# Forgetune

*/Startups/Forgetune*

## Startup Overview

This machine unlearning infrastructure excises specific training instances directly from large language model weights. Rather than filtering outputs, it locates and mathematically removes the exact parameter influence associated with unwanted, copyrighted, or sensitive data. Engineers submit a target dataset for removal, and the system outputs a scrubbed base model ready for immediate deployment.

Enterprise AI teams and compliance officers face rigid bottlenecks when a production model accidentally ingests proprietary code, personal data, or protected intellectual property. To comply with data deletion requests or correct ingestion errors, teams typically have to discard the model entirely and execute a full, costly retraining run from scratch.

Competing solutions like prompt-level guardrails only hide bad outputs, while LoRA adapter masking adds computational overhead during generation. This approach replaces those workarounds with a cryptographically verified ablation method that guarantees the targeted data is permanently erased. Because the excision happens directly at the base weight level, the scrubbed model runs with zero inference-time latency penalties.

## Startup Founding Hypothesis

**Approach**: that excises specific training instances directly from model weights
**Competitors**:
- [Full Model Retraining](/Competitors/Full_Model_Retraining)
- [Prompt-Level Guardrails](/Competitors/Prompt-Level_Guardrails)
- [LoRA Adapter Masking](/Competitors/LoRA_Adapter_Masking)
**Differentiator2x2**: a cryptographically verified ablation method with zero inference-time latency penalties

## Startup Solution Coordinate

**Solution**: [Verified Ablation Engine](/Software/Verified_Ablation_Engine)

## Startup Position2x2

```mermaid
quadrantChart
    x-axis Surface-Level Masking --> Cryptographically Verified Ablation
    y-axis High Inference Latency --> Zero Inference Latency
    quadrant-1 Provable & Performant
    quadrant-2 Baseline Non-Ablation
    quadrant-3 Sluggish & Superficial
    quadrant-4 Fast but Unverified
    Prompt-Level Guardrails: [0.15, 0.20]
    LoRA Adapter Masking: [0.40, 0.45]
    Full Model Retraining: [0.10, 0.85]
    Forgetune: [0.90, 0.90]
```

## Startup Offer

**Proof**:
- Targeting enterprise AI labs excising PII from 70B models without full retraining.
- Aiming to generate cryptographic proof of copyright removal to satisfy legal audits.
- Seeking to complete targeted ablations on sub-14B parameter models in under 4 hours.
**Tiers**:
- Name: Base Ablation · Price: ~$2,500–$5,000 per model · Inclusions: Targeted weight excision for open-weight models up to 14B parameters, including automated capability retention testing and baseline cryptographic verification.
- Name: Enterprise Fleet · Price: ~$30,000–$60,000/yr · Inclusions: Up to 20 ablation cycles per month for models up to 70B parameters, priority compute access, and verifiable compliance reporting designed for legal audits.
**Guarantee**: If the post-ablation model demonstrates a degradation of more than 2% on standard capability benchmarks (e.g., MMLU, HumanEval) compared to the pre-ablated baseline, the ablation cycle is refunded and re-run at no cost.
**Business Function**: ProvideService
**Objection Handlers**:
- Objection: Direct weight modification causes catastrophic forgetting in unrelated domains. Rebuttal: Forgetune maps the specific activation pathways of the target data to selectively dampen exact weights, isolating the excision to prevent generalized degradation.
- Objection: We can just use a system prompt guardrail to block the output. Rebuttal: Guardrails increase inference latency and remain vulnerable to jailbreaks; our ablation mathematically removes the knowledge from the weights so it cannot be extracted.
- Objection: How do we prove to regulators the data is actually gone? Rebuttal: The platform is designed to generate a cryptographic hash of the ablation delta, proving the specific training instance no longer influences the model distribution.
**Pricing Architecture**: Tiered
**Agent Checkout Support**:
- agentic-commerce-protocol

## Startup Brand

**Voice**: Highly technical and authoritative, distinguished by absolute cryptographic finality.
**Tagline**: Excise targeted training data from AI models without full retraining.
**Icon Concept**: scalpel
**Palette Intent**: electric-signal
**Visual Identity**: The visual identity contrasts deep terminal black with sharp neon cyan accents, utilizing monospace typography to evoke cryptographic hashes and targeted neural weight edits.
**Archetype Reference**: the-sage

## Startup Buyer Chain

**Chain**: Forgetune → Enterprise ML Operations / Legal Teams → Foundation Model Consumers
**Gtm Motion**: Acquires AI teams via targeted technical content and self-serve API access for single-instance compliance emergencies, such as urgent copyright or PII removal. Expands by embedding the cryptographic ablation protocol directly into the customer's CI/CD pipeline for continuous model auditing and automated data excision across model fleets.
**Agent Channel**: Designed to be registered in automated MLOps tool catalogs and autonomous agent frameworks, targeting integration into compliance-evaluator agents that would automatically trigger and verify specific instance deletions from model weights.
**Primary Channel**: Technical search intent on platforms like Hugging Face, GitHub, and Stack Overflow for specific developer queries like 'LLM machine unlearning API', 'remove PII from weights without retraining', or 'cryptographic model ablation'.

## Startup Customer Journey

```mermaid
flowchart LR
A[Developer Query] --> B[Emergency PII Excision]
B --> C[Ablation API Endpoint]
C --> D[Cryptographic Delta Hash]
D --> E[CI/CD Pipeline]
E --> F[Autonomous Compliance Agent]
F --> G[Legal Audit Report]
```

## 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 pilot excising 500 specific copyrighted documents from a 14B parameter open-weight model, aiming to prove zero extraction of the target text under adversarial testing while retaining baseline benchmark scores.
- A 30-day enterprise evaluation running up to 10 ablation cycles on a 70B model to remove injected PII, targeting the successful generation of a verifiable compliance report recognized by the pilot's legal team.
**Target Metrics**:
- Target: Under 4 hours to complete targeted ablations on sub-14B parameter models
- Aim: <2% capability degradation on standard benchmarks like MMLU and HumanEval compared to the pre-ablated baseline
- Target: 100% elimination of target data extraction during adversarial jailbreak testing
- Aim: 90% reduction in compute expenditure compared to running a full model retraining cycle to remove data
**Target Case Studies**:
- A mid-sized healthcare AI lab (Lead Research Scientist) mathematically excises leaked patient PII from a fine-tuned 70B model, avoiding a costly full retraining cycle while retaining 98% diagnostic accuracy.
- An enterprise legal tech provider (Chief Compliance Officer) removes specific copyrighted case law from a 14B parameter model, generating a cryptographic compliance report to satisfy a regulatory audit within 48 hours.
- A Series B generative AI startup (VP of Engineering) automates the removal of toxic training data from their production models, enabling them to drop high-latency system prompt guardrails.
**Testimonial Targets**:
- Chief Compliance Officer: Highlights the relief of presenting cryptographic proof of data removal to regulators instead of relying on easily bypassed prompt guardrails.
- Lead Machine Learning Engineer: Validates that the selective weight dampening completely excised target PII without causing catastrophic forgetting in unrelated domains.
- VP of Engineering: Praises the reduction in inference latency achieved by mathematically removing knowledge from the weights, allowing them to strip away heavy output guardrails.

## Startup Top Risks

**Risks**:
- Severity: existential · Description: Direct weight ablation causes catastrophic forgetting in adjacent knowledge domains and renders the excised model unusable for general tasks. · Mitigation Status: in-progress
- Severity: high · Description: Major foundational model providers release native data unlearning APIs that eliminate the market need for a third-party weight excision tool. · Mitigation Status: unmitigated
- Severity: moderate · Description: Generating cryptographic proofs of data ablation incurs high computational costs that deter adoption among budget-constrained enterprise customers. · Mitigation Status: in-progress
- Severity: low · Description: Standardization of LoRA-based masking in open-source libraries creates confusion around the distinct performance benefits of direct weight modification. · Mitigation Status: mitigated

## Startup Competitors

- [Full Model Retraining](/Competitors/Full_Model_Retraining) — Status Quo
- [Prompt-Level Guardrails](/Competitors/Prompt-Level_Guardrails) — Inference Workaround
- [LoRA Adapter Masking](/Competitors/LoRA_Adapter_Masking) — Parameter Tuning
- [In-Context Unlearning](/Competitors/In-Context_Unlearning) — Prompt Engineering
- [Dataset Pre-Filtering](/Competitors/Dataset_Pre-Filtering) — Data Management

## Startup Solution Stack

- [Model Compliance Service](/Services/Model_Compliance_Service) — Service-as-Software
- [Instance Localization Agent](/Agents/Instance_Localization_Agent) — Agent
- [Cryptographic Verification Worker](/Agents/Cryptographic_Verification_Worker) — Agent
- [Verified Ablation Engine](/Software/Verified_Ablation_Engine) — Software
- [Weight Modification API](/Software/Weight_Modification_API) — Software

## Startup Story Brand

**Hero**:
- **Need**: one of the few researchers who guarantees total model unlearning for legal audits
- **Want**: to remove specific PII or copyrighted data from LLM weights
- **Identity**: the head of AI safety at an enterprise foundation lab
**Plan**:
- Step: Identify · Detail: Specify the exact training instances, PII, or copyrighted passages requiring immediate model unlearning.
- Step: Verify · Detail: Review the activation pathway maps to ensure the excision is isolated to the target data distribution.
- Step: Deploy · Detail: Execute the weight excision and receive a cryptographic hash of the ablation delta for legal proof.
**Guide**:
- **Empathy**: Does your compliance process still rely on prompt-level filters that increase latency and fail under pressure?
**Problem**:
- **Villain**: unreliable guardrails
- **External**: Ablating copyrighted training data from a 70B Llama-3 model requires full retraining cycles costing millions in GPU hours.
- **Internal**: You feel exposed knowing that system prompts and filters are just fragile jailbreak targets.
- **Philosophical**: Expertise belongs in refining model capabilities, not in repeating trillion-token training runs for minor data removals.
**Success**: Targeted data is mathematically removed from the model distribution while retaining 98% of core benchmark performance.
**One Liner**: What if your model could actually forget? Forgetune excises targeted training instances directly from model weights, ensuring cryptographic unlearning without retraining.
**Positioning**:
- **So That**: mathematically excise specific data without incurring full retraining costs or latency
- **Unlike**: Full model retraining or prompt guardrails
- **For Whom**: Enterprise AI safety and compliance leads
- **Category**: Model unlearning and ablation services
**Call To Action**:
- **Direct**: Execute an ablation
- **Transitional**: Download sample cryptographic report
**Failure Stakes**:
- Catastrophic retraining costs
- Legal audits finding residual PII
- Inference-time latency spikes
**Transformation**:
- **To**: one of the few ML leads who provides cryptographic unlearning
- **From**: the researcher trapped in endless Llama retraining cycles
**Controlling Idea**: Weight excision is the only way to achieve true AI data compliance.

## Startup Token Hero

**Genre**: founding-hypothesis
**Rendered**: What if your model could actually forget? Forgetune excises targeted training instances directly from model weights, ensuring cryptographic unlearning without retraining.
**Mechanism**: spine-derived-v1
**Template Id**: spine-founding-hypothesis
**Vocab Fingerprint**: 21c2ce79b753ad8b

## Startup Token Positioning

**Genre**: moore-positioning
**Rendered**: Model unlearning and ablation services for Enterprise AI safety and compliance leads. Unlike Full model retraining or prompt guardrails — mathematically excise specific data without incurring full retraining costs or latency.
**Mechanism**: spine-derived-v1
**Template Id**: spine-moore-positioning
**Vocab Fingerprint**: 87ce0a13fd9901b5

## Startup Token Pitch Deck

**Genre**: pitch-deck
**Rendered**: Problem: Ablating copyrighted training data from a 70B Llama-3 model requires full retraining cycles costing millions in GPU hours.
Solution: What if your model could actually forget? Forgetune excises targeted training instances directly from model weights, ensuring cryptographic unlearning without retraining.
Customer: Enterprise AI safety and compliance leads
Unlike: Full model retraining or prompt guardrails
**Mechanism**: spine-derived-v1
**Template Id**: spine-pitch-deck
**Vocab Fingerprint**: cd581dd95fbed864

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

**Pain**: Ablating copyrighted training data from a 70B Llama-3 model requires full retraining cycles costing millions in GPU hours.
**Metrics**: Target: Targeted data is mathematically removed from the model distribution while retaining 98% of core benchmark performance.
**Rendered**: Pain: Ablating copyrighted training data from a 70B Llama-3 model requires full retraining cycles costing millions in GPU hours.
Economic buyer: Enterprise ML Operations / Legal Teams
Metrics: Target: Targeted data is mathematically removed from the model distribution while retaining 98% of core benchmark performance.
Competition: Full model retraining or prompt guardrails
**Mechanism**: spine-derived-v1
**Competition**: Full model retraining or prompt guardrails
**Economic Buyer**: Enterprise ML Operations / Legal Teams
**Vocab Fingerprint**: a48bd395b28fb9c7

## Startup Token Cold Email

**Genre**: cold-email
**Rendered**: Subject: Model unlearning and ablation services for Enterprise AI safety and compliance leads

Enterprise AI safety and compliance leads — Ablating copyrighted training data from a 70B Llama-3 model requires full retraining cycles costing millions in GPU hours. What if your model could actually forget? Forgetune excises targeted training instances directly from model weights, ensuring cryptographic unlearning without retraining.
**Mechanism**: spine-derived-v1
**Template Id**: spine-cold-email
**Vocab Fingerprint**: 73f95df007e382d0

## Startup Token Agent Spec

**Genre**: ai-agent-spec
**Rendered**: Model unlearning and ablation services. What if your model could actually forget? Forgetune excises targeted training instances directly from model weights, ensuring cryptographic unlearning without retraining. Serves Enterprise AI safety and compliance leads.
**Mechanism**: spine-derived-v1
**Template Id**: spine-ai-agent-spec
**Vocab Fingerprint**: c368a1efb891af14

## Neighborhood

### Candidate solutions

- [Source Heavy Plate Welders](/Problems/Source_Heavy_Plate_Welders) — candidate solution for · Problems

### What it offers

- [Verified Ablation Engine](/Software/Verified_Ablation_Engine) — offers · Software

### Composed of

- [Instance Localization Agent](/Agents/Instance_Localization_Agent) — composes · Agents
- [Model Compliance Service](/Services/Model_Compliance_Service) — composes · Services
- [Cryptographic Verification Worker](/Agents/Cryptographic_Verification_Worker) — composes · Agents
- [Weight Modification API](/Software/Weight_Modification_API) — composes · Software

### Embodies

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

### Competitors

- [Full Model Retraining](/Competitors/Full_Model_Retraining) — competes with · Competitors
- [Prompt-Level Guardrails](/Competitors/Prompt-Level_Guardrails) — competes with · Competitors
- [LoRA Adapter Masking](/Competitors/LoRA_Adapter_Masking) — competes with · Competitors
- [In-Context Unlearning](/Competitors/In-Context_Unlearning) — competes with · Competitors
- [Dataset Pre-Filtering](/Competitors/Dataset_Pre-Filtering) — competes with · Competitors

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