**: cold-email
**Rendered**: Subject: Incident Root Cause Analysis Service for SRE leads at mid-market engineering firms

SRE leads at mid-market engineering firms — On-call engineers spend hours grepping through Datadog traces and New Relic APM dashboards while Mean Time to Identification (MTTI) climbs. Every Sev-1 outage, SRE leads waste hours on manual log analysis. Evaluateroot maps telemetry spikes to offending code commits so you can resolve production incidents in minutes.
**Mechanism**: spine-derived-v1
**Template Id**: spine-cold-email
**Vocab Fingerprint**: c45574a2ccf3d7d4

## Startup Token Agent Spec

**Genre**: ai-agent-spec
**Rendered**: Incident Root Cause Analysis Service. Every Sev-1 outage, SRE leads waste hours on manual log analysis. Evaluateroot maps telemetry spikes to offending code commits so you can resolve production incidents in minutes. Serves SRE leads at mid-market engineering firms.
**Mechanism**: spine-derived-v1
**Template Id**: spine-ai-agent-spec
**Vocab Fingerprint**: 6e08ec8546319c25

## Neighborhood

### Candidate solutions

- [ABET Accreditation Data Collection](/Problems/ABET_Accreditation_Data_Collection) — candidate solution for · Problems

### Composed of

- [Accreditation Alignment Service](/Services/Accreditation_Alignment_Service) — composes · Services
- [Gradebook Integration API](/Software/Gradebook_Integration_API) — composes · Software
- [Criterion Auditor Agent](/Agents/Criterion_Auditor_Agent) — composes · Agents
- [Multimodal Extraction Worker](/Agents/Multimodal_Extraction_Worker) — composes · Agents
- [Artifact Parsing Engine](/Software/Artifact_Parsing_Engine) — composes · Software
- [Multimodal Parsing Engine](/Software/Multimodal_Parsing_Engine) — composes · Software
- [Accreditation Dossier Service](/Services/Accreditation_Dossier_Service) — composes · Services
- [Artifact Extraction Agent](/Agents/Artifact_Extraction_Agent) — composes · Agents
- [Gradebook Ingestion API](/Software/Gradebook_Ingestion_API) — composes · Software
- [Rubric Alignment Agent](/Agents/Rubric_Alignment_Agent) — composes · Agents
- [Telemetry Hook SDK](/Software/Telemetry_Hook_SDK) — composes · Software
- [Commit Blame Worker](/Agents/Commit_Blame_Worker) — composes · Agents
- [Commit Trace Engine](/Software/Commit_Trace_Engine) — composes · Software
- [Spike Triage Agent](/Agents/Spike_Triage_Agent) — composes · Agents
- [Incident Resolution Service](/Services/Incident_Resolution_Service) — composes · Services

### What it offers

- [Criterion Auditor](/Agents/Criterion_Auditor) — offers · Agents
- [Artifact Mapping Agent](/Agents/Artifact_Mapping_Agent) — offers · Agents

### Embodies

- [Agent](/Theses/Agent) — embodies · Theses
- [Software](/Theses/Software) — embodies · Theses

### Competitors

- [Manual Spreadsheet Mapping](/Competitors/Manual_Spreadsheet_Mapping) — competes with · Competitors
- [Watermark](/Competitors/Watermark) — competes with · Competitors
- [HelioCampus](/Competitors/HelioCampus) — competes with · Competitors
- [Canvas LMS](/Competitors/Canvas_LMS) — competes with · Competitors
- [Gradescope](/Competitors/Gradescope) — competes with · Competitors
- [Gradescope Rubric Tools](/Competitors/Gradescope_Rubric_Tools) — competes with · Competitors
- [Watermark Assessment Suites](/Competitors/Watermark_Assessment_Suites) — competes with · Competitors
- [Blackboard Learn](/Competitors/Blackboard_Learn) — competes with · Competitors
- [Watermark Assessment](/Competitors/Watermark_Assessment) — competes with · Competitors
- [manual SharePoint folders](/Competitors/manual_SharePoint_folders) — competes with · Competitors
- [manual departmental spreadsheets](/Competitors/manual_departmental_spreadsheets) — competes with · Competitors
- [spreadsheet mapping](/Competitors/spreadsheet_mapping) — competes with · Competitors
- [manual spreadsheet tracking](/Competitors/manual_spreadsheet_tracking) — competes with · Competitors
- [manual folder curation](/Competitors/manual_folder_curation) — competes with · Competitors
- [Microsoft SharePoint](/Competitors/Microsoft_SharePoint) — competes with · Competitors
- [manual dual rubrics](/Competitors/manual_dual_rubrics) — competes with · Competitors
- [Datadog](/Competitors/Datadog) — competes with · Competitors
- [Manual Log Analysis](/Competitors/Manual_Log_Analysis) — competes with · Competitors
- [Sentry Error Tracking](/Competitors/Sentry_Error_Tracking) — competes with · Competitors
- [Honeycomb Observability](/Competitors/Honeycomb_Observability) — competes with · Competitors
- [New Relic APM](/Competitors/New_Relic_APM) — competes with · Competitors

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