# Vellum

*/Startups/Vellum*

## Startup Overview

Engineering teams building generative applications lack reliable methods to validate changes to underlying models and context windows. This environment provides a version-controlled workspace to author, manage, and track prompt iterations. Instead of relying on manual spot-checks or brittle in-house tooling, developers deploy changes through a structured pipeline that treats prompt engineering as a rigorous software discipline.

Unlike frameworks such as LangSmith, PromptLayer, or ad-hoc Python scripts that bind teams to specific ecosystems, this architecture operates independent of any single model provider. It executes strict quantitative regression tests against a matrix of large language models and parameters. By enforcing rigorously deterministic evaluation metrics, developers isolate variables, measure exact output drift, and deploy prompt updates into production with strict reliability.

## Startup Founding Hypothesis

**Approach**: that versions prompts and executes strict quantitative regression tests
**Competitors**:
- [LangSmith](/Competitors/LangSmith)
- [PromptLayer](/Competitors/PromptLayer)
- [custom Python scripts](/Competitors/custom_Python_scripts)
**Differentiator2x2**: model-agnostic for deployment and rigorously deterministic in its evaluation metrics

## Startup Solution Coordinate

**Solution**: [Vellum Evaluation Engine](/Software/Vellum_Evaluation_Engine)

## Startup Position2x2

```mermaid
quadrantChart
title Positioning: Model-Agnostic vs Deterministic Evaluation
x-axis "Model-coupled" --> "Model-agnostic"
y-axis "Heuristic Evaluation" --> "Deterministic Evaluation"
quadrant-1 "Agnostic & Deterministic"
quadrant-2 "Coupled & Deterministic"
quadrant-3 "Coupled & Heuristic"
quadrant-4 "Agnostic & Heuristic"
Vellum: [0.85, 0.88]
LangSmith: [0.25, 0.65]
PromptLayer: [0.70, 0.50]
custom Python scripts: [0.95, 0.15]
```

## Startup Offer

**Proof**:
- Targeting ML teams: catch strict quantitative prompt degradation before production deployment.
- Aiming for AI product managers: reduce manual prompt evaluation time by establishing deterministic test criteria.
- Targeting enterprise engineering: achieve consistent multi-model benchmarking across automated CI/CD pipelines.
**Tiers**:
- Name: Developer Sandbox · Price: ~$40–$90/mo · Inclusions: Up to 3 team members, 5,000 monthly test executions, version history retention, and standard deterministic evaluation metrics.
- Name: Production Team · Price: ~$250–$600/mo · Inclusions: Up to 10 team members, 50,000 monthly test executions, multi-model parallel benchmarking, and CI/CD integration pipelines.
- Name: Enterprise Scale · Price: custom: ~$15k–$35k/yr · Inclusions: Unlimited seats, millions of test executions, custom quantitative evaluation metrics, and dedicated deployment architectures.
**Guarantee**: If the regression suite fails to flag a prompt change that mathematically degrades output quality against your defined deterministic baselines, your next month of service is fully refunded.
**Business Function**: ProvideService
**Objection Handlers**:
- Objection: We already use LangSmith for observability. Rebuttal: Vellum focuses strictly on pre-deployment quantitative regression testing, complementing post-deployment tracing.
- Objection: Our custom Python scripts handle our evaluations. Rebuttal: Vellum standardizes those isolated scripts into version-controlled, collaborative test suites that product teams can safely operate.
- Objection: We are locked into one provider and do not need model routing. Rebuttal: Vellum separates the prompt logic from the deployment model, allowing you to seamlessly benchmark alternatives without rewriting your core workflow.
**Pricing Architecture**: Tiered
**Agent Checkout Support**:
- agentic-commerce-protocol

## Startup Brand

**Voice**: Clinical register grounded in statistical precision and deterministic outcomes.
**Tagline**: Deploy prompt changes with deterministic regression tests.
**Icon Concept**: caliper
**Palette Intent**: electric-signal
**Visual Identity**: Slate gray and stark white contrast with precise terminal-green accents, evoking command-line test outputs.
**Archetype Reference**: the-sage

## Startup Buyer Chain

**Chain**: Vellum → AI Engineering Team → LLM Application User
**Gtm Motion**: Developer-led adoption where individual engineers integrate the platform to debug prompt drift locally, expanding to enterprise contracts when the tool is embedded into the broader engineering organization's CI/CD pipeline for automated regression testing.
**Agent Channel**: Designed to list as a testing capability in the Model Context Protocol (MCP) directory and LangChain tool ecosystem, enabling autonomous coding agents to discover and invoke strict prompt evaluations during automated code generation.
**Primary Channel**: Organic search for technical queries like 'LLM regression testing' and 'prompt version control', alongside targeted developer advocacy in technical communities like Hacker News and AI engineering Discord servers.

## Startup Customer Journey

```mermaid
flowchart LR; A[Technical Community] --> B[Developer Sandbox]; B --> C[Regression Test Suite]; C --> D[CI/CD Pipeline]; D --> E[Production Team Plan]; E --> F[Enterprise Deployment]; F --> G[Engineering Organization];
```

## 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 parallel testing pilot where an engineering team runs their existing Python scripts alongside the platform to prove the automated suite catches quantitative regression errors faster and with fewer false positives
- A 30-day CI/CD integration sprint with an enterprise ML team aimed at proving the system can successfully automate 50,000 multi-model benchmark tests without disrupting their existing post-deployment tracing tools
**Target Metrics**:
- Target: 100% detection rate of mathematical prompt degradation against defined baselines prior to production release
- Aim: 80% reduction in manual prompt evaluation hours per deployment cycle
- Target: 3x increase in parallel multi-model benchmarking execution speed compared to isolated custom scripts
- Aim: Zero undetected output quality regressions passing through automated CI/CD pipelines
**Target Case Studies**:
- A mid-sized fintech machine learning team transitions from ad-hoc Python evaluation scripts to version-controlled test suites, allowing product managers to safely modify and benchmark prompts without engineering bottlenecks
- An enterprise software engineering department integrates pre-deployment regression testing into their CI/CD pipeline, catching strict quantitative prompt degradation before production deployment across multiple LLM providers
- An early-stage AI application startup utilizes the deterministic testing sandbox to establish baseline evaluations for core features, eliminating manual prompt review cycles and safely decoupling prompt logic from deployment models
**Testimonial Targets**:
- Lead Machine Learning Engineer expressing relief that they no longer manually maintain disparate evaluation scripts because pre-deployment regression testing is standardized
- AI Product Manager highlighting the newfound ability to safely test and iterate on prompt logic against quantitative metrics without waiting for developer bandwidth
- VP of Engineering validating that separating prompt logic from deployment models allowed the team to benchmark alternative providers seamlessly and avoid vendor lock-in

## Startup Top Risks

**Risks**:
- Severity: existential · Description: Foundation model providers like OpenAI release robust built-in prompt versioning and evaluation suites that render third-party tooling obsolete. · Mitigation Status: in-progress
- Severity: high · Description: Engineering teams default to building in-house evaluation pipelines using custom Python scripts rather than paying for a specialized SaaS product. · Mitigation Status: unmitigated
- Severity: high · Description: Direct competitor LangSmith leverages its deep integration with the LangChain ecosystem to capture enterprise accounts before Vellum establishes market share. · Mitigation Status: in-progress
- Severity: moderate · Description: Executing high-volume quantitative regression tests triggers API rate limits from underlying model providers, causing severe test bottlenecks for users. · Mitigation Status: in-progress

## Startup Competitors

- [LangSmith](/Competitors/LangSmith) — LLMOps Platform
- [PromptLayer](/Competitors/PromptLayer) — Prompt Engineering
- [Custom Python Scripts](/Competitors/Custom_Python_Scripts) — Status Quo
- [Braintrust Data](/Competitors/Braintrust_Data) — LLM Evaluation
- [Weights And Biases](/Competitors/Weights_And_Biases) — MLOps Platform
- [Helicone AI](/Competitors/Helicone_AI) — LLM Observability

## Startup Story Brand

**Hero**:
- **Need**: to be the rigorous architect of stable AI systems, not a prompt-guesser
- **Want**: to deploy prompt updates without breaking existing model performance
- **Identity**: the machine learning engineer at an AI-first software company
**Plan**:
- Step: Upload · Detail: Import your current prompt versions and baseline datasets from your existing local Python environment.
- Step: Check · Detail: Run automated regression suites to identify mathematical score drops across your defined evaluation metrics.
- Step: Deploy · Detail: Ship the validated prompt with statistical confidence that your output quality remains intact.
**Guide**:
- **Empathy**: When a prompt change improves one edge case but degrades ten others, your team loses weeks to manual rollback.
**Problem**:
- **Villain**: subjective evaluation
- **External**: Manually reviewing LLM outputs in custom Python scripts or LangSmith traces leads to silent regressions in production.
- **Internal**: You feel a constant underlying anxiety every time you push a prompt change to the CI/CD pipeline.
- **Philosophical**: Engineering expertise belongs in mathematical proofs, not in vibes-based testing.
**Success**: Prompt deployments are backed by hard data, reducing manual review time by 90% and ensuring multi-model stability.
**One Liner**: Subjective prompt tuning costs AI teams production stability. Vellum versions prompts and executes strict quantitative regression tests so every deployment is mathematically validated.
**Positioning**:
- **So That**: catch prompt degradation before production deployment with mathematical certainty
- **Unlike**: manual evaluation and LangSmith traces
- **For Whom**: machine learning engineers and AI product managers
- **Category**: Prompt Regression Testing Platform
**Call To Action**:
- **Direct**: Run regression test
- **Transitional**: View sample evaluation report
**Failure Stakes**:
- Silent production regressions
- Manual evaluation bottlenecks
- Model-provider lock-in
**Transformation**:
- **To**: the product's deterministic quality lead
- **From**: a prompt-tuning engineer reliant on vibes
**Controlling Idea**: AI product reliability must be governed by deterministic quantitative metrics, not human observation.

## Startup Token Hero

**Genre**: founding-hypothesis
**Rendered**: Subjective prompt tuning costs AI teams production stability. Vellum versions prompts and executes strict quantitative regression tests so every deployment is mathematically validated.
**Mechanism**: spine-derived-v1
**Template Id**: spine-founding-hypothesis
**Vocab Fingerprint**: 1c0fdc63ba9de239

## Startup Token Positioning

**Genre**: moore-positioning
**Rendered**: Prompt Regression Testing Platform for machine learning engineers and AI product managers. Unlike manual evaluation and LangSmith traces — catch prompt degradation before production deployment with mathematical certainty.
**Mechanism**: spine-derived-v1
**Template Id**: spine-moore-positioning
**Vocab Fingerprint**: d8bfa0bbf7158966

## Startup Token Pitch Deck

**Genre**: pitch-deck
**Rendered**: Problem: Manually reviewing LLM outputs in custom Python scripts or LangSmith traces leads to silent regressions in production.
Solution: Subjective prompt tuning costs AI teams production stability. Vellum versions prompts and executes strict quantitative regression tests so every deployment is mathematically validated.
Customer: machine learning engineers and AI product managers
Unlike: manual evaluation and LangSmith traces
**Mechanism**: spine-derived-v1
**Template Id**: spine-pitch-deck
**Vocab Fingerprint**: 09026cf8d67fee5c

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

**Pain**: Manually reviewing LLM outputs in custom Python scripts or LangSmith traces leads to silent regressions in production.
**Metrics**: Target: Prompt deployments are backed by hard data, reducing manual review time by 90% and ensuring multi-model stability.
**Rendered**: Pain: Manually reviewing LLM outputs in custom Python scripts or LangSmith traces leads to silent regressions in production.
Economic buyer: AI Engineering Team
Metrics: Target: Prompt deployments are backed by hard data, reducing manual review time by 90% and ensuring multi-model stability.
Competition: manual evaluation and LangSmith traces
**Mechanism**: spine-derived-v1
**Competition**: manual evaluation and LangSmith traces
**Economic Buyer**: AI Engineering Team
**Vocab Fingerprint**: 98e28b3e93f8d8ec

## Startup Token Cold Email

**Genre**: cold-email
**Rendered**: Subject: Prompt Regression Testing Platform for machine learning engineers and AI product managers

machine learning engineers and AI product managers — Manually reviewing LLM outputs in custom Python scripts or LangSmith traces leads to silent regressions in production. Subjective prompt tuning costs AI teams production stability. Vellum versions prompts and executes strict quantitative regression tests so every deployment is mathematically validated.
**Mechanism**: spine-derived-v1
**Template Id**: spine-cold-email
**Vocab Fingerprint**: 3f1bba3e0345ad58

## Startup Token Agent Spec

**Genre**: ai-agent-spec
**Rendered**: Prompt Regression Testing Platform. Subjective prompt tuning costs AI teams production stability. Vellum versions prompts and executes strict quantitative regression tests so every deployment is mathematically validated. Serves machine learning engineers and AI product managers.
**Mechanism**: spine-derived-v1
**Template Id**: spine-ai-agent-spec
**Vocab Fingerprint**: 285c6ab8713363af

## Neighborhood

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### Competitors

- [Braintrust Data](/Competitors/Braintrust_Data) — competes with · Competitors
- [Helicone AI](/Competitors/Helicone_AI) — competes with · Competitors
- [Weights And Biases](/Competitors/Weights_And_Biases) — competes with · Competitors
- [LangSmith](/Competitors/LangSmith) — competes with · Competitors
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### Embodies

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

### What it offers

- [Vellum Evaluation Engine](/Software/Vellum_Evaluation_Engine) — offers · Software
- [Vellum Tax Engine](/Software/Vellum_Tax_Engine) — offers · Software
- [Vellum Tax Compute](/Software/Vellum_Tax_Compute) — offers · Software

### Composed of

- [Tax Computation Service](/Services/Tax_Computation_Service) — composes · Services
- [Contextual Parsing Engine](/Software/Contextual_Parsing_Engine) — composes · Software
- [K-1 Extraction Agent](/Agents/K-1_Extraction_Agent) — composes · Agents
- [Variance Detection Worker](/Agents/Variance_Detection_Worker) — composes · Agents
- [Calculated Ledger API](/Software/Calculated_Ledger_API) — composes · Software
- [Liability Calculation API](/Software/Liability_Calculation_API) — composes · Software
- [Missing Artifact Agent](/Agents/Missing_Artifact_Agent) — composes · Agents
- [Schedule Extraction Worker](/Agents/Schedule_Extraction_Worker) — composes · Agents

### Who it serves

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

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