# Compass

*/Startups/Compass*

## Startup Overview

This system operates as an autonomous crawler that scans internal databases, code repositories, and data warehouses to map data lineage. It extracts dependencies and tracks how data elements flow and transform across organizational infrastructure without requiring manual input or schema declarations.

Data engineering and governance teams typically rely on manual spreadsheet tracking or heavy, enterprise-wide deployments like Collibra and Alation to understand data provenance. These legacy approaches require extensive upfront configuration, mandatory software agents, and constant manual updates to remain accurate. This solution eliminates the deployment overhead by indexing the actual state of data environments directly from the source code and logs.

Instead of charging for seat licenses or deployment scale, the billing model is tied strictly to verified lineage outcomes. The zero-integration architecture allows teams to run the crawler and immediately generate accurate dependency maps. Organizations pay only for the exact data flows successfully traced and verified, aligning software cost directly with functional data visibility.

## Startup Founding Hypothesis

**Approach**: that crawls internal repositories to trace data lineage
**Competitors**:
- [Collibra](/Competitors/Collibra)
- [Alation](/Competitors/Alation)
- [manual spreadsheet tracking](/Competitors/manual_spreadsheet_tracking)
**Differentiator2x2**: zero-integration and priced purely on verified lineage outcomes

## Startup Solution Coordinate

**Solution**: [Data Lineage Tracer](/Services/Data_Lineage_Tracer)

## Startup Position2x2

```mermaid
quadrantChart
    title Market Positioning
    x-axis "Complex Setup" --> "Zero-Integration"
    y-axis "Fixed Licensing" --> "Outcome-Based Pricing"
    quadrant-1 "Outcome-driven & Seamless"
    quadrant-2 "Complex Setup & Outcome Pricing"
    quadrant-3 "Heavy Enterprise & Fixed Cost"
    quadrant-4 "Light Setup & Fixed Cost"
    Collibra: [0.15, 0.25]
    Alation: [0.25, 0.30]
    manual spreadsheet tracking: [0.80, 0.10]
    Compass: [0.85, 0.85]
```

## Startup Offer

**Proof**:
- Targeting accurate lineage mapping of deeply nested transformation models without requiring live database access.
- Aiming to replace weeks of manual spreadsheet documentation for migrating enterprise data teams.
- Designed to parse standard analytics repositories and deliver complete lineage graphs in under 48 hours.
**Tiers**:
- Name: Targeted Pipeline Discovery · Price: ~$15–$30 per verified connection · Inclusions: On-demand repository crawling for specific data pipelines, delivering documented lineage paths from source file to destination table.
- Name: Warehouse Batch Scan · Price: ~$5,000–$12,000 per 1,000 verified links · Inclusions: Comprehensive bulk scanning of internal analytics repositories designed for large-scale data warehouse migrations, including priority compute.
**Guarantee**: Clients are billed exclusively for verified lineage paths; if a connection between a source and destination cannot be conclusively mapped and documented by the crawler, the attempt is not billed.
**Business Function**: ProvideService
**Objection Handlers**:
- Objection: We need active database API connections to trust the data lineage. Rebuttal: Compass is designed to parse the actual codebase (SQL, Python, dbt) defining the transformations, capturing the true logic without requiring live production database credentials.
- Objection: Our code repositories are massive and contain unrelated applications. Rebuttal: The crawler isolates and evaluates only relevant data transformation scripts and configuration files, entirely ignoring standard application code.
- Objection: How do we know the mapped lineage is accurate before we are charged? Rebuttal: The pricing structure dictates that billing triggers only on outcomes that pass deterministic verification against your known schemas.
**Pricing Architecture**: UsageMeter
**Agent Checkout Support**:
- agentic-commerce-protocol

## Startup Brand

**Voice**: Technical and precise, prioritizing verified repository metrics over abstract promises.
**Tagline**: Map internal data lineage with zero integration overhead.
**Icon Concept**: compass
**Palette Intent**: institutional-cool
**Visual Identity**: Deep navy and slate gray establish an authoritative, structured foundation that pairs monospace typography with stark architectural diagrams of data repositories.
**Archetype Reference**: the-sage

## Startup Buyer Chain

**Chain**: B2B → Data Engineering Lead → Data Analytics Team
**Gtm Motion**: Acquires initial usage by allowing data engineers to run a zero-integration crawl on a single repository to diagnose an immediate pipeline break. Expands account value by pricing purely on verified lineage outcomes as governance teams request coverage across the broader enterprise data stack.
**Agent Channel**: Intended for listing in the LangChain tool registry and the Model Context Protocol (MCP) ecosystem, designed to let autonomous enterprise data agents query verified table lineage before executing downstream SQL.
**Primary Channel**: Bottom-up developer acquisition through tool drops in the dbt Slack community and r/dataengineering, targeting data engineers actively searching for spreadsheet-free lineage mapping.

## Startup Customer Journey

```mermaid
flowchart LR
A[Data Developer Community] --> B[Single Repository Crawler]
B --> C[Verified Lineage Graph]
C --> D[Analytics Team Repository]
D --> E[Enterprise Data Stack]
E --> F[Data Governance Council]
```

## 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 scoped scan of a single legacy data repository targeting the automated generation of a complete lineage graph for up to 1,000 tables to prove codebase parsing accuracy over manual tracing.
- 48-hour targeted pipeline discovery on a highly nested dbt project to conclusively verify 100 specific lineage paths, demonstrating the deterministic verification required for the usage-based billing.
**Target Metrics**:
- Target: Under 48-hour turnaround time to parse standard analytics repositories and deliver complete lineage graphs.
- Aim: Zero active database API connections or production database credentials required to map data transformations.
- Target: 100 percent deterministic verification of source-to-destination lineage paths against known schemas before triggering any billing.
**Target Case Studies**:
- Enterprise Healthcare Data Architect: Aiming to validate how parsing legacy SQL scripts directly from the codebase eliminates the need for manual spreadsheet documentation during a major cloud warehouse migration.
- Series C Fintech Head of Data: Targeting a scenario where deeply nested dbt transformation models are completely mapped from source to destination using only Git read access, without requiring live production database credentials.
- Mid-market E-commerce Lead Data Engineer: Illustrating a successful bulk scan of 1,000+ internal analytics repository links, proving that the crawler isolates relevant data transformation scripts and ignores unrelated application code.
**Testimonial Targets**:
- Lead Data Engineer: Expressing relief that they mapped deeply nested Python and SQL logic entirely through codebase parsing rather than requesting live database access from the security team.
- VP of Analytics: Highlighting the financial safety of the usage-metered model, praising the fact that billing only occurred for conclusively mapped and documented lineage paths.
- Data Governance Manager: Emphasizing the crawler's ability to cleanly isolate data transformation scripts from massive, mixed-use code repositories without capturing unrelated application code.

## Startup Top Risks

**Risks**:
- Severity: existential · Description: Enterprise security teams block the broad read access required to crawl internal data repositories. · Mitigation Status: unmitigated
- Severity: high · Description: Outcome-based pricing model leads to revenue disputes over the definition of a verified lineage outcome. · Mitigation Status: unmitigated
- Severity: high · Description: Zero-integration static analysis fails to trace complex or undocumented data transformations accurately. · Mitigation Status: in-progress
- Severity: moderate · Description: Incumbents like Collibra bundle automated lineage tracing into existing enterprise data governance contracts. · Mitigation Status: unmitigated

## Startup Story Brand

**Hero**:
- **Need**: to be the trusted steward of data truth during complex warehouse migrations
- **Want**: to map every internal data pipeline path with surgical precision
- **Identity**: the data architect at a high-growth enterprise analytics team
**Plan**:
- Step: Point repositories · Detail: Select the internal codebases containing your transformation logic and pipeline configurations.
- Step: Validate connections · Detail: Review the deterministic lineage paths our crawler identifies between your source files and destination tables.
- Step: Export documentation · Detail: Download verified lineage maps to secure your migration or satisfy your compliance audit requirements.
**Guide**:
- **Empathy**: Does your migration plan still stall because of hidden transformation dependencies?
**Problem**:
- **Villain**: Spreadsheet Documentation
- **External**: Mapping lineage across internal repositories currently requires weeks of manual copy-pasting from SQL and Python files into Excel or Alation.
- **Internal**: You feel like a forensic investigator instead of an architect, buried in code you didn't write.
- **Philosophical**: Every data architect deserves a verified source of truth — not a manual log that is outdated by Monday.
**Success**: Your entire warehouse migration stays on schedule with fully documented, verified lineage that requires zero manual data entry.
**One Liner**: Instead of chasing manual spreadsheet updates, Compass crawls your repositories to map every transformation path — delivering a verified lineage graph in 48 hours.
**Positioning**:
- **So That**: migrations complete without hidden dependency failures
- **Unlike**: manual spreadsheet tracking
- **For Whom**: enterprise data architects
- **Category**: Automated data lineage discovery
**Call To Action**:
- **Direct**: Verify a pipeline
- **Transitional**: Sample lineage graph
**Failure Stakes**:
- Broken downstream analytics tables
- Months of migration delays
- Inaccurate regulatory reporting
**Transformation**:
- **To**: shipping verified data migrations instead of documenting legacy debt
- **From**: an architect manually tracing SQL in dbt files
**Controlling Idea**: Data lineage must be discovered from code, not documented by hand.

## Startup Landing Hero

**Eyebrow**: Automated data lineage discovery
**Headline**: Ship migrations without manual documentation debt
**Supporting Proof**: Static code analysis for SQL and Python repositories

## Startup Landing Hero Services

**Eyebrow**: Automated data lineage discovery
**Headline**: Verified data lineage graphs in 48 hours
**Supporting Proof**: Parses SQL and Python repositories without database credentials.

## Startup Landing Hero Headless Saa S

**Eyebrow**: Headless data lineage API
**Headline**: Extract deterministic data lineage from source code
**Supporting Proof**: Parses SQL and Python without requiring database credentials

## Startup Landing Problem

**Cards**:
- Body: You spend your mornings opening dbt models and raw SQL files to manually copy column names into a master spreadsheet. By the time you finish mapping one pipeline, a developer pushes a change that renders your documentation obsolete. · Heading: Manual SQL Tracing in Excel
- Body: You schedule recurring syncs with senior developers to ask which Python scripts touch specific tables. This tribal knowledge never makes it into Alation or your migration plan, leaving you blind to downstream breaks during the next warehouse cutover. · Heading: Interviewing Engineers for Hidden Dependencies
- Body: You resort to global text searches across internal repositories to find where a specific table is mentioned. This provides a list of files but fails to show the actual logic flow, forcing you to guess the transformation order. · Heading: Searching GitHub for Upstream References
**Section Heading**: Your spreadsheets cannot keep pace with your code repositories

## Startup Landing Solution

**Section Heading**: Ship verified data migrations instead of documenting legacy debt
**Solution Statement**: Compass is an automated data lineage discovery tool designed to parse logic inside GitHub and GitLab repositories. It scans SQL and Python files to visualize dependencies across Snowflake and BigQuery environments, replacing manual spreadsheet mapping with code-based truth.

## Startup Landing Social Proof

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

**Section Heading**: Built to map data lineage directly from codebases
**Capability Claims**:
- Parses internal SQL and Python repositories to deliver lineage graphs in under 48 hours.
- Maps deeply nested transformation models without requiring live production database credentials.
- Isolates data transformation scripts from mixed-use code repositories while ignoring unrelated application code.
- Verifies source-to-destination lineage paths deterministically against known schemas before billing occurs.
**Foundation Signals**:
- Built for GitHub and GitLab repository integration
- Compatible with dbt transformation logic
- Uses OAuth for secure repository read access

## Startup Landing Pricing

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

**Tiers**:
- Name: Targeted Pipeline Discovery · Price: ~$15–$30 per verified connection · Tagline: For architects validating specific transformation paths within complex data environments · Cta Label: Verify a pipeline · Highlighted: false
- Name: Warehouse Batch Scan · Price: ~$5,000–$12,000 per 1,000 verified links · Tagline: For enterprise teams executing full-scale warehouse migrations or audits · Cta Label: Sample lineage graph · Highlighted: true
**Billing Note**: Usage-metered pricing — illustrative bands shown until this Startup is live and billing.
**Section Heading**: Automate lineage mapping for your next migration

## Startup Landing Faq

**Faqs**:
- Answer: You only pay for deterministic results. Our usage-based billing triggers exclusively when a connection passes verification against your defined schemas, meaning you are never billed for ambiguous or incomplete paths. · Question: How do I know the mapped lineage is accurate before I am charged?
- Answer: No database access is required. The system parses your transformation logic directly from SQL, Python, and dbt files in your repositories to map lineage from the code itself, maintaining your production security perimeter. · Question: Do I need to provide live database API credentials for this to work?
- Answer: The crawler ignores standard application logic. It specifically isolates and evaluates only the data transformation scripts, configuration files, and pipeline definitions relevant to your data warehouse architecture. · Question: What if our repositories are massive and full of unrelated application code?
- Answer: You receive a documented lineage graph in under 48 hours. Once you point the crawler at your internal repositories, it automates the forensic work that typically takes data architects weeks of manual copy-pasting. · Question: How long does it take to get a complete lineage map?
- Answer: Yes, the system is built for enterprise complexity. It traces logic through nested models and cross-repository dependencies to surface the actual path from source file to destination table without manual intervention. · Question: Can this handle deeply nested transformations or complex dbt models?
**Section Heading**: Common questions about lineage discovery

## Startup Landing Final Cta

**Subhead**: Stop stalling your migration on hidden dependencies and manual spreadsheet updates that are outdated before you finish them. Check your paths now.  Wait any longer and you risk broken analytics tables and months of migration delays. Use Compass to map every transformation path from your code.  Get started today.

## Startup Token Hero

**Genre**: founding-hypothesis
**Rendered**: Instead of chasing manual spreadsheet updates, Compass crawls your repositories to map every transformation path — delivering a verified lineage graph in 48 hours.
**Mechanism**: spine-derived-v1
**Template Id**: spine-founding-hypothesis
**Vocab Fingerprint**: 8016fd7cb659214a

## Startup Token Positioning

**Genre**: moore-positioning
**Rendered**: Automated data lineage discovery for enterprise data architects. Unlike manual spreadsheet tracking — migrations complete without hidden dependency failures.
**Mechanism**: spine-derived-v1
**Template Id**: spine-moore-positioning
**Vocab Fingerprint**: 5e9d9ee25fc9651b

## Startup Token Pitch Deck

**Genre**: pitch-deck
**Rendered**: Problem: Mapping lineage across internal repositories currently requires weeks of manual copy-pasting from SQL and Python files into Excel or Alation.
Solution: Instead of chasing manual spreadsheet updates, Compass crawls your repositories to map every transformation path — delivering a verified lineage graph in 48 hours.
Customer: enterprise data architects
Unlike: manual spreadsheet tracking
**Mechanism**: spine-derived-v1
**Template Id**: spine-pitch-deck
**Vocab Fingerprint**: ec9a0a6acc193940

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

**Pain**: Mapping lineage across internal repositories currently requires weeks of manual copy-pasting from SQL and Python files into Excel or Alation.
**Metrics**: Target: Your entire warehouse migration stays on schedule with fully documented, verified lineage that requires zero manual data entry.
**Rendered**: Pain: Mapping lineage across internal repositories currently requires weeks of manual copy-pasting from SQL and Python files into Excel or Alation.
Economic buyer: Data Engineering Lead
Metrics: Target: Your entire warehouse migration stays on schedule with fully documented, verified lineage that requires zero manual data entry.
Competition: manual spreadsheet tracking
**Mechanism**: spine-derived-v1
**Competition**: manual spreadsheet tracking
**Economic Buyer**: Data Engineering Lead
**Vocab Fingerprint**: 1edb9563901a7036

## Startup Token Cold Email

**Genre**: cold-email
**Rendered**: Subject: Automated data lineage discovery for enterprise data architects

enterprise data architects — Mapping lineage across internal repositories currently requires weeks of manual copy-pasting from SQL and Python files into Excel or Alation. Instead of chasing manual spreadsheet updates, Compass crawls your repositories to map every transformation path — delivering a verified lineage graph in 48 hours.
**Mechanism**: spine-derived-v1
**Template Id**: spine-cold-email
**Vocab Fingerprint**: fb926e0dde968e7c

## Startup Token Agent Spec

**Genre**: ai-agent-spec
**Rendered**: Automated data lineage discovery. Instead of chasing manual spreadsheet updates, Compass crawls your repositories to map every transformation path — delivering a verified lineage graph in 48 hours. Serves enterprise data architects.
**Mechanism**: spine-derived-v1
**Template Id**: spine-ai-agent-spec
**Vocab Fingerprint**: 37f0d7e8bba11acd

## Neighborhood

### Candidate solutions

- [arguing detention fees with carriers who have better paperwork than you](/Problems/arguing_detention_fees_with_carriers_who_have_better_paperwork_than_you) — candidate solution for · Problems
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- [Missing Client Document Chasing](/Problems/Missing_Client_Document_Chasing) — candidate solution for · Problems
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### Competitors

- [Alation](/Competitors/Alation) — competes with · Competitors
- [Collibra](/Competitors/Collibra) — competes with · Competitors
- [manual spreadsheet tracking](/Competitors/manual_spreadsheet_tracking) — competes with · Competitors
- [Tempo Timesheets](/Competitors/Tempo_Timesheets) — competes with · Competitors
- [Jellyfish](/Competitors/Jellyfish) — competes with · Competitors
- [retroactive manager interviews](/Competitors/retroactive_manager_interviews) — competes with · Competitors
- [Slack Polling Workarounds](/Competitors/Slack_Polling_Workarounds) — competes with · Competitors
- [HashiCorp Terraform](/Competitors/HashiCorp_Terraform) — competes with · Competitors
- [AWS Cost Explorer](/Competitors/AWS_Cost_Explorer) — competes with · Competitors
- [Mandatory Tagging Policies](/Competitors/Mandatory_Tagging_Policies) — competes with · Competitors
- [Manual Video Review](/Startups/Manual_Video_Review) — competes with · Startups
- [Axon Evidence](/Startups/Axon_Evidence) — competes with · Startups
- [PROSECUTORbyKarpel](/Startups/PROSECUTORbyKarpel) — competes with · Startups
- [Tyler Technologies Odyssey](/Startups/Tyler_Technologies_Odyssey) — competes with · Startups

### What it offers

- [Data Lineage Tracer](/Services/Data_Lineage_Tracer) — offers · Services
- [Compass Sandbox Engine](/Software/Compass_Sandbox_Engine) — offers · Software
- [Commit Ledger Agent](/Agents/Commit_Ledger_Agent) — offers · Agents
- [Compass Discovery Engine](/Agents/Compass_Discovery_Engine) — offers · Agents

### Embodies

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

### What it addresses

- [Software Capitalization Audits](/Problems/Software_Capitalization_Audits) — addresses · Problems
- [Brady Discovery Compliance](/Problems/Brady_Discovery_Compliance) — addresses · Problems

### Composed of

- [Brady Compliance Service](/Agents/Brady_Compliance_Service) — composes · Agents
- [Multimodal Ingestion API](/Agents/Multimodal_Ingestion_API) — composes · Agents
- [Exculpatory Inference Engine](/Agents/Exculpatory_Inference_Engine) — composes · Agents
- [Database Sync Agent](/Agents/Database_Sync_Agent) — composes · Agents
- [Evidence Reconciliation Agent](/Agents/Evidence_Reconciliation_Agent) — composes · Agents

### Entrant in opportunity

- [AI Brady Discovery for Prosecutors](/Opportunities/AI_Brady_Discovery_for_Prosecutors) — is entrant in · Opportunities

### Who it serves

- [District Attorney Office](/CompanyTypes/District_Attorney_Office) — serves · CompanyTypes

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