# Wholisual

*/Startups/Wholisual*

## Startup Overview

This infrastructure observability platform extracts interactive topology maps directly from raw eBPF telemetry. It captures network flows and application requests at the kernel level, rendering exact system state in real time.

Site reliability and platform engineering teams struggle to track deeply nested dependencies, often resorting to manual architecture diagramming or complex instrumentation via application performance monitors like Datadog and Dynatrace. These methods demand ongoing codebase maintenance and frequently omit critical communication pathways.

Operating entirely in the kernel space, the solution deploys without code changes, SDKs, or sidecar proxies. It discovers and maps every active network connection automatically, surfacing a complete structural view inclusive of undocumented shadow services and isolated legacy workloads.

## Startup Founding Hypothesis

**Approach**: that extracts interactive topology maps from raw eBPF telemetry
**Competitors**:
- [Datadog](/Competitors/Datadog)
- [Dynatrace](/Competitors/Dynatrace)
- [manual architecture diagramming](/Competitors/manual_architecture_diagramming)
**Differentiator2x2**: deployed without code changes and inclusive of undocumented shadow services

## Startup Solution Coordinate

**Solution**: [eBPF Topology Mapper](/Software/eBPF_Topology_Mapper)

## Startup Position2x2

```mermaid
quadrantChart
  title Position vs Competitors
  x-axis Heavy Code Changes --> Zero-Code Deployment
  y-axis Misses Shadow Services --> Captures Shadow Services
  quadrant-1 Automated Discovery
  quadrant-2 Blind eBPF
  quadrant-3 Legacy APM
  quadrant-4 Static Models
  Datadog: [0.25, 0.35]
  Dynatrace: [0.35, 0.45]
  Manual Diagramming: [0.90, 0.15]
  Wholisual: [0.85, 0.85]
```

## Startup Offer

**Proof**:
- Targeting 100% automated service discovery across entire Kubernetes environments without requiring any code changes.
- Aiming to replace manual architecture diagramming with near-real-time generated topology maps.
- Designed to capture and map transient network dependencies that traditional application performance monitors routinely miss.
**Tiers**:
- Name: Cluster Discovery · Price: ~$15–$30 per node/mo · Inclusions: Continuous eBPF-based topology extraction, automated dependency mapping, and identification of undocumented services for up to 50 nodes.
- Name: Enterprise Topology · Price: ~$40–$75 per node/mo · Inclusions: Unlimited node support, historical state retention, multi-cluster federation, and intended data-export integrations with existing APM platforms.
**Guarantee**: If the platform fails to discover and map at least one uninstrumented shadow service within the first 14 days of deployment, the first month of node usage is refunded in full.
**Business Function**: ProvideService
**Objection Handlers**:
- Overhead concerns: eBPF agents consume too much compute. -> Built on lightweight kernel hooks designed to consume minimal host CPU overhead compared to traditional sidecars.
- Security risks: Kernel-level network visibility is a compliance hazard. -> Intended to deploy entirely within the customer environment, dropping payload data and only extracting connection metadata.
- Redundancy: We already use Datadog or Dynatrace. -> This supplements existing APMs by specifically mapping the uninstrumented shadow services and third-party dependencies those platforms cannot trace.
**Pricing Architecture**: UsageMeter
**Agent Checkout Support**:
- agentic-commerce-protocol

## Startup Brand

**Voice**: Clinical and objective, prioritizing deep technical exactness over marketing speak
**Tagline**: Map your exact, undocumented service topology without code changes
**Icon Concept**: stethoscope
**Palette Intent**: electric-signal
**Visual Identity**: A dark-mode interface uses sharp, high-contrast neon green lines against deep charcoal, echoing raw terminal traces transforming into precise topological layouts.
**Archetype Reference**: the-sage

## Startup Buyer Chain

**Chain**: Wholisual -> Platform Engineers / SREs -> Engineering Organizations
**Gtm Motion**: Acquisition happens through a self-serve freemium tier where individual DevOps engineers deploy the eBPF agent on a single Kubernetes cluster to map shadow services. Expansion occurs by upselling cross-cluster topology views and incident management integrations to Platform Engineering directors.
**Agent Channel**: Designed to list in AIOps integration catalogs and LangChain tool directories, allowing autonomous SRE agents to discover and query the topology map during root-cause analysis.
**Primary Channel**: Developer communities and technical forums like GitHub, Hacker News, and r/kubernetes, capturing infrastructure engineers searching for eBPF-based service mapping.

## Startup Customer Journey

```mermaid
flowchart LR;A[Technical Forums]-->B[Freemium eBPF Agent];B-->C[Shadow Service Map];C-->D[Continuous Topology Extractor];D-->E[Cross-Cluster Federation];E-->F[Autonomous SRE Agent Query];
```

## 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 single-cluster deployment pilot targeting the definitive discovery and mapping of at least one previously undocumented internal service or third-party dependency.
- A 30-day multi-cluster federation pilot aiming to continuously export connection metadata into the customer's existing APM platform while maintaining under 1 percent CPU overhead on host nodes.
**Target Metrics**:
- Target: 0 code changes required for complete Kubernetes environment visibility.
- Aim: Under 1 percent host CPU utilization overhead for the eBPF extraction agent.
- Target: 100 percent discovery rate of active but previously uninstrumented shadow services.
- Aim: 14-day time-to-value for identifying and mapping at least one undocumented network dependency.
**Target Case Studies**:
- Mid-market FinTech engineering team managing multi-cluster environments: Target transformation is shifting from outdated manual architecture diagrams to near-real-time automated topology maps that expose undocumented internal APIs.
- Enterprise SaaS infrastructure team auditing cluster security: Target transformation is the immediate identification and mapping of transient shadow services and third-party dependencies completely missed by their primary APM.
- High-growth e-commerce platform migrating legacy monoliths to Kubernetes: Target transformation is achieving 100 percent automated service discovery across multiple clusters without requiring a single code change or sidecar redeployment.
**Testimonial Targets**:
- Director of Platform Engineering: Target sentiment is relief at possessing an accurate, real-time map of all service dependencies without needing to force developers to update static documentation.
- Lead Cloud Security Architect: Target sentiment is confidence in the organization's compliance posture after utilizing the metadata-only extraction to discover legacy shadow services that bypassed standard instrumentation.
- Site Reliability Engineer: Target sentiment is appreciation for the lightweight eBPF architecture, specifically validating that it captures transient network connections without the severe compute drag of traditional sidecar proxies.

## Startup Top Risks

**Risks**:
- Severity: existential · Description: Kernel-level eBPF probes cause production instability or unexpected latency, leading to immediate enterprise rejection of the agent. · Mitigation Status: unmitigated
- Severity: high · Description: Datadog or Dynatrace aggressively commoditize eBPF-based topology mapping, bundling it into their existing APM suites at no extra cost. · Mitigation Status: unmitigated
- Severity: high · Description: Enterprise security and compliance teams block deployment due to strict policies against third-party tools requiring kernel-level execution privileges. · Mitigation Status: in-progress
- Severity: moderate · Description: Discovered shadow services lack sufficient application-layer context, generating overwhelmingly noisy topology maps that engineers cannot reliably action. · Mitigation Status: in-progress

## Startup Competitors

- [Datadog](/Competitors/Datadog) — Incumbent APM
- [Dynatrace](/Competitors/Dynatrace) — Incumbent APM
- [Manual Architecture Diagramming](/Competitors/Manual_Architecture_Diagramming) — Status Quo
- [New Relic APM](/Competitors/New_Relic_APM) — Incumbent APM
- [Pixie Observability](/Competitors/Pixie_Observability) — eBPF Alternative
- [Coroot Platform](/Competitors/Coroot_Platform) — eBPF Alternative

## Startup Solution Stack

- [Topology Mapping Service](/Services/Topology_Mapping_Service) — Service-as-Software
- [Telemetry Extraction Agent](/Agents/Telemetry_Extraction_Agent) — Agent
- [Shadow Discovery Worker](/Agents/Shadow_Discovery_Worker) — Agent
- [Kernel Parsing Engine](/Software/Kernel_Parsing_Engine) — Software
- [Kernel Telemetry API](/Software/Kernel_Telemetry_API) — Software

## Startup Story Brand

**Hero**:
- **Need**: to be the architect who masters the cluster's hidden reality
- **Want**: to see every active service and dependency without manual instrumentation
- **Identity**: the platform engineer managing a complex Kubernetes environment
**Plan**:
- Step: Deploy agents · Detail: Install lightweight eBPF hooks across your nodes to begin silent telemetry collection.
- Step: Verify topology · Detail: Watch as the system automatically discovers and visualizes every active service connection.
- Step: Export maps · Detail: Push exact, real-time dependency data into your existing documentation or APM dashboards.
**Guide**:
- **Empathy**: Does your service mapping still omit uninstrumented third-party dependencies?
**Problem**:
- **Villain**: shadow services
- **External**: System architecture diagrams in Miro or Lucidchart diverge from reality as uninstrumented third-party APIs and undocumented microservices escape Datadog traces.
- **Internal**: You feel blind to the transient network dependencies that could trigger the next production outage.
- **Philosophical**: Why should platform teams accept incomplete observability when raw kernel telemetry is available to reveal the truth?
**Success**: Your entire cluster architecture is visible in real-time, including every shadow service and third-party API that traditional APMs miss.
**One Liner**: Instead of manual architecture diagramming, Wholisual extracts live interactive topology maps from raw eBPF telemetry — revealing every undocumented service without a single code change.
**Positioning**:
- **So That**: reveal undocumented shadow services without modifying your cluster architecture
- **Unlike**: manual architecture diagramming and traditional APMs
- **For Whom**: platform engineers in complex Kubernetes environments
- **Category**: eBPF-based service topology discovery
**Call To Action**:
- **Direct**: Map your cluster
- **Transitional**: View sample topology
**Failure Stakes**:
- Critical outages from hidden dependencies
- Wasted hours manual diagramming
- Compliance gaps from unmonitored traffic
**Transformation**:
- **To**: the infrastructure's topology authority
- **From**: a manual diagrammer chasing stale Miro boards
**Controlling Idea**: Infrastructure visibility should come from the kernel, not manual code instrumentation.

## Startup Token Hero

**Genre**: founding-hypothesis
**Rendered**: Instead of manual architecture diagramming, Wholisual extracts live interactive topology maps from raw eBPF telemetry — revealing every undocumented service without a single code change.
**Mechanism**: spine-derived-v1
**Template Id**: spine-founding-hypothesis
**Vocab Fingerprint**: d385381f636ae194

## Startup Token Positioning

**Genre**: moore-positioning
**Rendered**: eBPF-based service topology discovery for platform engineers in complex Kubernetes environments. Unlike manual architecture diagramming and traditional APMs — reveal undocumented shadow services without modifying your cluster architecture.
**Mechanism**: spine-derived-v1
**Template Id**: spine-moore-positioning
**Vocab Fingerprint**: f475e901a8a87e04

## Startup Token Pitch Deck

**Genre**: pitch-deck
**Rendered**: Problem: System architecture diagrams in Miro or Lucidchart diverge from reality as uninstrumented third-party APIs and undocumented microservices escape Datadog traces.
Solution: Instead of manual architecture diagramming, Wholisual extracts live interactive topology maps from raw eBPF telemetry — revealing every undocumented service without a single code change.
Customer: platform engineers in complex Kubernetes environments
Unlike: manual architecture diagramming and traditional APMs
**Mechanism**: spine-derived-v1
**Template Id**: spine-pitch-deck
**Vocab Fingerprint**: f9f353f627be275f

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

**Pain**: System architecture diagrams in Miro or Lucidchart diverge from reality as uninstrumented third-party APIs and undocumented microservices escape Datadog traces.
**Metrics**: Target: Your entire cluster architecture is visible in real-time, including every shadow service and third-party API that traditional APMs miss.
**Rendered**: Pain: System architecture diagrams in Miro or Lucidchart diverge from reality as uninstrumented third-party APIs and undocumented microservices escape Datadog traces.
Economic buyer: Platform Engineers / SREs
Metrics: Target: Your entire cluster architecture is visible in real-time, including every shadow service and third-party API that traditional APMs miss.
Competition: manual architecture diagramming and traditional APMs
**Mechanism**: spine-derived-v1
**Competition**: manual architecture diagramming and traditional APMs
**Economic Buyer**: Platform Engineers / SREs
**Vocab Fingerprint**: bba9abd89dd12ce9

## Startup Token Cold Email

**Genre**: cold-email
**Rendered**: Subject: eBPF-based service topology discovery for platform engineers in complex Kubernetes environments

platform engineers in complex Kubernetes environments — System architecture diagrams in Miro or Lucidchart diverge from reality as uninstrumented third-party APIs and undocumented microservices escape Datadog traces. Instead of manual architecture diagramming, Wholisual extracts live interactive topology maps from raw eBPF telemetry — revealing every undocumented service without a single code change.
**Mechanism**: spine-derived-v1
**Template Id**: spine-cold-email
**Vocab Fingerprint**: 0ac1bb07047531a1

## Startup Token Agent Spec

**Genre**: ai-agent-spec
**Rendered**: eBPF-based service topology discovery. Instead of manual architecture diagramming, Wholisual extracts live interactive topology maps from raw eBPF telemetry — revealing every undocumented service without a single code change. Serves platform engineers in complex Kubernetes environments.
**Mechanism**: spine-derived-v1
**Template Id**: spine-ai-agent-spec
**Vocab Fingerprint**: 1759883806db85e3

## Neighborhood

### Candidate solutions

- [Standardize Visual Color Grading](/Problems/Standardize_Visual_Color_Grading) — candidate solution for · Problems

### Composed of

- [Topology Derivation Service](/Services/Topology_Derivation_Service) — composes · Services
- [Kernel Parsing Engine](/Software/Kernel_Parsing_Engine) — composes · Software
- [Kernel Telemetry API](/Software/Kernel_Telemetry_API) — composes · Software
- [Telemetry Extraction Agent](/Agents/Telemetry_Extraction_Agent) — composes · Agents
- [Shadow Discovery Worker](/Agents/Shadow_Discovery_Worker) — composes · Agents

### What it offers

- [eBPF Topology Mapper](/Software/eBPF_Topology_Mapper) — offers · Software

### Competitors

- [Coroot Platform](/Competitors/Coroot_Platform) — competes with · Competitors
- [Datadog](/Competitors/Datadog) — competes with · Competitors
- [Dynatrace](/Competitors/Dynatrace) — competes with · Competitors
- [Manual Architecture Diagramming](/Competitors/Manual_Architecture_Diagramming) — competes with · Competitors
- [New Relic APM](/Competitors/New_Relic_APM) — competes with · Competitors
- [Pixie Observability](/Competitors/Pixie_Observability) — competes with · Competitors

### Embodies

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

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