# Supasis

*/Startups/Supasis*

## Startup Overview

This platform maps unstructured digital workflows directly into strict relational schemas. It ingests fragmented communications, documents, and system interactions, converting them into queryable, structured data in real time. The engine guarantees that complex operational steps are instantly stored as exact, verifiable database records.

Operations teams and data engineers currently rely on manual data entry or brittle automation loops to extract reliable data from messy digital processes. These legacy methods break when software interfaces update or workflows deviate slightly, corrupting downstream analytics and requiring constant maintenance.

Unlike robotic process automation tools like UiPath that rely on fragile UI-level scripting, or human-in-the-loop services like Scale AI that introduce latency, this architecture is API-native and strictly deterministic. By connecting directly to software endpoints and enforcing rigid schema mapping, it bypasses the need for screen scraping entirely. Teams receive structured, accurate data flows that scale predictably and remain immune to the interface changes that derail traditional automation.

## Startup Founding Hypothesis

**Approach**: that maps unstructured digital workflows into strict relational schemas
**Competitors**:
- [UiPath](/Competitors/UiPath)
- [Scale AI](/Competitors/Scale_AI)
- [Manual data entry](/Competitors/Manual_data_entry)
**Differentiator2x2**: API-native and strictly deterministic, eliminating fragile UI-level scripting

## Startup Solution Coordinate

**Solution**: [Workflow Schema Engine](/Software/Workflow_Schema_Engine)

## Startup Position2x2

```mermaid
quadrantChart
 x-axis UI Scripting or Manual --> API-Native
 y-axis Fragile or Probabilistic --> Strictly Deterministic
 Manual data entry: [0.1, 0.2]
 UiPath: [0.2, 0.4]
 Scale AI: [0.8, 0.3]
 Supasis: [0.9, 0.9]
```

## Startup Offer

**Proof**:
- Targeting logistics operations teams to eliminate 95% of manual unstructured invoice-to-database entry.
- Aiming to reduce integration maintenance by 80% for fintechs handling varied customer compliance documents.
- Designed to achieve zero schema-validation errors in automated healthcare record parsing.
**Tiers**:
- Name: Standard Metered · Price: ~$0.08–$0.15 per mapping event · Inclusions: Real-time unstructured data extraction mapped to standard relational schemas, capped at 100,000 events per month, with email support.
- Name: Production Tier · Price: ~$1,200–$2,500/mo · Inclusions: Up to 500,000 events per month, support for custom relational schema enforcement, designated webhooks, and anomaly detection.
- Name: Enterprise Deployment · Price: ~$15,000–$25,000/yr · Inclusions: Dedicated VPC deployment, unlimited mapping volume, custom deterministic pipeline engineering, and 24/7 SLA coverage.
**Guarantee**: If a mapped event violates your strict relational schema parameters, you are not charged for that API call and we will provide a deterministic trace of the failure within 24 hours.
**Business Function**: ProvideService
**Objection Handlers**:
- Unstructured data always breaks strict schemas eventually. -> We rely on deterministic mapping logic combined with fallback anomaly detection, rejecting invalid payloads before they hit your database.
- How is this different from existing RPA tools like UiPath? -> Supasis is completely API-native, eliminating the fragile UI-level scripting and screen-scraping that break when layouts change.
- What if an upstream vendor radically changes their document format? -> The system detects structural drift automatically, quarantining the workflow for quick human review instead of silently corrupting your schema.
**Pricing Architecture**: UsageMeter
**Agent Checkout Support**:
- agentic-commerce-protocol
- stored-credential

## Startup Brand

**Voice**: Clinical engineering register marked by uncompromising deterministic precision.
**Tagline**: Turns unstructured digital workflows into strict relational schemas.
**Icon Concept**: ledger
**Palette Intent**: electric-signal
**Visual Identity**: A high-contrast interface pairing deep terminal black with sharp neon green accents to emphasize deterministic precision and API-native execution.
**Archetype Reference**: the-ruler

## Startup Buyer Chain

**Chain**: Supasis -> Automation Developer -> Autonomous Agent -> Enterprise Database
**Gtm Motion**: Automation developers adopt the API via self-serve sandbox tiers to replace brittle UI scraping workflows with deterministic schema endpoints. Accounts expand through usage-based billing as engineering teams route higher volumes of unstructured document processing through the system instead of scaling manual data-entry teams.
**Agent Channel**: Designed to list in the LangChain Tool Hub and OpenAI schema registries, allowing autonomous workflow agents to discover and invoke the deterministic parsing endpoints during unstructured data tasks.
**Primary Channel**: Technical documentation and orchestration communities like Apache Airflow forums, where data engineers search for programmatic API-native alternatives to legacy RPA platforms.

## Startup Customer Journey

```mermaid
flowchart LR; A[Developer Community] --> B[API Sandbox]; B --> C[Validation Endpoint]; C --> D[Autonomous Agent]; D --> E[Data Pipeline]; E --> F[Enterprise Database]; F --> G[Registry Hub];
```

## Startup Proof Points

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

**Pilot Goals**:
- Aiming for a 30-day logistics pilot mapping 10,000 unstructured freight invoices, validating that the system correctly rejects invalid payloads before hitting the database.
- Aiming for a 60-day fintech compliance pilot testing 50,000 document extractions, proving the automatic quarantine of structural drift while maintaining zero relational schema violations.
**Target Metrics**:
- Target: 95% reduction in manual unstructured invoice-to-database entry hours.
- Target: 80% decrease in integration maintenance time for varied document workflows.
- Target: 0 schema-validation errors in automated database record parsing.
- Target: 100% detection rate of structural drift triggering automatic quarantine workflows.
**Target Case Studies**:
- Target: A mid-sized logistics operations team processing varied freight invoices. Transformation: Eliminating 95% of manual unstructured invoice-to-database entry by routing documents through deterministic API endpoints.
- Target: A Series B fintech handling diverse compliance documents. Transformation: Reducing integration maintenance by 80% by replacing fragile RPA screen-scraping with API-native mapping.
- Target: A regional healthcare provider parsing patient intake forms. Transformation: Achieving zero schema-validation errors when standardizing unstructured clinical text into a strict relational database.
**Testimonial Targets**:
- Target: Logistics Operations Director confirming that API-native mapping ended their reliance on fragile UI-level scripting and prevented database corruption.
- Target: Fintech Lead Data Engineer stating that automatic structural drift detection quarantines bad vendor formats before they break downstream compliance schemas.
- Target: Healthcare IT Manager praising the strict schema enforcement guarantee and the receipt of deterministic traces within 24 hours for any failed mappings.

## Startup Top Risks

**Risks**:
- Severity: existential · Description: Target enterprise systems lack sufficient or stable APIs to capture the entire workflow, forcing fallbacks to the exact UI scraping the product avoids. · Mitigation Status: unmitigated
- Severity: high · Description: The deterministic mapping engine fails to parse highly variable unstructured data, requiring manual human intervention that destroys target unit economics. · Mitigation Status: in-progress
- Severity: high · Description: Upstream API providers alter their rate limits or schema payloads without notice, instantly breaking the strict relational mapping downstream. · Mitigation Status: in-progress
- Severity: moderate · Description: Customers struggle to define their target strict relational schemas accurately, causing severe onboarding delays and abandonment during initial implementation. · Mitigation Status: in-progress

## Startup Competitors

- [UiPath](/Competitors/UiPath) — RPA Incumbent
- [Scale AI](/Competitors/Scale_AI) — Human-In-The-Loop
- [Manual Data Entry](/Competitors/Manual_Data_Entry) — Status Quo
- [Automation Anywhere](/Competitors/Automation_Anywhere) — Legacy RPA
- [Snorkel AI](/Competitors/Snorkel_AI) — Programmatic Labeling

## Startup Solution Stack

- [Workflow Mapping Service](/Services/Workflow_Mapping_Service) — Service-as-Software
- [Schema Extraction Agent](/Agents/Schema_Extraction_Agent) — Agent
- [Relational Validation Worker](/Agents/Relational_Validation_Worker) — Agent
- [Deterministic Integration API](/Software/Deterministic_Integration_API) — Software
- [Schema Engine SDK](/Software/Schema_Engine_SDK) — Software

## Startup Story Brand

**Hero**:
- **Need**: to be the architect of a stable digital system, not a troubleshooter
- **Want**: to convert messy unstructured invoices into clean database-ready relational data
- **Identity**: the logistics operations lead at a growing freight-forwarding firm
**Plan**:
- Step: Define · Detail: Provide your target relational schema and specify the validation rules for your production database.
- Step: Inspect · Detail: Review the deterministic mapping traces to ensure every unstructured field aligns with your strict data types.
- Step: Streamline · Detail: Deploy the API endpoint to handle real-time mapping of high-volume event data without manual intervention.
**Guide**:
- **Empathy**: When a vendor changes an invoice layout without warning, your entire automation stack collapses and forces your team back into manual data entry.
**Problem**:
- **Villain**: fragile UI scripting
- **External**: Manually re-keying shipping manifests into internal databases or managing broken UiPath scripts costs hours of engineering rework weekly.
- **Internal**: You feel like you are building on quicksand because every minor vendor layout change breaks your entire data pipeline.
- **Philosophical**: Every operations team deserves data integrity by design — not by brittle workarounds.
**Success**: Your data pipelines run on autopilot with zero schema-validation errors, even as upstream document formats shift.
**One Liner**: Fragile UI-level scripting costs logistics teams thousands in engineering rework. Supasis provides API-native mapping so unstructured workflows become strict relational data.
**Positioning**:
- **So That**: unstructured workflows map to strict relational schemas reliably
- **Unlike**: UiPath and manual data entry
- **For Whom**: logistics operations and fintech teams
- **Category**: API-native data extraction service
**Call To Action**:
- **Direct**: Submit a schema
- **Transitional**: View deterministic trace sample
**Failure Stakes**:
- Silent database corruption
- Constant automation maintenance overhead
- Slower freight processing times
**Transformation**:
- **To**: free to scale operational throughput, no longer stuck fixing fragile RPA workflows
- **From**: a script-mender buried in UI-level breakages
**Controlling Idea**: Data extraction must be deterministic and schema-enforced to be reliable.

## Startup Token Hero

**Genre**: founding-hypothesis
**Rendered**: Fragile UI-level scripting costs logistics teams thousands in engineering rework. Supasis provides API-native mapping so unstructured workflows become strict relational data.
**Mechanism**: spine-derived-v1
**Template Id**: spine-founding-hypothesis
**Vocab Fingerprint**: 20275e72c54d29b3

## Startup Token Positioning

**Genre**: moore-positioning
**Rendered**: API-native data extraction service for logistics operations and fintech teams. Unlike UiPath and manual data entry — unstructured workflows map to strict relational schemas reliably.
**Mechanism**: spine-derived-v1
**Template Id**: spine-moore-positioning
**Vocab Fingerprint**: 2f68132940626307

## Startup Token Pitch Deck

**Genre**: pitch-deck
**Rendered**: Problem: Manually re-keying shipping manifests into internal databases or managing broken UiPath scripts costs hours of engineering rework weekly.
Solution: Fragile UI-level scripting costs logistics teams thousands in engineering rework. Supasis provides API-native mapping so unstructured workflows become strict relational data.
Customer: logistics operations and fintech teams
Unlike: UiPath and manual data entry
**Mechanism**: spine-derived-v1
**Template Id**: spine-pitch-deck
**Vocab Fingerprint**: b3c7b6206cdf7ad7

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

**Pain**: Manually re-keying shipping manifests into internal databases or managing broken UiPath scripts costs hours of engineering rework weekly.
**Metrics**: Target: Your data pipelines run on autopilot with zero schema-validation errors, even as upstream document formats shift.
**Rendered**: Pain: Manually re-keying shipping manifests into internal databases or managing broken UiPath scripts costs hours of engineering rework weekly.
Economic buyer: Automation Developer
Metrics: Target: Your data pipelines run on autopilot with zero schema-validation errors, even as upstream document formats shift.
Competition: UiPath and manual data entry
**Mechanism**: spine-derived-v1
**Competition**: UiPath and manual data entry
**Economic Buyer**: Automation Developer
**Vocab Fingerprint**: fef1d557d594cf8c

## Startup Token Cold Email

**Genre**: cold-email
**Rendered**: Subject: API-native data extraction service for logistics operations and fintech teams

logistics operations and fintech teams — Manually re-keying shipping manifests into internal databases or managing broken UiPath scripts costs hours of engineering rework weekly. Fragile UI-level scripting costs logistics teams thousands in engineering rework. Supasis provides API-native mapping so unstructured workflows become strict relational data.
**Mechanism**: spine-derived-v1
**Template Id**: spine-cold-email
**Vocab Fingerprint**: aac5e097a1901ce5

## Startup Token Agent Spec

**Genre**: ai-agent-spec
**Rendered**: API-native data extraction service. Fragile UI-level scripting costs logistics teams thousands in engineering rework. Supasis provides API-native mapping so unstructured workflows become strict relational data. Serves logistics operations and fintech teams.
**Mechanism**: spine-derived-v1
**Template Id**: spine-ai-agent-spec
**Vocab Fingerprint**: 7401774eeed8e47b

## Neighborhood

### Candidate solutions

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

### What it offers

- [Workflow Schema Engine](/Software/Workflow_Schema_Engine) — offers · Software

### Composed of

- [Schema Extraction Agent](/Agents/Schema_Extraction_Agent) — composes · Agents
- [Schema Engine SDK](/Software/Schema_Engine_SDK) — composes · Software
- [Workflow Mapping Service](/Services/Workflow_Mapping_Service) — composes · Services
- [Relational Validation Worker](/Agents/Relational_Validation_Worker) — composes · Agents
- [Deterministic Integration API](/Software/Deterministic_Integration_API) — composes · Software

### Competitors

- [Snorkel AI](/Competitors/Snorkel_AI) — competes with · Competitors
- [UiPath](/Competitors/UiPath) — competes with · Competitors
- [Scale AI](/Competitors/Scale_AI) — competes with · Competitors
- [Automation Anywhere](/Competitors/Automation_Anywhere) — competes with · Competitors
- [Manual Data Entry](/Competitors/Manual_Data_Entry) — competes with · Competitors

### Embodies

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

### Similar Startups

- [Lumill](/Startups/Lumill) — similar · Startups
- [Tractablenon](/Startups/Tractablenon) — similar · Startups
- [Schemadirector](/Startups/Schemadirector) — similar · Startups
- [Structity](/Startups/Structity) — similar · Startups
- [Gorgond](/Startups/Gorgond) — similar · Startups
- [Duputh](/Startups/Duputh) — similar · Startups
- [Parseaxis](/Startups/Parseaxis) — similar · Startups
- [Ocviv](/Startups/Ocviv) — similar · Startups
- [Mentica](/Startups/Mentica) — similar · Startups
- [Strucvert](/Startups/Strucvert) — similar · Startups
- [Documentharbor](/Startups/Documentharbor) — similar · Startups
- [Rebormat](/Startups/Rebormat) — similar · Startups
- [Amberparsing](/Startups/Amberparsing) — similar · Startups
- [Intakevessel](/Startups/Intakevessel) — similar · Startups
- [Struclum](/Startups/Struclum) — similar · Startups
- [Scrub](/Startups/Scrub) — similar · Startups
- [Cornerstonebluff](/Startups/Cornerstonebluff) — similar · Startups
- [Nostruct](/Startups/Nostruct) — similar · Startups
- [Problata](/Startups/Problata) — similar · Startups
- [Contextual Clerk](/Startups/Contextual_Clerk) — similar · Startups