Vision
The Market for Intelligence
Fine-tuning is the industry's answer to "make AI work for me." It's the wrong answer. The right answer is owning what your AI knows, who it knows it for, and when it applies.
The Problem
Fine-Tuning Is Not the Answer
The AI industry's playbook for customization is to pour your data into model weights and hope it learns. This is wrong in three ways.
You lose control
Fine-tuning bakes your data into model weights. You can't scope it, version it, revoke it, or audit it. Once it's in the model, it's the model vendor's asset, not yours.
Everything all the time
The current paradigm is 'give the AI access to everything and let it figure out relevance.' No scoping. No access control. No awareness that the intern and the CISO should see different things.
Expensive and fragile
Fine-tuning costs thousands per run, takes hours, and breaks with every model update. You have to do it again for every new model. It's technical debt disguised as customization.
The Alternative
Own What Your AI Knows
Instead of fine-tuning models, own the intelligence layer above the model. Rules, skills, memories, context: these are your assets. They're portable across models. They're scoped to the right people. They're cryptographically yours.
When the model changes, your intelligence doesn't. When the vendor gets acquired, your knowledge doesn't go with them. When you switch from GPT to Claude to Llama, your rules still enforce, your skills still teach, and your memories still remember.
This is the difference between renting intelligence and owning it. Fine-tuning is rent. Anneal is ownership.
Market Structure
Five Ways Intelligence Flows
When intelligence is an owned, portable asset, it creates market relationships that nobody in AI has built yet.
Individual
Your AI, free and open. Personal intelligence that grows with you. The adoption funnel: users become advocates inside organizations.
Enterprise
Institutional intelligence with compliance, team memory, and scope enforcement. Annual contracts. Per-seat licensing that scales across the whole organization, not just engineering.
Platform
IDEs, inference providers, and SaaS tools embed the intelligence layer. Their developers get Anneal built in. Runtime licensing that turns every platform into a distribution channel.
Marketplace
Developers build specialized intelligence packs: HIPAA-aware rules for healthcare, SOC2 compliance skills for finance, domain-specific memories for any vertical.
Network
An individual's AI profile becomes valuable to the businesses they interact with. A consultant walks into a company and brings their AI with them. Personal context enriches every business interaction.
Network Effects
The Flywheel
These five relationships create a network effect that compounds.
B2C adoption creates D2B creators. Individual users learn the platform, build expertise, and start packaging their knowledge as intelligence packs for businesses.
D2B creators attract B2B enterprises. A marketplace of domain-specific intelligence makes Anneal the obvious choice for organizations that need compliance, vertical expertise, or institutional memory out of the box.
B2B enterprises drive B2D platform adoption. When enterprises standardize on Anneal, the platforms those enterprises use have incentive to embed the intelligence layer natively.
C2B makes every user a channel. When individuals bring their AI to every business they interact with, the intelligence layer spreads through professional networks, not marketing campaigns.
This is the market for intelligence. Not fine-tuning. Not prompt engineering. Owned, portable, scoped intelligence that flows between individuals, developers, and businesses.