Over the past two years this has hardened into a playbook: an open-source model,
proprietary task data, and a reinforcement-learning stage against a scored version of
the workflow. Below, we discuss three scenarios where this approach has been applied to real-world tasks.
**Bridgewater Associates** is one of the largest hedge funds in the world. Its
analysts sift a constant stream of articles, filings, and emails, judging which
documents are relevant to the firm's investment thesis and where boilerplate
content begins. The catch is that *relevant* means relevant by Bridgewater's internal
judgment, and no amount of prompting got frontier models to absorb that judgment
reliably. So, the company decided to train an open-source
model on labels from its own expert investors. The trained model
makes
roughly 30% fewer mistakes than the best frontier model, at a fraction of the
inference cost.
**Harvey** builds AI agents for law firms. Its hardest workloads are
long-horizon: transaction due diligence and legal memo drafting, where the agent
navigates large document sets, errors compound across steps, and even the best
frontier models at maximum reasoning effort kept falling short of the quality bar.
Harvey ran reinforcement learning on an open-weight model and got
a
legal agent that outperforms both GPT-5.5 and Claude Opus 4.8 on its rubrics.
**Intercom** is a customer-service platform whose AI agent, Fin, resolves
almost two million customer issues a week. At that volume the problem is unit
economics: frontier per-call pricing adds up fast, and every point of resolution rate
matters. So Intercom's AI group post-trained its own vertical support model, Fin Apex,
on billions of customer-service interactions. Intercom reports that it
resolves
more issues than the best frontier models while being cheaper to run.
The same shape repeats well beyond these three. The
appendix collects eight more deployments, with what each
model was trained to do and what changed once it shipped.