# Prifig

*/Startups/Prifig*

## Startup Overview

This data privacy engine maps user consent states directly to database access policies. Instead of treating privacy as an administrative checklist, the system translates user preferences into strict technical constraints. When a user updates their data sharing settings, the platform immediately propagates those changes to the underlying data architecture.

Data engineering and compliance teams routinely struggle to enforce privacy mandates across distributed data environments. Relying on manual compliance tickets and disconnected preference centers creates dangerous gaps between legal consent and actual system access. This disconnect exposes organizations to structural non-compliance and unintended data misuse.

Unlike legacy compliance tools like OneTrust or TrustArc that operate at the application layer, this system is entirely infrastructure-native. It enforces privacy rules in real-time at the query level. Any internal data request that violates an active user consent state is automatically blocked before execution, securing compliance directly within database operations.

## Startup Founding Hypothesis

**Approach**: that maps user consent states directly to database access policies
**Competitors**:
- [OneTrust](/Competitors/OneTrust)
- [TrustArc](/Competitors/TrustArc)
- [manual compliance tickets](/Competitors/manual_compliance_tickets)
**Differentiator2x2**: infrastructure-native and enforced in real-time at the query level

## Startup Solution Coordinate

**Solution**: [Consent Policy Engine](/Software/Consent_Policy_Engine)

## Startup Position2x2

```mermaid
quadrantChart
title Database Privacy & Consent Enforcement
x-axis Application Layer --> Infrastructure Native
y-axis Post-hoc/Manual Audit --> Real-time Query Enforcement
quadrant-1 Native & Real-time
quadrant-2 Application & Real-time
quadrant-3 Application & Manual
quadrant-4 Native & Manual
OneTrust: [0.25, 0.45]
TrustArc: [0.15, 0.35]
manual compliance tickets: [0.10, 0.10]
Prifig: [0.85, 0.85]
```

## Startup Offer

**Proof**:
- Aiming to reduce unauthorized data access incidents to zero for high-compliance healthcare and fintech platforms
- Targeting sub-10 millisecond query latency overhead when enforcing mapped privacy policies
- Designed to automate 100% of data-deletion and data-masking requests directly at the infrastructure level
**Tiers**:
- Name: Startup Node · Price: ~$200–$500/mo · Inclusions: Designed for single-database applications, supporting up to 50,000 monthly active tracked users and intended mapping for standard PostgreSQL/MySQL native row-level security.
- Name: Distributed Enforcement · Price: ~$1,000–$2,500/mo · Inclusions: Intended for multi-database environments, up to 500,000 monthly active users, featuring real-time query interception and designed sync with policy engines like OPA.
- Name: Enterprise Fabric · Price: ~$4,000–$8,000/mo · Inclusions: Uncapped active users, intended support for data warehouses (Snowflake, BigQuery), continuous compliance reporting, and VPC deployment architecture.
**Guarantee**: Prifig guarantees that mapped database access policies update within 50 milliseconds of an upstream user consent state change, or the current month's usage fees are refunded in full.
**Business Function**: ProvideService
**Objection Handlers**:
- Query latency concerns: Buyers worry adding policy checks destroys database performance. Rebuttal: Prifig is designed to compile consent states directly into native row-level security (RLS) rules, eliminating application-layer overhead.
- Redundancy with OneTrust: Buyers claim they already have a consent management platform. Rebuttal: OneTrust only captures the consent; Prifig is designed to ingest those webhooks and actively enforce the rules in your database infrastructure.
- Schema mutation breaks: Buyers fear database schema changes will orphan the policy maps. Rebuttal: The platform intends to automatically detect schema changes during CI/CD and flag unmapped PII columns before deployment.
**Pricing Architecture**: UsageMeter
**Agent Checkout Support**:
- agentic-commerce-protocol

## Startup Brand

**Voice**: Direct infrastructure register marked by exacting technical precision
**Tagline**: Enforce user consent natively on every database query
**Icon Concept**: Valve
**Palette Intent**: institutional-cool
**Visual Identity**: A stark palette of slate gray and cool institutional blue anchors monospace typography, using structural database schema motifs rather than generic security padlocks.
**Archetype Reference**: the-ruler

## Startup Buyer Chain

**Chain**: Prifig → Data Engineering Lead → Privacy Officer → Application End-User
**Gtm Motion**: Bottom-up acquisition targeting data engineering teams burdened by manual compliance tickets, starting with a self-serve implementation for a single database or application. Expansion occurs cross-departmentally as privacy and compliance officers mandate the query-level enforcement standard across all enterprise data stores.
**Agent Channel**: Designed to list in the Model Context Protocol (MCP) registry and LangChain tool catalogs, enabling automated data governance and compliance-auditing agents to discover and interact with the consent-mapping policies dynamically.
**Primary Channel**: Technical SEO and developer-focused content surfaces targeting specific implementation pain points, such as queries for 'mapping OneTrust consent to PostgreSQL' or 'automating GDPR deletion requests at the database tier'.

## Startup Customer Journey

```mermaid
flowchart LR; A[Technical SEO Article] --> B[Developer Sandbox Account]; B --> C[PostgreSQL RLS Rule]; C --> D[Consent Webhook Pipeline]; D --> E[Enterprise Data Warehouse]; E --> F[Compliance Audit Agent]
```

## 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 staging environment deployment for a single-database application, aiming to validate sub-10ms latency overhead while actively enforcing row-level security for up to 50,000 mapped user consent states.
- A 30-day multi-database integration pilot, targeting successful synchronization with an existing Open Policy Agent (OPA) setup to demonstrate real-time query interception and automated data masking without application downtime.
**Target Metrics**:
- Target: <10 millisecond query latency overhead during active policy enforcement
- Target: <50 millisecond synchronization time between upstream consent state changes and database access policy updates
- Aim: 100 percent automation of right-to-be-forgotten data-deletion and masking requests
- Target: 0 unauthorized data access incidents resulting from unmapped PII columns after CI/CD schema mutations
**Target Case Studies**:
- A mid-market fintech platform connecting their existing consent management tool to PostgreSQL, using Prifig to automatically translate user opt-outs into native row-level security rules without writing new application-layer code.
- A healthcare SaaS provider operating a multi-database architecture, using Prifig to intercept queries and enforce real-time data masking at the infrastructure level while maintaining strict sub-10 millisecond latency overhead.
- An enterprise data warehouse team managing Snowflake and BigQuery environments, deploying Prifig to automate 100 percent of user data-deletion requests and continuous compliance reporting, eliminating manual DBA scripting.
**Testimonial Targets**:
- Head of Engineering expressing relief that Prifig compiles privacy rules directly into native RLS, proving that strict data compliance does not destroy database query performance.
- Chief Information Security Officer stating confidence that web-layer consent states now definitively translate into hard database infrastructure enforcement, closing the gap between compliance platforms and actual data access.
- Lead Database Administrator praising the CI/CD integration that automatically detects schema changes and flags unmapped PII columns before they reach production environments.

## Startup Top Risks

**Risks**:
- Severity: existential · Description: Real-time query interception introduces unacceptable latency to production database read operations, causing engineering teams to remove the tool. · Mitigation Status: in-progress
- Severity: high · Description: A bug in policy enforcement blocks legitimate application queries or leaks unconsented PII, creating massive liability and immediate customer churn. · Mitigation Status: unmitigated
- Severity: high · Description: Enterprise security teams refuse to grant an early-stage startup deep infrastructure-level access to their core production databases. · Mitigation Status: in-progress
- Severity: moderate · Description: The platform fails to support a sufficiently broad range of database dialects, stalling adoption in heterogeneous infrastructure environments. · Mitigation Status: unmitigated

## Startup Competitors

- [OneTrust](/Competitors/OneTrust) — Incumbent
- [TrustArc](/Competitors/TrustArc) — Incumbent
- [Manual Compliance Tickets](/Competitors/Manual_Compliance_Tickets) — Status Quo
- [Ketch Privacy](/Competitors/Ketch_Privacy) — Data Control Platform
- [Securiti Data Command](/Competitors/Securiti_Data_Command) — Data Security Platform

## Startup Story Brand

**Hero**:
- **Need**: to be the guardian of data integrity, not a ticket-processor for compliance
- **Want**: to enforce privacy policies directly at the infrastructure level
- **Identity**: the lead architect at a high-compliance fintech platform
**Plan**:
- Step: Connect · Detail: Map your OneTrust consent signals to your production PostgreSQL or Snowflake schema.
- Step: Check · Detail: Verify that every PII column is covered by a real-time query-level enforcement policy.
- Step: Deploy · Detail: Automate data-masking and deletion requests directly at the infrastructure level during CI/CD.
**Guide**:
- **Empathy**: When a user revokes consent in a web form, that preference often sits idle in a SaaS dashboard while your database remains wide open.
**Problem**:
- **Villain**: disconnected consent states
- **External**: Privacy choices captured in OneTrust or TrustArc never actually reach the PostgreSQL row-level security policies where data lives.
- **Internal**: You feel anxious that a single application-layer bug will leak PII and trigger a catastrophic audit.
- **Philosophical**: Every technical lead deserves a database that respects user rights by default — not a pile of manual compliance tickets.
**Success**: User consent is enforced on every query with sub-10ms overhead and zero manual intervention.
**One Liner**: What if your database refused to return PII whenever a user revoked consent? Prifig maps consent states to native row-level security, ensuring real-time privacy enforcement.
**Positioning**:
- **So That**: consent states are enforced natively on every query
- **Unlike**: manual compliance tickets and disconnected dashboards
- **For Whom**: architects at high-compliance fintech and healthcare platforms
- **Category**: Real-time Privacy Enforcement Infrastructure
**Call To Action**:
- **Direct**: Launch Startup Node
- **Transitional**: View enforcement schema motifs
**Failure Stakes**:
- Unauthorized PII access incidents
- Stalled compliance audits
- Manual data-deletion backlogs
**Transformation**:
- **To**: free to build features on a self-governing data fabric, no longer stuck fixing manual compliance leaks
- **From**: a developer burying privacy logic in application code
**Controlling Idea**: Privacy belongs in the database engine, not the application layer.

## Startup Token Hero

**Genre**: founding-hypothesis
**Rendered**: What if your database refused to return PII whenever a user revoked consent? Prifig maps consent states to native row-level security, ensuring real-time privacy enforcement.
**Mechanism**: spine-derived-v1
**Template Id**: spine-founding-hypothesis
**Vocab Fingerprint**: a9b5751e5b6e0e6f

## Startup Token Positioning

**Genre**: moore-positioning
**Rendered**: Real-time Privacy Enforcement Infrastructure for architects at high-compliance fintech and healthcare platforms. Unlike manual compliance tickets and disconnected dashboards — consent states are enforced natively on every query.
**Mechanism**: spine-derived-v1
**Template Id**: spine-moore-positioning
**Vocab Fingerprint**: d168ec93d7412c7f

## Startup Token Pitch Deck

**Genre**: pitch-deck
**Rendered**: Problem: Privacy choices captured in OneTrust or TrustArc never actually reach the PostgreSQL row-level security policies where data lives.
Solution: What if your database refused to return PII whenever a user revoked consent? Prifig maps consent states to native row-level security, ensuring real-time privacy enforcement.
Customer: architects at high-compliance fintech and healthcare platforms
Unlike: manual compliance tickets and disconnected dashboards
**Mechanism**: spine-derived-v1
**Template Id**: spine-pitch-deck
**Vocab Fingerprint**: b8c276d7f7688e6d

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

**Pain**: Privacy choices captured in OneTrust or TrustArc never actually reach the PostgreSQL row-level security policies where data lives.
**Metrics**: Target: User consent is enforced on every query with sub-10ms overhead and zero manual intervention.
**Rendered**: Pain: Privacy choices captured in OneTrust or TrustArc never actually reach the PostgreSQL row-level security policies where data lives.
Economic buyer: Data Engineering Lead
Metrics: Target: User consent is enforced on every query with sub-10ms overhead and zero manual intervention.
Competition: manual compliance tickets and disconnected dashboards
**Mechanism**: spine-derived-v1
**Competition**: manual compliance tickets and disconnected dashboards
**Economic Buyer**: Data Engineering Lead
**Vocab Fingerprint**: d4c8c3743742b217

## Startup Token Cold Email

**Genre**: cold-email
**Rendered**: Subject: Real-time Privacy Enforcement Infrastructure for architects at high-compliance fintech and healthcare platforms

architects at high-compliance fintech and healthcare platforms — Privacy choices captured in OneTrust or TrustArc never actually reach the PostgreSQL row-level security policies where data lives. What if your database refused to return PII whenever a user revoked consent? Prifig maps consent states to native row-level security, ensuring real-time privacy enforcement.
**Mechanism**: spine-derived-v1
**Template Id**: spine-cold-email
**Vocab Fingerprint**: a66328f02fa34ba2

## Startup Token Agent Spec

**Genre**: ai-agent-spec
**Rendered**: Real-time Privacy Enforcement Infrastructure. What if your database refused to return PII whenever a user revoked consent? Prifig maps consent states to native row-level security, ensuring real-time privacy enforcement. Serves architects at high-compliance fintech and healthcare platforms.
**Mechanism**: spine-derived-v1
**Template Id**: spine-ai-agent-spec
**Vocab Fingerprint**: 4874c1746f2595b0

## Neighborhood

### Candidate solutions

- [API Integration Drop-Off](/Problems/API_Integration_Drop-Off) — candidate solution for · Problems

### Competitors

- [Ketch Privacy](/Competitors/Ketch_Privacy) — competes with · Competitors
- [Securiti Data Command](/Competitors/Securiti_Data_Command) — competes with · Competitors
- [OneTrust](/Competitors/OneTrust) — competes with · Competitors
- [TrustArc](/Competitors/TrustArc) — competes with · Competitors
- [Manual Compliance Tickets](/Competitors/Manual_Compliance_Tickets) — competes with · Competitors
- [AWS Lambda](/Competitors/AWS_Lambda) — competes with · Competitors
- [Playwright](/Competitors/Playwright) — competes with · Competitors
- [custom polling loops](/Competitors/custom_polling_loops) — competes with · Competitors
- [synchronous scraping endpoints](/Competitors/synchronous_scraping_endpoints) — competes with · Competitors
- [local Puppeteer containers](/Competitors/local_Puppeteer_containers) — competes with · Competitors
- [Local Playwright Containers](/Competitors/Local_Playwright_Containers) — competes with · Competitors
- [Self-Hosted Puppeteer](/Competitors/Self-Hosted_Puppeteer) — competes with · Competitors
- [Synchronous Extraction APIs](/Competitors/Synchronous_Extraction_APIs) — competes with · Competitors
- [Playwright Containers](/Competitors/Playwright_Containers) — competes with · Competitors
- [AWS Lambda Endpoints](/Competitors/AWS_Lambda_Endpoints) — competes with · Competitors
- [synchronous scraping APIs](/Competitors/synchronous_scraping_APIs) — competes with · Competitors
- [Puppeteer Clusters](/Competitors/Puppeteer_Clusters) — competes with · Competitors
- [Apify](/Competitors/Apify) — competes with · Competitors
- [Vercel Serverless Endpoints](/Competitors/Vercel_Serverless_Endpoints) — competes with · Competitors
- [Synchronous Lambda Endpoints](/Competitors/Synchronous_Lambda_Endpoints) — competes with · Competitors
- [Containerized Playwright](/Competitors/Containerized_Playwright) — competes with · Competitors
- [Synchronous REST APIs](/Competitors/Synchronous_REST_APIs) — competes with · Competitors
- [Puppeteer](/Competitors/Puppeteer) — competes with · Competitors
- [Synchronous API Endpoints](/Competitors/Synchronous_API_Endpoints) — competes with · Competitors
- [Custom Webhook Listeners](/Competitors/Custom_Webhook_Listeners) — competes with · Competitors
- [Local Headless Browser Containers](/Competitors/Local_Headless_Browser_Containers) — competes with · Competitors
- [Vercel Serverless](/Competitors/Vercel_Serverless) — competes with · Competitors
- [Browserless Endpoints](/Competitors/Browserless_Endpoints) — competes with · Competitors
- [Browserless](/Competitors/Browserless) — competes with · Competitors
- [Apify Scrapers](/Competitors/Apify_Scrapers) — competes with · Competitors
- [Puppeteer Containers](/Competitors/Puppeteer_Containers) — competes with · Competitors
- [custom polling scripts](/Competitors/custom_polling_scripts) — competes with · Competitors
- [Synchronous REST endpoints](/Competitors/Synchronous_REST_endpoints) — competes with · Competitors
- [Custom Lambda polling loops](/Competitors/Custom_Lambda_polling_loops) — competes with · Competitors
- [synchronous serverless scripts](/Competitors/synchronous_serverless_scripts) — competes with · Competitors

### What it offers

- [Consent Policy Engine](/Software/Consent_Policy_Engine) — offers · Software
- [Prifig Async Relay](/Services/Prifig_Async_Relay) — offers · Services
- [Async Payload Relay](/Services/Async_Payload_Relay) — offers · Services

### Embodies

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

### Composed of

- [Webhook Sieve API](/Software/Webhook_Sieve_API) — composes · Software
- [Async Buffer SDK](/Software/Async_Buffer_SDK) — composes · Software
- [Pipeline Hydration Agent](/Agents/Pipeline_Hydration_Agent) — composes · Agents
- [Timeout Orchestration Worker](/Agents/Timeout_Orchestration_Worker) — composes · Agents
- [Extraction Relay Service](/Services/Extraction_Relay_Service) — composes · Services
- [Timeout Recovery Agent](/Agents/Timeout_Recovery_Agent) — composes · Agents
- [Lifecycle Webhook API](/Software/Lifecycle_Webhook_API) — composes · Software
- [Async Orchestration SDK](/Software/Async_Orchestration_SDK) — composes · Software
- [DOM Rendering Worker](/Agents/DOM_Rendering_Worker) — composes · Agents
- [Payload Relay Service](/Services/Payload_Relay_Service) — composes · Services

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