# Aberr

*/Startups/Aberr*

## Startup Overview

An agentless observability platform maps anomalous service spikes directly to the exact code commits responsible for the disruption. When an incident occurs, the system traces the performance degradation or error spike back to the repository level, identifying the specific pull request that introduced the instability.

Site reliability engineers and DevOps teams typically rely on heavy instrumentation from Datadog or Dynatrace, followed by manual log correlation to hunt down root causes during an outage. Operating entirely without proprietary agents, this architecture removes the deployment friction and computational overhead of traditional application performance monitoring tools.

By correlating existing infrastructure telemetry with version control history, the engine delivers diagnostic granularity down to the exact code commit. This precision bypasses the standard trial-and-error debugging phase, allowing engineering teams to immediately revert the offending change and restore service stability.

## Startup Founding Hypothesis

**Approach**: that maps anomalous service spikes to exact code commits
**Competitors**:
- [Datadog](/Competitors/Datadog)
- [Dynatrace](/Competitors/Dynatrace)
- [manual log correlation](/Competitors/manual_log_correlation)
**Differentiator2x2**: completely agentless and granular to the exact code commit

## Startup Solution Coordinate

**Solution**: [Aberr Trace Engine](/Software/Aberr_Trace_Engine)

## Startup Position2x2

```mermaid
quadrantChart
x-axis "Heavy Instrumentation" --> "Completely Agentless"
y-axis "Generic Alerting" --> "Commit Granularity"
quadrant-1 "Zero-Touch Precision"
quadrant-2 "Heavy Precision"
quadrant-3 "Legacy / Coarse"
quadrant-4 "Disconnected / Manual"
Datadog: [0.20, 0.40]
Dynatrace: [0.15, 0.60]
Manual Log Correlation: [0.85, 0.15]
Aberr: [0.90, 0.90]
```

## Startup Customer Journey

```mermaid
flowchart LR
A[GitHub Marketplace] --> B[eBPF Diagnostic API]
B --> C[Commit-Level Telemetry]
C --> D[Datadog Webhook Ingestion]
D --> E[Engineering Pods]
E --> F[Post-Mortem Slack Channels]
```

## Startup Proof Points

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

**Pilot Goals**:
- 30-day proof-of-concept on a single high-traffic staging environment: Successfully ingest existing APM webhook alerts and correctly identify the exact git commit responsible for at least three injected service anomalies.
- 14-day parallel run for a subset of 25 microservices: Demonstrate that Aberr maps network and resource spikes to deployment timestamps with 95% accuracy using only git metadata and eBPF telemetry.
**Target Metrics**:
- Target: 75% reduction in high-severity incident mean time to resolution (MTTR)
- Target: 95% successful correlation rate between production latency spikes and exact originating pull requests
- Target: 0% increase in compute overhead using agentless eBPF instrumentation compared to traditional APM agents
- Target: 100% of service anomalies mapped to git commit hashes within the first 30 days of deployment
**Target Case Studies**:
- Mid-market fintech engineering team: Transitioned from manual log parsing to Aberr's eBPF intelligence layer, correlating existing Datadog webhook alerts directly to specific GitHub pull requests without exposing raw code payloads.
- Enterprise SaaS infrastructure organization: Deployed Aberr within an on-prem VPC to achieve zero-overhead production monitoring, successfully mapping kernel-level resource spikes to deployment timestamps for over 100 monitored services.
- Small startup DevOps team: Implemented the Starter tier to automate root-cause analysis, eliminating the need to manually cross-reference latency spikes with git commit histories during rapid deployment cycles.
**Testimonial Targets**:
- Vice President of Engineering: Expresses relief that developers no longer spend hours cross-referencing Datadog alerts with GitHub commit histories to identify breaking changes.
- Lead Site Reliability Engineer (SRE): Validates the security and efficiency of the platform, praising the ability to achieve application-level granularity through eBPF without granting read access to proprietary source code.
- DevOps Manager: Highlights the immediate time-to-value, noting the ease of ingesting existing webhooks and automatically mapping kernel-level network spikes to deployment timestamps.

## Startup Top Risks

**Risks**:
- Severity: existential · Description: Agentless data collection methods fail to capture sufficient application-layer context to pinpoint exact code commits in highly compiled or obfuscated environments. · Mitigation Status: in-progress
- Severity: high · Description: Incumbents like Datadog or Dynatrace build or acquire exact-commit correlation and bundle it into their omnipresent APM suites. · Mitigation Status: unmitigated
- Severity: moderate · Description: Enterprise security and compliance teams block the required read-access to proprietary source code repositories needed for the correlation engine. · Mitigation Status: unmitigated
- Severity: moderate · Description: Engineers distrust the automated attribution during high-stress outages and revert to familiar manual log correlation workflows. · Mitigation Status: in-progress

## Startup Competitors

- [Datadog](/Competitors/Datadog) — Incumbent APM
- [Dynatrace](/Competitors/Dynatrace) — Enterprise Observability
- [Manual Log Correlation](/Competitors/Manual_Log_Correlation) — Status Quo
- [New Relic](/Competitors/New_Relic) — Legacy APM
- [Sentry](/Competitors/Sentry) — Error Tracking

## Neighborhood

### Candidate solutions

- [Order Accuracy and Walkouts](/Problems/Order_Accuracy_and_Walkouts) — candidate solution for · Problems
- [Autonomous SaaS Threat](/Problems/Autonomous_SaaS_Threat) — candidate solution for · Problems
- [Formulation Margin Squeeze](/Problems/Formulation_Margin_Squeeze) — candidate solution for · Problems
- [Claims Denial Management](/Problems/Claims_Denial_Management) — candidate solution for · Problems
- [Audit Regulatory Training Completion](/Problems/Audit_Regulatory_Training_Completion) — candidate solution for · Problems
- [Frontline Staff Attrition](/Problems/Frontline_Staff_Attrition) — candidate solution for · Problems
- [Equipment Safety Compliance Risk](/Problems/Equipment_Safety_Compliance_Risk) — candidate solution for · Problems
- [Inaccurate Service Contract Bidding](/Problems/Inaccurate_Service_Contract_Bidding) — candidate solution for · Problems
- [At-Risk Student Retention](/Problems/At-Risk_Student_Retention) — candidate solution for · Problems
- [Resolve Damaged Cargo Claims](/Problems/Resolve_Damaged_Cargo_Claims) — candidate solution for · Problems
- [Flight Disruption Triage](/Problems/Flight_Disruption_Triage) — candidate solution for · Problems

### What it offers

- [Aberr Trace Engine](/Software/Aberr_Trace_Engine) — offers · Software

### Composed of

- [Root Cause Agent](/Agents/Root_Cause_Agent) — composes · Agents
- [Commit Attribution Service](/Services/Commit_Attribution_Service) — composes · Services
- [Spike Analysis Worker](/Agents/Spike_Analysis_Worker) — composes · Agents
- [Agentless Telemetry API](/Agents/Agentless_Telemetry_API) — composes · Agents
- [Commit Indexing Engine](/Agents/Commit_Indexing_Engine) — composes · Agents

### Competitors

- [New Relic](/Competitors/New_Relic) — competes with · Competitors
- [Sentry](/Competitors/Sentry) — competes with · Competitors
- [Dynatrace](/Competitors/Dynatrace) — competes with · Competitors
- [Datadog](/Competitors/Datadog) — competes with · Competitors
- [Manual Log Correlation](/Competitors/Manual_Log_Correlation) — competes with · Competitors

### Embodies

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

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