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Case Study: Email Deep Research Agent
We trained an email agent using our agent reinforcement trainer (ART) that finds and answers deep-research-type questions within your inbox. We achieved SOTA with a small Qwen 2.5 14B model, which also comes with lower latency, lower cost, and the ability to deploy on-prem.
Learn more about how we did this in our blog post highlighting ART, our industry-leading RL framework.
Email Agent Success Rate
What We Do
OpenPipe's post-training platform makes it easy to get product-defining results through SFT and reinforcement learning. We pair RL experts with your team to quickly identify the highest‑impact use cases to meet your business goals. Within a few weeks, you'll see side‑by‑side evals that quantify how RL‑trained agents outperform standard implementations on your own metrics of quality, compliance, and cost.
Our key technology is the open-source agent reinforcement trainer (ART), our industry-leading RL framework.
Key Features
Evaluate, fine-tune, and serve LLMs with a seamless developer experience.
Continuous RL Optimization
GRPO‑powered feedback loops keep your models learning from fresh production data so accuracy improves every release—no rebuilds required.
On‑Prem & VPC Deployment
Run the full OpenPipe stack inside your private cloud or data center; zero customer data or model weights ever leave your network.
Regulatory Compliance & Governance
SOC 2 Type II, HIPAA, and GDPR support, plus role‑based access controls and immutable audit logs, satisfy the strictest InfoSec reviews.
Dedicated Support & Contractual SLAs
Named solution architects, SLAs, and roadmap influence are written directly into your enterprise agreement.
Predictable Enterprise Economics
Up to 8× lower inference cost than GPT‑4‑class APIs, with volume discounts and optional fixed‑fee tiers for budget certainty.
Unified Observability & Evaluation Hub
Live dashboards, automated guardrails, and approval workflows make it easy to prove alignment and catch regressions before they reach production.