# Eronata

*/Startups/Eronata*

## Startup Overview

This observability engine correlates disparate telemetry streams across multi-cloud environments into a single root-cause analysis. It ingests logs, metrics, and traces from distributed services and maps them directly to the active infrastructure state.

Site reliability engineers and DevOps teams lose critical uptime manually cross-referencing dashboards and querying fragmented logs. Legacy monitoring platforms force operators to stitch together isolated alerts to diagnose an underlying failure. This platform eliminates manual log correlation by automatically tracking dependencies and state changes across the entire infrastructure footprint.

Instead of charging by data ingestion volume like Datadog or Splunk, the billing model scales based entirely on resolved incidents. Because telemetry maps contextually to the physical and virtual infrastructure state, operators see the exact node or configuration failure immediately, bypassing the noise of cascading alerts to execute a precise fix.

## Startup Founding Hypothesis

**Approach**: that correlates cross-cloud telemetry streams into unified root causes
**Competitors**:
- [Datadog](/Competitors/Datadog)
- [Splunk](/Competitors/Splunk)
- [manual log correlation](/Competitors/manual_log_correlation)
**Differentiator2x2**: contextually mapped to infrastructure state and priced by resolved incident

## Startup Solution Coordinate

**Solution**: [Eronata Root Cause Engine](/Software/Eronata_Root_Cause_Engine)

## Startup Position2x2

```mermaid
quadrantChart
    title Eronata Position vs Competitors
    x-axis Unmapped Telemetry --> Infrastructure State Mapped
    y-axis Volume/Ingest Pricing --> Priced by Resolved Incident
    quadrant-1 Automated Resolution
    quadrant-2 Incident Specific
    quadrant-3 Legacy Operations
    quadrant-4 Broad Observability
    Datadog: [0.75, 0.30]
    Splunk: [0.35, 0.20]
    manual log correlation: [0.15, 0.40]
    Eronata: [0.85, 0.85]
```

## Startup Offer

**Proof**:
- Targeting enterprise DevOps teams to eliminate hours of manual log correlation during Sev-1 outages.
- Aiming to reduce mean time to resolution (MTTR) by up to 70% for multi-cloud infrastructure environments.
- Targeting cloud-native platforms to successfully isolate cross-service latency bottlenecks to singular infrastructure state changes.
**Tiers**:
- Name: On-Demand Correlation · Price: ~$100–$250 per resolved incident · Inclusions: Automated root cause analysis across up to 3 cloud environments, contextual mapping for standard Kubernetes deployments, and direct incident ticket generation for IT teams.
- Name: Enterprise Volume · Price: ~$50–$150 per resolved incident + ~$2,000/mo platform fee · Inclusions: Unlimited cloud environments, custom infrastructure state mapping via intended Terraform integrations, and automated runbook triggering with guaranteed SLAs.
**Guarantee**: Eronata guarantees to deliver a definitive, unified root cause within 15 minutes of an incident trigger; if the provided analysis fails to identify the correct failing component, the incident is not billed.
**Business Function**: ProvideService
**Objection Handlers**:
- We already pay Datadog and Splunk for monitoring -> Eronata is designed to ingest those existing telemetry streams and map them to live state, charging you only for the resolved incident rather than raw data storage.
- Our infrastructure state changes too rapidly to map -> The platform is built to continuously read your infrastructure-as-code manifests, ensuring the context map updates in real-time before an incident occurs.
- What if the system bills us for false alarms? -> You are only billed when the platform successfully maps a telemetry spike to a verified infrastructure failure, automatically ignoring transient noise.
**Pricing Architecture**: UsageMeter
**Agent Checkout Support**:
- agentic-commerce-protocol

## Startup Brand

**Voice**: Direct engineering register characterized by uncompromising precision and strict factual brevity.
**Tagline**: Find cross-cloud root causes without paying for raw telemetry.
**Icon Concept**: probe
**Palette Intent**: electric-signal
**Visual Identity**: Deep charcoal backgrounds contrast against sharp neon cyan and magenta accents to reflect raw telemetry streams converging into clear focal points.
**Archetype Reference**: the-sage

## Startup Buyer Chain

**Chain**: Eronata → VP of Cloud Operations → Site Reliability Engineers
**Gtm Motion**: Acquisition relies on retroactive log analysis during active post-mortems, allowing engineering teams to ingest recent outage data for free to experience the correlation engine. Expansion is driven by connecting live cross-cloud telemetry streams and shifting to a pay-per-resolved-incident model as the tool is adopted by additional service pods.
**Agent Channel**: Designed to list in the LangChain tool hub and emerging autonomous SRE integration catalogs as a callable query endpoint, allowing automated remediation agents to pull infrastructure state and root-cause context before executing fixes.
**Primary Channel**: The PagerDuty and Opsgenie integration directories, where DevOps teams actively search for alert correlation and incident context extensions to attach to their existing routing workflows.

## Startup Customer Journey

```mermaid
flowchart LR A[PagerDuty Directory] --> B[Post-Mortem Log File] --> C[Root Cause Correlation Report] --> D[Live Telemetry Stream] --> E[Enterprise Service Pod] --> F[Automated Remediation Runbook]
```

## 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 staging environment pilot connected to existing Datadog instances to prove the platform successfully identifies 5 intentionally injected infrastructure failures within 15 minutes each.
- 60-day limited production deployment on a single Kubernetes cluster to target a 50% decrease in manual log triage hours during Sev-1 outages.
**Target Metrics**:
- Target: 70% reduction in multi-cloud mean time to resolution (MTTR).
- Target: 15-minute maximum time from incident trigger to definitive root cause identification.
- Target: 0 dollars billed for unverified false alarms and transient system noise.
**Target Case Studies**:
- Enterprise DevOps team at a cloud-native SaaS: Transition from multi-hour log correlation war rooms to resolving Sev-1 outages via direct incident tickets that isolate specific Terraform misconfigurations.
- Mid-market FinTech Site Reliability Engineering (SRE) unit: Eliminate manual cross-referencing between Datadog and AWS CloudWatch by mapping telemetry spikes directly to live infrastructure state changes.
**Testimonial Targets**:
- Director of Site Reliability Engineering expressing relief that the usage-based pricing model aligns costs with actual resolved incidents rather than raw data storage volume.
- Lead DevOps Engineer confirming the platform successfully isolates cross-service latency bottlenecks without requiring engineers to manually hunt through monitoring dashboards.

## Startup Top Risks

**Risks**:
- Severity: existential · Description: Customers dispute the definition of a resolved incident to avoid paying, leaving the company liable for massive telemetry ingestion compute costs without matching revenue. · Mitigation Status: unmitigated
- Severity: high · Description: Enterprise security teams refuse to grant the deep cross-cloud IAM permissions required to build the contextual infrastructure state map. · Mitigation Status: in-progress
- Severity: high · Description: Datadog or Splunk bundle automated root-cause correlation into their widely deployed agents, neutralizing the product differentiation. · Mitigation Status: unmitigated
- Severity: moderate · Description: Major cloud providers throttle the specific telemetry APIs required to maintain real-time log ingestion streams. · Mitigation Status: in-progress

## Startup Competitors

- [Datadog](/Competitors/Datadog) — Incumbent
- [Splunk](/Competitors/Splunk) — Incumbent
- [Manual Log Correlation](/Competitors/Manual_Log_Correlation) — Status Quo
- [New Relic](/Competitors/New_Relic) — Observability Platform
- [Sumo Logic](/Competitors/Sumo_Logic) — Log Management
- [BigPanda](/Competitors/BigPanda) — AIOps Platform

## Startup Solution Stack

- [Incident Resolution Service](/Services/Incident_Resolution_Service) — Service-as-Software
- [Telemetry Correlation Agent](/Agents/Telemetry_Correlation_Agent) — Agent
- [State Topology Worker](/Agents/State_Topology_Worker) — Agent
- [Anomaly Detection Engine](/Software/Anomaly_Detection_Engine) — Software
- [Cloud Ingestion API](/Software/Cloud_Ingestion_API) — Software

## Startup Story Brand

**Hero**:
- **Need**: to be the strategic architect who prevents downtime, not the fire-fighter reading logs
- **Want**: to pinpoint the exact cause of Sev-1 outages across fragmented cloud environments
- **Identity**: the DevOps engineer managing complex multi-cloud Kubernetes clusters
**Plan**:
- Step: Trigger incident · Detail: An alert from your existing monitoring stack signals Eronata to begin its analysis phase.
- Step: Approve analysis · Detail: Review the unified root cause map that identifies the specific failing component and service.
- Step: Resolve outage · Detail: Execute the provided fix and only pay when the incident is successfully cleared.
**Guide**:
- **Empathy**: You shouldn't still be manually stitching cloud logs. Datadog wasn't built to map telemetry directly to your infrastructure's intended state.
**Problem**:
- **Villain**: telemetry tax
- **External**: SRE teams waste hours in Datadog and Splunk manually correlating log spikes to specific Terraform state changes during outages
- **Internal**: You feel drained by the pressure of the 'on-call' clock while scrolling through endless noise
- **Philosophical**: Engineering talent belongs in architecture and prevention, not in manual log-matching.
**Success**: You receive a definitive root cause within 15 minutes of an alert, allowing your team to deploy fixes while only paying for the resolution, not the data.
**One Liner**: What if you could skip the manual log correlation during an outage? Eronata correlates cross-cloud telemetry with infrastructure state, delivering definitive root causes in minutes.
**Positioning**:
- **So That**: identify the specific infrastructure failure without paying for raw data
- **Unlike**: manual log correlation in Splunk
- **For Whom**: DevOps teams at cloud-native enterprises
- **Category**: Root cause analysis for multi-cloud
**Call To Action**:
- **Direct**: Resolve an incident
- **Transitional**: View incident analysis sample
**Failure Stakes**:
- Millions lost in downtime
- Burnt-out SRE teams
- Exploding data storage costs
**Transformation**:
- **To**: the infrastructure's lead architect
- **From**: the log-drilling SRE stuck in Splunk
**Controlling Idea**: Engineers should pay for resolved incidents, not the logs that cause them.

## Startup Token Hero

**Genre**: founding-hypothesis
**Rendered**: What if you could skip the manual log correlation during an outage? Eronata correlates cross-cloud telemetry with infrastructure state, delivering definitive root causes in minutes.
**Mechanism**: spine-derived-v1
**Template Id**: spine-founding-hypothesis
**Vocab Fingerprint**: 240d033dd500e5e4

## Startup Token Positioning

**Genre**: moore-positioning
**Rendered**: Root cause analysis for multi-cloud for DevOps teams at cloud-native enterprises. Unlike manual log correlation in Splunk — identify the specific infrastructure failure without paying for raw data.
**Mechanism**: spine-derived-v1
**Template Id**: spine-moore-positioning
**Vocab Fingerprint**: 4af2cd0c0edda076

## Startup Token Pitch Deck

**Genre**: pitch-deck
**Rendered**: Problem: SRE teams waste hours in Datadog and Splunk manually correlating log spikes to specific Terraform state changes during outages
Solution: What if you could skip the manual log correlation during an outage? Eronata correlates cross-cloud telemetry with infrastructure state, delivering definitive root causes in minutes.
Customer: DevOps teams at cloud-native enterprises
Unlike: manual log correlation in Splunk
**Mechanism**: spine-derived-v1
**Template Id**: spine-pitch-deck
**Vocab Fingerprint**: 23241a0227d5d34e

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

**Pain**: SRE teams waste hours in Datadog and Splunk manually correlating log spikes to specific Terraform state changes during outages
**Metrics**: Target: You receive a definitive root cause within 15 minutes of an alert, allowing your team to deploy fixes while only paying for the resolution, not the data.
**Rendered**: Pain: SRE teams waste hours in Datadog and Splunk manually correlating log spikes to specific Terraform state changes during outages
Economic buyer: VP of Cloud Operations
Metrics: Target: You receive a definitive root cause within 15 minutes of an alert, allowing your team to deploy fixes while only paying for the resolution, not the data.
Competition: manual log correlation in Splunk
**Mechanism**: spine-derived-v1
**Competition**: manual log correlation in Splunk
**Economic Buyer**: VP of Cloud Operations
**Vocab Fingerprint**: 9b6c92464706cea5

## Startup Token Cold Email

**Genre**: cold-email
**Rendered**: Subject: Root cause analysis for multi-cloud for DevOps teams at cloud-native enterprises

DevOps teams at cloud-native enterprises — SRE teams waste hours in Datadog and Splunk manually correlating log spikes to specific Terraform state changes during outages What if you could skip the manual log correlation during an outage? Eronata correlates cross-cloud telemetry with infrastructure state, delivering definitive root causes in minutes.
**Mechanism**: spine-derived-v1
**Template Id**: spine-cold-email
**Vocab Fingerprint**: 58cd0049e25dc540

## Startup Token Agent Spec

**Genre**: ai-agent-spec
**Rendered**: Root cause analysis for multi-cloud. What if you could skip the manual log correlation during an outage? Eronata correlates cross-cloud telemetry with infrastructure state, delivering definitive root causes in minutes. Serves DevOps teams at cloud-native enterprises.
**Mechanism**: spine-derived-v1
**Template Id**: spine-ai-agent-spec
**Vocab Fingerprint**: a5ac4a6ec88e8116

## Neighborhood

### Candidate solutions

- [Grower Packout Settlement Disputes](/Problems/Grower_Packout_Settlement_Disputes) — candidate solution for · Problems

### What it offers

- [Eronata Root Cause Engine](/Software/Eronata_Root_Cause_Engine) — offers · Software

### Competitors

- [Manual Log Correlation](/Competitors/Manual_Log_Correlation) — competes with · Competitors
- [New Relic](/Competitors/New_Relic) — competes with · Competitors
- [Sumo Logic](/Competitors/Sumo_Logic) — competes with · Competitors
- [Datadog](/Competitors/Datadog) — competes with · Competitors
- [Splunk](/Competitors/Splunk) — competes with · Competitors
- [BigPanda](/Competitors/BigPanda) — competes with · Competitors

### Embodies

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

### Composed of

- [Cloud Ingestion API](/Software/Cloud_Ingestion_API) — composes · Software
- [State Topology Worker](/Agents/State_Topology_Worker) — composes · Agents
- [Telemetry Correlation Agent](/Agents/Telemetry_Correlation_Agent) — composes · Agents
- [Anomaly Detection Engine](/Software/Anomaly_Detection_Engine) — composes · Software
- [Incident Resolution Service](/Services/Incident_Resolution_Service) — composes · Services

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