# Outagetile

*/Startups/Outagetile*

## Startup Overview

This system maps microservice architectures to pinpoint cascading failures in real time. It monitors traffic flow and API latency across distributed networks, translating complex service dependencies into visual degradation heatmaps. Engineering and site reliability teams use this topography to identify the exact node causing an outage before the failure propagates through the entire application stack.

Traditional monitoring tools like PagerDuty or Datadog Service Map require extensive manual tagging, SDK integration, and complex configuration to yield usable topology data. Instead, this engine operates with entirely zero instrumentation. It passively ingests network traffic to immediately generate a visually deterministic map of service health without altering existing codebases.

By eliminating the setup friction typical of Atlassian Statuspage and legacy observability suites, the platform deploys instantly to provide immediate architectural visibility. Incident responders rely on this visual evidence to bypass alert floods, instantly isolating degraded dependencies and routing remediation efforts directly to the root cause.

## Startup Founding Hypothesis

**Approach**: that renders microservice dependencies into visual degradation heatmaps
**Competitors**:
- [PagerDuty](/Competitors/PagerDuty)
- [Datadog Service Map](/Competitors/Datadog_Service_Map)
- [Atlassian Statuspage](/Competitors/Atlassian_Statuspage)
**Differentiator2x2**: visually deterministic and entirely zero-instrumentation for immediate deployment

## Startup Solution Coordinate

**Solution**: [Outagetile Dependency Map](/Software/Outagetile_Dependency_Map)

## Startup Position2x2

```mermaid
quadrantChart
    title Service Degradation Visibility vs Setup Effort
    x-axis Heavy Instrumentation --> Zero-Instrumentation
    y-axis Text/List Driven --> Visually Deterministic
    Datadog Service Map: [0.15, 0.85]
    PagerDuty: [0.25, 0.30]
    Atlassian Statuspage: [0.10, 0.20]
    Outagetile: [0.90, 0.90]
```

## Startup Offer

**Proof**:
- Targeting a 40% reduction in mean-time-to-resolution (MTTR) for mid-market site reliability engineering teams.
- Aiming to isolate cascading failure sources up to 3x faster than manual log correlation methods.
- Designed to autonomously map environments with over 1,000 microservice edges in under 5 minutes.
**Tiers**:
- Name: Core Topography · Price: ~$300–$500/mo per cluster · Inclusions: Real-time visual degradation heatmaps and automatic dependency mapping for a single containerized environment, capped at 250 tracked services.
- Name: Distributed Grid · Price: ~$1,200–$2,500/mo · Inclusions: Cross-region dependency mapping, historical incident playback, and API access designed for multi-cluster environments up to 2,000 tracked services.
**Guarantee**: If Outagetile does not produce a complete, visually accurate dependency map of your environment within 20 minutes of initial deployment, you receive a full refund for your first billing cycle.
**Business Function**: ProvideService
**Objection Handlers**:
- Objection: Zero-instrumentation tools miss deep application context. Rebuttal: Outagetile is designed to parse standard HTTP/gRPC traffic at the network layer to infer service health without requiring active trace injection.
- Objection: Datadog already provides a Service Map. Rebuttal: Legacy service maps require manual tracing libraries in every codebase; this approach is designed to instantly map untraced third-party and legacy black-box components.
- Objection: Continuous network parsing will add unacceptable overhead to our production nodes. Rebuttal: The intended data collection architecture operates in kernel space, targeting less than 1% CPU overhead on host machines.
**Pricing Architecture**: Tiered
**Agent Checkout Support**:
- agentic-commerce-protocol

## Startup Brand

**Voice**: Technical and direct, prioritizing immediate diagnostic clarity for incident responders.
**Tagline**: See exactly where your microservices degrade without manual instrumentation.
**Icon Concept**: fuse
**Palette Intent**: electric-signal
**Visual Identity**: High-contrast dark interfaces accented with neon amber and vivid crimson surface failing architecture nodes, supported by rigid monospaced typography for precise incident logs.
**Archetype Reference**: the-sage

## Startup Buyer Chain

**Chain**: Outagetile → SRE / DevOps Lead → Engineering Teams
**Gtm Motion**: Bottom-up adoption begins when individual Site Reliability Engineers deploy the zero-instrumentation mapper during an active incident or post-mortem. Expansion happens virally across the organization as engineers paste the generated visual heatmap URLs into Slack incident channels or Jira tickets, driving other squads to adopt it for their specific service domains.
**Agent Channel**: Designed to list as an available diagnostic tool in autonomous DevOps agent registries, such as the intended LangChain tool directory or OpenAI plugin catalog, allowing AI troubleshooting agents to pull degradation heatmaps during automated incident triage.
**Primary Channel**: Organic discovery via developer communities like Hacker News and r/devops, or direct search intent for zero-instrumentation microservice mapping tools during infrastructure modernization.

## Startup Customer Journey

```mermaid
flowchart LR;A[Developer Communities]-->B[Zero-Instrumentation Mapper];B-->C[Visual Degradation Heatmap];C-->D[Site Reliability Engineers];D-->E[Slack Incident Channels];E-->F[Distributed Grid];F-->G[DevOps Agent Registries];
```

## Startup Proof Points

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

**Pilot Goals**:
- A 14-day deployment in a single containerized environment to prove Outagetile autonomously generates a complete, visually accurate dependency map of up to 250 services in under 20 minutes.
- A 30-day multi-cluster evaluation targeting up to 2,000 tracked services to validate the accuracy of cross-region dependency mapping via standard HTTP/gRPC traffic parsing.
**Target Metrics**:
- Target: 40% reduction in mean-time-to-resolution (MTTR) for multi-service incidents
- Target: <5 minute initial environment mapping time for architectures with over 1,000 microservice edges
- Aim: 3x faster isolation of cascading failure sources compared to manual trace correlation
- Target: <1% host CPU overhead during continuous kernel-space network data collection
**Target Case Studies**:
- A mid-market fintech Site Reliability Engineering team transitioning from manual log correlation to visual degradation heatmaps, targeting a drastic reduction in time spent isolating failing third-party APIs during multi-cluster outages.
- A large e-commerce platform DevOps group deploying the tool to map over 1,000 uninstrumented microservices and legacy black-box components in under 20 minutes without modifying existing codebases.
- A SaaS infrastructure provider utilizing historical incident playback to conduct exact root-cause analysis, aiming to map cross-region cascading failures rather than relying on assumed service dependencies.
**Testimonial Targets**:
- A Lead Site Reliability Engineer praising the zero-instrumentation deployment, specifically noting how the tool instantly mapped untraced legacy components without requiring code changes.
- A VP of Infrastructure validating the historical incident playback feature, expressing relief at seeing exact cross-region dependency states during a simulated P1 outage.
- A DevOps Manager highlighting the kernel-space parsing architecture, confirming that continuous service health inference added negligible overhead to their production nodes.

## Startup Top Risks

**Risks**:
- Severity: existential · Description: Mutual TLS and strict service mesh encryption block the zero-instrumentation network analysis, rendering the mapping engine blind. · Mitigation Status: unmitigated
- Severity: high · Description: Datadog bundles a zero-instrumentation eBPF module into their existing Service Map tier, instantly capturing the target market. · Mitigation Status: unmitigated
- Severity: high · Description: Passive traffic interception incurs unacceptable CPU overhead on host nodes in environments exceeding ten thousand requests per second. · Mitigation Status: in-progress
- Severity: moderate · Description: The visual heatmap becomes an illegible cluster when rendering enterprise architectures containing more than five hundred interacting microservices. · Mitigation Status: in-progress

## Startup Competitors

- [PagerDuty](/Competitors/PagerDuty) — Incident Alerting
- [Datadog Service Map](/Competitors/Datadog_Service_Map) — Legacy Observability
- [Atlassian Statuspage](/Competitors/Atlassian_Statuspage) — Public Status
- [Dynatrace Smartscape](/Competitors/Dynatrace_Smartscape) — Heavy Instrumentation
- [Static Architecture Diagrams](/Competitors/Static_Architecture_Diagrams) — Status Quo

## Startup Solution Stack

- [Degradation Heatmap Service](/Services/Degradation_Heatmap_Service) — Service-as-Software
- [Dependency Inference Agent](/Agents/Dependency_Inference_Agent) — Agent
- [Traffic Observation Engine](/Software/Traffic_Observation_Engine) — Software
- [Topology Rendering API](/Software/Topology_Rendering_API) — Software

## Startup Story Brand

**Hero**:
- **Need**: to be the strategic architect who prevents downtime, not the firefighter chasing logs
- **Want**: to isolate the root cause of cascading microservice failures instantly
- **Identity**: the Site Reliability Engineer at a mid-market engineering firm
**Plan**:
- Step: Deploy · Detail: Drop the kernel-space agent into your containerized environment to begin immediate network-layer parsing.
- Step: Validate · Detail: Confirm your complete service topography and dependencies as they appear automatically in high-contrast heatmaps.
- Step: Resolve · Detail: Watch neon amber and crimson indicators surface failing nodes to fix outages 3x faster than manual methods.
**Guide**:
- **Empathy**: Does your incident response process still stall because of invisible third-party service failures?
**Problem**:
- **Villain**: untraced dependency sprawl
- **External**: Identifying service degradation in Datadog Service Map requires manual tracing libraries across every codebase, leaving legacy black-box components as invisible failure points.
- **Internal**: You feel blind during high-severity incidents while waiting for manual log correlation across a dozen clusters.
- **Philosophical**: Every engineering lead deserves visual clarity of their architecture — not a scavenger hunt through fragmented distributed traces.
**Success**: You view a live, zero-instrumentation map of every service dependency, pinpointing failures the moment they occur.
**One Liner**: Invisible dependency failures cost engineering teams hours of downtime. Outagetile maps every microservice instantly so responders isolate and fix outages 3x faster.
**Positioning**:
- **So That**: isolate cascading failures across untraced legacy and third-party components
- **Unlike**: Datadog Service Map
- **For Whom**: SRE teams in mid-market engineering firms
- **Category**: Zero-instrumentation service dependency mapping
**Call To Action**:
- **Direct**: Deploy Core Topography
- **Transitional**: View Sample Heatmap
**Failure Stakes**:
- Extended Mean Time to Resolution
- Burnout from blind incident firefighting
- Lost revenue during untraced cascading failures
**Transformation**:
- **To**: mapping failures instantly instead of manual tracing
- **From**: the lead buried in manual PagerDuty log correlation
**Controlling Idea**: Visualizing service degradation should require zero code instrumentation.

## Startup Token Hero

**Genre**: founding-hypothesis
**Rendered**: Invisible dependency failures cost engineering teams hours of downtime. Outagetile maps every microservice instantly so responders isolate and fix outages 3x faster.
**Mechanism**: spine-derived-v1
**Template Id**: spine-founding-hypothesis
**Vocab Fingerprint**: 5f5c371e497af170

## Startup Token Positioning

**Genre**: moore-positioning
**Rendered**: Zero-instrumentation service dependency mapping for SRE teams in mid-market engineering firms. Unlike Datadog Service Map — isolate cascading failures across untraced legacy and third-party components.
**Mechanism**: spine-derived-v1
**Template Id**: spine-moore-positioning
**Vocab Fingerprint**: d60dee48384ed45d

## Startup Token Pitch Deck

**Genre**: pitch-deck
**Rendered**: Problem: Identifying service degradation in Datadog Service Map requires manual tracing libraries across every codebase, leaving legacy black-box components as invisible failure points.
Solution: Invisible dependency failures cost engineering teams hours of downtime. Outagetile maps every microservice instantly so responders isolate and fix outages 3x faster.
Customer: SRE teams in mid-market engineering firms
Unlike: Datadog Service Map
**Mechanism**: spine-derived-v1
**Template Id**: spine-pitch-deck
**Vocab Fingerprint**: 017b819987bff007

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

**Pain**: Identifying service degradation in Datadog Service Map requires manual tracing libraries across every codebase, leaving legacy black-box components as invisible failure points.
**Metrics**: Target: You view a live, zero-instrumentation map of every service dependency, pinpointing failures the moment they occur.
**Rendered**: Pain: Identifying service degradation in Datadog Service Map requires manual tracing libraries across every codebase, leaving legacy black-box components as invisible failure points.
Economic buyer: SRE / DevOps Lead
Metrics: Target: You view a live, zero-instrumentation map of every service dependency, pinpointing failures the moment they occur.
Competition: Datadog Service Map
**Mechanism**: spine-derived-v1
**Competition**: Datadog Service Map
**Economic Buyer**: SRE / DevOps Lead
**Vocab Fingerprint**: 62d02992f90fd915

## Startup Token Cold Email

**Genre**: cold-email
**Rendered**: Subject: Zero-instrumentation service dependency mapping for SRE teams in mid-market engineering firms

SRE teams in mid-market engineering firms — Identifying service degradation in Datadog Service Map requires manual tracing libraries across every codebase, leaving legacy black-box components as invisible failure points. Invisible dependency failures cost engineering teams hours of downtime. Outagetile maps every microservice instantly so responders isolate and fix outages 3x faster.
**Mechanism**: spine-derived-v1
**Template Id**: spine-cold-email
**Vocab Fingerprint**: c6ca32e9299033c1

## Startup Token Agent Spec

**Genre**: ai-agent-spec
**Rendered**: Zero-instrumentation service dependency mapping. Invisible dependency failures cost engineering teams hours of downtime. Outagetile maps every microservice instantly so responders isolate and fix outages 3x faster. Serves SRE teams in mid-market engineering firms.
**Mechanism**: spine-derived-v1
**Template Id**: spine-ai-agent-spec
**Vocab Fingerprint**: 8de05294191e2603

## Neighborhood

### Candidate solutions

- [Frontline Staff Churn](/Problems/Frontline_Staff_Churn) — candidate solution for · Problems

### Composed of

- [Clock Telemetry API](/Software/Clock_Telemetry_API) — composes · Software
- [Burnout Detection Worker](/Agents/Burnout_Detection_Worker) — composes · Agents
- [Shift Broker Agent](/Agents/Shift_Broker_Agent) — composes · Agents
- [Flight Risk Interception Service](/Services/Flight_Risk_Interception_Service) — composes · Services
- [Roster Sync Engine](/Software/Roster_Sync_Engine) — composes · Software
- [Roster Telemetry API](/Software/Roster_Telemetry_API) — composes · Software
- [Flight Risk Engine](/Software/Flight_Risk_Engine) — composes · Software
- [Swap Match Agent](/Agents/Swap_Match_Agent) — composes · Agents
- [Burnout Intercept Agent](/Agents/Burnout_Intercept_Agent) — composes · Agents
- [Shift Broker Service](/Services/Shift_Broker_Service) — composes · Services
- [Degradation Heatmap Service](/Services/Degradation_Heatmap_Service) — composes · Services
- [Dependency Inference Agent](/Agents/Dependency_Inference_Agent) — composes · Agents
- [Traffic Observation Engine](/Software/Traffic_Observation_Engine) — composes · Software
- [Topology Rendering API](/Software/Topology_Rendering_API) — composes · Software

### Embodies

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

### What it offers

- [Shift Anchor](/Services/Shift_Anchor) — offers · Services
- [Shift Mediation Desk](/Services/Shift_Mediation_Desk) — offers · Services
- [Outagetile Dependency Map](/Software/Outagetile_Dependency_Map) — offers · Software

### Competitors

- [UKG Pro](/Competitors/UKG_Pro) — competes with · Competitors
- [HotSchedules](/Competitors/HotSchedules) — competes with · Competitors
- [When I Work](/Competitors/When_I_Work) — competes with · Competitors
- [group text threads](/Competitors/group_text_threads) — competes with · Competitors
- [Atlassian Statuspage](/Competitors/Atlassian_Statuspage) — competes with · Competitors
- [Datadog Service Map](/Competitors/Datadog_Service_Map) — competes with · Competitors
- [Dynatrace Smartscape](/Competitors/Dynatrace_Smartscape) — competes with · Competitors
- [PagerDuty](/Competitors/PagerDuty) — competes with · Competitors
- [Static Architecture Diagrams](/Competitors/Static_Architecture_Diagrams) — competes with · Competitors

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