# Crunchumen

*/Startups/Crunchumen*

## Startup Overview

This extraction engine ingests raw corporate financial disclosures and outputs structured, normalized data entities. Investment analysts use it to bypass the grueling process of manually transferring figures from 10-Ks, 10-Qs, and earnings transcripts into custom spreadsheets. The software identifies, standardizes, and links line items and footnote metrics across disparate reporting formats.

Unlike legacy aggregators like S&P Capital IQ or outsourced data operations like Daloopa, the parsing logic is fully deterministic. Every extracted figure maps directly back to its source document without probabilistic guessing, guaranteeing absolute mathematical consistency for equity models. Furthermore, instead of locking research teams into monolithic terminal subscriptions, the system operates on a strict per-document pricing model, allowing funds to pay exactly for the filings they process.

## Startup Founding Hypothesis

**Approach**: that extracts and normalizes entities from raw financial disclosures
**Competitors**:
- [Manual Analyst Spreadsheets](/Competitors/Manual_Analyst_Spreadsheets)
- [S&P Capital IQ](/Competitors/S&P_Capital_IQ)
- [Daloopa](/Competitors/Daloopa)
**Differentiator2x2**: fully deterministic in extraction and priced strictly per processed document

## Startup Solution Coordinate

**Solution**: [Financial Entity Extractor](/Software/Financial_Entity_Extractor)

## Startup Position2x2

```mermaid
quadrantChart
title Financial Data Extraction Position
x-axis Broad Subscription --> Pay-per-Document
y-axis Human/Heuristic --> Fully Deterministic
quadrant-1 Scalable Data API
quadrant-2 Enterprise SaaS
quadrant-3 Legacy Platforms
quadrant-4 Variable Labor
Manual Analyst Spreadsheets: [0.85, 0.15]
S&P Capital IQ: [0.15, 0.25]
Daloopa: [0.25, 0.75]
Crunchumen: [0.85, 0.95]
```

## Startup Offer

**Proof**:
- Targeting zero-hallucination extraction for fundamental equity analysts.
- Aiming to convert raw 10-K tables into normalized database rows in under five minutes.
- Designed to achieve 100% auditable linkage between internal models and source SEC filings.
**Tiers**:
- Name: Ad-Hoc Processing · Price: ~$15–$30 per document · Inclusions: On-demand entity and table extraction from individual unstructured filings, delivering traceable data arrays.
- Name: Batch Watchlist · Price: ~$8–$14 per document · Inclusions: Automated ingestion of bulk filings against a custom ticker watchlist, designed to route normalized outputs directly to internal data stores.
**Guarantee**: If any extracted value cannot be deterministically traced back to its exact coordinate in the source document, the processing fee for that document is refunded.
**Business Function**: ProvideService
**Objection Handlers**:
- LLMs hallucinate financial data. -> Crunchumen applies a fully deterministic extraction model; it outputs only what exists in the document, bound to explicit coordinate citations.
- We already pay for Capital IQ. -> Capital IQ forces you into their rigid data models and charges fixed seat fees; we populate your bespoke templates strictly on a pay-per-document basis.
- We have non-standard, messy table formats. -> The system is designed to anchor on raw tabular structures rather than relying on summarization, keeping exact reported values intact regardless of layout.
**Pricing Architecture**: UsageMeter
**Agent Checkout Support**:
- agentic-commerce-protocol

## Startup Brand

**Voice**: Clinical and precise, prioritizing factual density over conversational filler.
**Tagline**: Exact financial entities extracted deterministically from raw corporate disclosures.
**Icon Concept**: ledger
**Palette Intent**: institutional-cool
**Visual Identity**: Deep navy and stark white form a high-contrast palette paired with monospaced typography evocative of raw SEC filings.
**Archetype Reference**: the-sage

## Startup Buyer Chain

**Chain**: Crunchumen → Financial Data Engineers → Quantitative Analysts
**Gtm Motion**: Acquisition is driven by a self-serve developer sandbox where data teams process a limited set of historical SEC filings to verify extraction accuracy against their internal benchmarks. Expansion occurs automatically via API usage, scaling strictly per processed document as hedge funds and asset managers plug the endpoints into their live daily ingestion pipelines.
**Agent Channel**: Designed to be listed as a structured tool in the LangChain integrations hub and as an OpenAPI schema in the OpenAI GPT Action directory, allowing autonomous financial research agents to query and retrieve normalized financial entities from raw disclosures on demand.
**Primary Channel**: Developer-focused search intent for technical parsing challenges (e.g., 'deterministic SEC 10-K extraction API', 'XBRL to JSON parser') and intended provider listings on quantitative data marketplaces like AWS Data Exchange and the Snowflake Data Cloud.

## Startup Customer Journey

```mermaid
flowchart LR;A[AWS Data Exchange]-->B[Developer Sandbox];B-->C[Extracted 10-K JSON];C-->D[Production API];D-->E[Bulk Watchlist];E-->F[LangChain Integration Hub];
```

## 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 historical backtest processing 500 unstructured SEC filings to prove 100% accurate ingestion into the firm's internal models with zero hallucinated data points.
- 30-day live watchlist pilot tracking 50 specific tickers to validate routing normalized extraction outputs to internal data stores within five minutes of document publication.
**Target Metrics**:
- Target: Under 5-minute processing time from raw 10-K table to normalized database row.
- Target: 100% deterministic traceability rate linking extracted values to exact source document coordinates.
- Target: Zero hallucinated data points across bulk batch watchlist processing.
**Target Case Studies**:
- Mid-market hedge fund fundamental equity analyst: Validates the transition from manual 10-K data entry to automated, coordinate-linked database ingestion.
- Boutique asset manager data engineering team: Demonstrates the replacement of fixed-seat legacy data subscriptions with a pay-per-document pipeline populating bespoke financial templates.
**Testimonial Targets**:
- Lead Equity Analyst expressing absolute confidence in the data because every extracted cell provides a strict coordinate citation back to the original source filing.
- Head of Financial Data highlighting the relief of escaping rigid vendor models and paying only for the specific document processing they actually consume.

## Startup Top Risks

**Risks**:
- Severity: existential · Description: Deterministic extraction logic fails to parse the highly irregular footnote formats found in non-standard financial filings without falling back to manual intervention. · Mitigation Status: in-progress
- Severity: high · Description: Incumbent competitors like S&P Capital IQ aggressively bundle extraction tools into existing enterprise site licenses, neutralizing the per-document pricing advantage. · Mitigation Status: unmitigated
- Severity: high · Description: Primary financial disclosure repositories implement aggressive anti-scraping measures that block the automated ingestion of raw filing documents. · Mitigation Status: in-progress
- Severity: moderate · Description: Target customers at hedge funds and asset managers reject unpredictable usage-based billing in favor of familiar flat-fee subscription models. · Mitigation Status: unmitigated

## Startup Competitors

- [Manual Analyst Spreadsheets](/Competitors/Manual_Analyst_Spreadsheets) — Status Quo
- [S&P Capital IQ](/Competitors/S&P_Capital_IQ) — Incumbent
- [Daloopa](/Competitors/Daloopa) — Direct Competitor
- [FactSet](/Competitors/FactSet) — Incumbent
- [Sentieo](/Competitors/Sentieo) — Direct Competitor

## Startup Solution Stack

- [Financial Disclosure Service](/Services/Financial_Disclosure_Service) — Service-as-Software
- [Table Extraction Worker](/Agents/Table_Extraction_Worker) — Agent
- [Entity Normalization Agent](/Agents/Entity_Normalization_Agent) — Agent
- [Deterministic Parsing Engine](/Software/Deterministic_Parsing_Engine) — Software
- [Document Ingestion API](/Software/Document_Ingestion_API) — Software

## Startup Story Brand

**Hero**:
- **Need**: to be the rigorous decision-maker, not the clerk auditing manual spreadsheet entries
- **Want**: to populate internal valuation models directly from raw SEC filings instantly
- **Identity**: the fundamental analyst at a long-short equity hedge fund
**Plan**:
- Step: Upload Disclosure · Detail: Drop any unstructured PDF or SEC filing into the processing queue.
- Step: Inspect Mappings · Detail: Verify that every extracted entity links directly to its source coordinate for absolute auditability.
- Step: Route Data · Detail: Send normalized tables straight to your internal data stores or proprietary Excel models.
**Guide**:
- **Empathy**: When a 10-K drops at 4:00 PM, you face an unnecessary choice between speed and accuracy.
**Problem**:
- **Villain**: manual data entry
- **External**: Analysts spend hours copying 10-K tables into Excel because S&P Capital IQ lacks the specific granularity of their internal templates.
- **Internal**: You feel a constant dread that one misplaced decimal point in a model will ruin a high-conviction trade.
- **Philosophical**: Financial disclosures were built for transparency, not misuse as a manual labor sink.
**Success**: Your internal models update in under five minutes with perfect coordinate-level traceability for every number.
**One Liner**: Instead of manual analyst spreadsheets, Crunchumen extracts deterministic entity data from raw filings — delivering 100% auditable models in minutes.
**Positioning**:
- **So That**: populate bespoke models with zero-hallucination auditable data
- **Unlike**: Manual Analyst Spreadsheets
- **For Whom**: fundamental analysts at equity funds
- **Category**: Deterministic financial entity extraction
**Call To Action**:
- **Direct**: Process raw filing
- **Transitional**: View sample audit trail
**Failure Stakes**:
- Fat-finger errors in models
- Missing critical footnotes
- Lagging behind the market news
**Transformation**:
- **To**: the analyst who executes high-conviction trades instantly
- **From**: the analyst buried in manual spreadsheet workarounds
**Controlling Idea**: Financial extraction must be deterministic and auditable to be useful for fundamental analysis.

## Startup Token Hero

**Genre**: founding-hypothesis
**Rendered**: Instead of manual analyst spreadsheets, Crunchumen extracts deterministic entity data from raw filings — delivering 100% auditable models in minutes.
**Mechanism**: spine-derived-v1
**Template Id**: spine-founding-hypothesis
**Vocab Fingerprint**: 03bfbb932a5047df

## Startup Token Positioning

**Genre**: moore-positioning
**Rendered**: Deterministic financial entity extraction for fundamental analysts at equity funds. Unlike Manual Analyst Spreadsheets — populate bespoke models with zero-hallucination auditable data.
**Mechanism**: spine-derived-v1
**Template Id**: spine-moore-positioning
**Vocab Fingerprint**: 7eb95e53057bf29b

## Startup Token Pitch Deck

**Genre**: pitch-deck
**Rendered**: Problem: Analysts spend hours copying 10-K tables into Excel because S&P Capital IQ lacks the specific granularity of their internal templates.
Solution: Instead of manual analyst spreadsheets, Crunchumen extracts deterministic entity data from raw filings — delivering 100% auditable models in minutes.
Customer: fundamental analysts at equity funds
Unlike: Manual Analyst Spreadsheets
**Mechanism**: spine-derived-v1
**Template Id**: spine-pitch-deck
**Vocab Fingerprint**: 49217bc221d95822

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

**Pain**: Analysts spend hours copying 10-K tables into Excel because S&P Capital IQ lacks the specific granularity of their internal templates.
**Metrics**: Target: Your internal models update in under five minutes with perfect coordinate-level traceability for every number.
**Rendered**: Pain: Analysts spend hours copying 10-K tables into Excel because S&P Capital IQ lacks the specific granularity of their internal templates.
Economic buyer: Financial Data Engineers
Metrics: Target: Your internal models update in under five minutes with perfect coordinate-level traceability for every number.
Competition: Manual Analyst Spreadsheets
**Mechanism**: spine-derived-v1
**Competition**: Manual Analyst Spreadsheets
**Economic Buyer**: Financial Data Engineers
**Vocab Fingerprint**: 5eeceea1be589ffd

## Startup Token Cold Email

**Genre**: cold-email
**Rendered**: Subject: Deterministic financial entity extraction for fundamental analysts at equity funds

fundamental analysts at equity funds — Analysts spend hours copying 10-K tables into Excel because S&P Capital IQ lacks the specific granularity of their internal templates. Instead of manual analyst spreadsheets, Crunchumen extracts deterministic entity data from raw filings — delivering 100% auditable models in minutes.
**Mechanism**: spine-derived-v1
**Template Id**: spine-cold-email
**Vocab Fingerprint**: 8cae42ea86223a73

## Startup Token Agent Spec

**Genre**: ai-agent-spec
**Rendered**: Deterministic financial entity extraction. Instead of manual analyst spreadsheets, Crunchumen extracts deterministic entity data from raw filings — delivering 100% auditable models in minutes. Serves fundamental analysts at equity funds.
**Mechanism**: spine-derived-v1
**Template Id**: spine-ai-agent-spec
**Vocab Fingerprint**: b8b2cde06515cbbb

## Neighborhood

### Candidate solutions

- [Tax Season Capacity Bottlenecks](/Problems/Tax_Season_Capacity_Bottlenecks) — candidate solution for · Problems

### What it offers

- [Financial Entity Extractor](/Software/Financial_Entity_Extractor) — offers · Software
- [Crunchumen Document Router](/Services/Crunchumen_Document_Router) — offers · Services
- [Return Assembly Desk](/Agents/Return_Assembly_Desk) — offers · Agents

### Composed of

- [Complexity Scoring Engine](/Software/Complexity_Scoring_Engine) — composes · Software
- [Unstructured Data Mapping Worker](/Agents/Unstructured_Data_Mapping_Worker) — composes · Agents
- [Document Triage Agent](/Agents/Document_Triage_Agent) — composes · Agents
- [Multimodal Parsing API](/Software/Multimodal_Parsing_API) — composes · Software
- [Draft Return Assembly Service](/Services/Draft_Return_Assembly_Service) — composes · Services
- [Table Extraction Worker](/Agents/Table_Extraction_Worker) — composes · Agents
- [Financial Disclosure Service](/Services/Financial_Disclosure_Service) — composes · Services
- [Document Ingestion API](/Software/Document_Ingestion_API) — composes · Software
- [Deterministic Parsing Engine](/Software/Deterministic_Parsing_Engine) — composes · Software
- [Entity Normalization Agent](/Agents/Entity_Normalization_Agent) — composes · Agents

### Competitors

- [Offshore Seasonal Contractors](/Competitors/Offshore_Seasonal_Contractors) — competes with · Competitors
- [CCH Axcess Practice](/Competitors/CCH_Axcess_Practice) — competes with · Competitors
- [Xero Practice Manager](/Competitors/Xero_Practice_Manager) — competes with · Competitors
- [Thomson Reuters Practice CS](/Competitors/Thomson_Reuters_Practice_CS) — competes with · Competitors
- [Seasonal Offshore Contractors](/Competitors/Seasonal_Offshore_Contractors) — competes with · Competitors
- [Offshore Contractors](/Competitors/Offshore_Contractors) — competes with · Competitors
- [Offshore Temporary Contractors](/Competitors/Offshore_Temporary_Contractors) — competes with · Competitors
- [Mandatory Overtime](/Competitors/Mandatory_Overtime) — competes with · Competitors
- [Offshore Temporary Labor](/Competitors/Offshore_Temporary_Labor) — competes with · Competitors
- [Offshore Staffing Agencies](/Competitors/Offshore_Staffing_Agencies) — competes with · Competitors
- [Offshore Tax Contractors](/Competitors/Offshore_Tax_Contractors) — competes with · Competitors
- [Master Spreadsheets](/Competitors/Master_Spreadsheets) — competes with · Competitors
- [Canopy Practice Management](/Competitors/Canopy_Practice_Management) — competes with · Competitors
- [Manual Master Spreadsheets](/Competitors/Manual_Master_Spreadsheets) — competes with · Competitors
- [Thomson Reuters Practice](/Competitors/Thomson_Reuters_Practice) — competes with · Competitors
- [Excel Master Spreadsheets](/Competitors/Excel_Master_Spreadsheets) — competes with · Competitors
- [manual spreadsheet schedules](/Competitors/manual_spreadsheet_schedules) — competes with · Competitors
- [Master Excel Spreadsheets](/Competitors/Master_Excel_Spreadsheets) — competes with · Competitors
- [Offshore Temp Labor](/Competitors/Offshore_Temp_Labor) — competes with · Competitors
- [S&P Capital IQ](/Competitors/S&P_Capital_IQ) — competes with · Competitors
- [Manual Analyst Spreadsheets](/Competitors/Manual_Analyst_Spreadsheets) — competes with · Competitors
- [Sentieo](/Competitors/Sentieo) — competes with · Competitors
- [FactSet](/Competitors/FactSet) — competes with · Competitors
- [Daloopa](/Competitors/Daloopa) — competes with · Competitors

### Who it serves

- [Accounting Firm](/CompanyTypes/Accounting_Firm) — serves · CompanyTypes

### Embodies

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

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