DetVector.co

Guaranteed identical vector outputs on every run, every machine.

Score: 6.7/10BrazilMedium Build
Brand Colors

The Opportunity

Problem

AI systems produce inconsistent outputs for identical inputs because vector databases rely on non-deterministic floating-point arithmetic that varies across CPUs and hardware.

Solution

DetVector replaces standard vector DB calls with a deterministic compute layer that forces fixed-point arithmetic and seeded operations. Engineers send identical inputs and always receive byte-for-byte identical embeddings and similarity results regardless of CPU or GPU. Full audit logs capture every operation for compliance.

Target Audience

AI engineers and platform teams building reproducible, auditable, or safety-critical systems in finance, healthcare, SRE, multi-agent operations, and AI alignment research.

Differentiator

First and only vector service that mathematically guarantees bitwise reproducibility instead of best-effort consistency.

Brand Voice

professional

Features

Deterministic Embedding API

must-have18h

Returns identical vectors for same input across all hardware

Fixed-Point Arithmetic Engine

must-have22h

Replaces floating-point ops with deterministic fixed precision

Reproducibility Audit Log

must-have12h

Immutable record of every vector computation and seed

Hardware Fingerprinting

must-have14h

Detects and compensates for CPU/GPU differences automatically

Seeded Similarity Search

must-have16h

Guarantees identical ranking results on repeated queries

Comparison Diff Tool

nice-to-have8h

Side-by-side view of expected vs actual outputs

Batch Reproducibility Reports

nice-to-have10h

Generates compliance PDFs for audits

Multi-tenant Isolation

future15h

Separate deterministic namespaces per team

Total Build Time: 115 hours

Database Schema

projects

ColumnTypeNullable
iduuidNo
user_iduuidNo
nametextNo
created_attimestampNo

Relationships:

  • user_id references users.id

embeddings

ColumnTypeNullable
iduuidNo
project_iduuidNo
input_hashtextNo
vectortextNo
seedtextNo
created_attimestampNo

Relationships:

  • project_id references projects.id

audit_logs

ColumnTypeNullable
iduuidNo
embedding_iduuidNo
hardware_fingerprinttextNo
output_hashtextNo
created_attimestampNo

Relationships:

  • embedding_id references embeddings.id

API Endpoints

POST
/api/embed

Generate deterministic embedding

🔒 Auth Required
POST
/api/search

Run reproducible similarity search

🔒 Auth Required
GET
/api/audit

Retrieve reproducibility logs

🔒 Auth Required

Tech Stack

Frontend
Next.js 14 + Tailwind + shadcn/ui
Backend
Next.js API routes
Database
Supabase Postgres
Auth
Supabase Auth
Payments
Stripe
Hosting
Vercel
Additional Tools
RedisBullMQFixed-point math library

Build Timeline

Week 1: Core deterministic engine

35h
  • Fixed-point embedding service
  • Basic API

Week 2: Database and auth

28h
  • User accounts
  • Project and embedding tables

Week 3: Audit logging

22h
  • Immutable logs
  • Hardware fingerprinting

Week 4: Dashboard and billing

25h
  • Stripe integration
  • Usage dashboard
Total Timeline: 4 weeks • 110 hours

Pricing Tiers

Free

$0/mo

1 project

  • 1,000 deterministic calls/month
  • Basic audit logs

Pro

$25/mo

10 projects

  • 50,000 calls/month
  • Full audit reports
  • Hardware compensation

Enterprise

$199/mo

Unlimited

  • Unlimited calls
  • SOC2 report
  • Dedicated support
  • On-prem option

Revenue Projections

MonthUsersConversionMRRARR
Month 14012%$120$1,440
Month 638018%$1,710$20,520

Unit Economics

$38
CAC
$420
LTV
4.5%
Churn
82%
Margin
LTV:CAC Ratio: 11.1xExcellent!

Landing Page Copy

Vector embeddings that are identical every single time

Eliminate non-determinism from your AI pipelines with mathematically guaranteed reproducibility

Feature Highlights

Bitwise identical outputs across CPUs
Complete audit trail for regulators
Works with existing vector workflows

Social Proof (Placeholders)

"Finally passed our financial model audit thanks to DetVector"
"Cut reproducibility bugs from 17 to zero in our healthcare agents"

First Three Customers

Post in r/MachineLearning and AI Alignment Discord offering free Pro accounts for 30 days to teams working on reproducible research. DM 20 engineers from finance and healthcare AI teams on LinkedIn who have tweeted about non-determinism issues.

Launch Channels

ProductHuntr/MachineLearningHugging Face forumsAI Engineering newsletter

SEO Keywords

deterministic embeddingsreproducible vector searchconsistent AI outputsvector database auditnon-deterministic fix

Competitive Analysis

Pinecone

pinecone.io
Usage-based
Strength

Scale and managed service

Weakness

No reproducibility guarantees

Our Advantage

Guaranteed identical results on every run

🏰 Moat Strategy

Proprietary fixed-point arithmetic engine plus growing dataset of hardware-specific compensation rules

⏰ Why Now?

Regulatory pressure on AI in finance and healthcare is rising rapidly while existing vector DBs still rely on non-deterministic floating point

Risks & Mitigation

technicalmedium severity

Performance overhead from fixed-point math

Mitigation

Benchmark and offer async batch mode

Validation Roadmap

pre-build7 days

Interview 15 AI engineers about reproducibility pain

Success: 8+ confirm weekly impact

Pivot Options

  • Shift to on-premise appliance for air-gapped environments
  • White-label SDK for embedding model providers

Quick Stats

Build Time
110h
Target MRR (6 mo)
$1,710
Market Size
$480.0M
Features
8
Database Tables
3
API Endpoints
3