Envive post-trains open-weight models into a dedicated model for your business, graded on your outcomes before every release.
Envive post-trains open-weight models into a dedicated model for your business, optimized towards your business goals.
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in new annual revenue. Conversion up 11.5%, about 5,950 extra orders a month, order value held, no added marketing spend.
Read the story →
“One of the rare tools that actually delivers speed, trust and results: quick to train, compliant on claims, and drives measurable performance lift.”
0 compliance violations across {{n3}},000+ sessions in a regulated category.
return on spend. Conversion more than doubled; Envive went from 15% of Black Friday traffic to 90% by Cyber Monday.
Read the story →“Envive has been a game-changer for us. Their safe and effective AI significantly boosted our site conversions and truly brought our brand to life.”
more searches ending in a checkout: 9% of searches now convert, up from 5.3% with the previous provider.
Read the story →The post-training platform for business outcomes.
Simulation, evals and training in one loop, on infrastructure built for open-weight models.
Explore the Training Stack →Your data, policies and voice, scored for quality before training starts.
Personas with a goal and no answer key, so the model has to earn the outcome in a sandbox.
Fine-tuning, preference optimization and RL on an open-weight base, with rollouts and training running asynchronously on dedicated GPUs.
The same agent, tools and environment in training and production, so new weights deploy straight to serving.
Your business outcomes become test cases. Every release has to beat the last one.
Explore Evals →Code checks, rubrics and an LLM judge score accuracy, voice, policy and whether the outcome happened.
Every tool call, lookup and decision behind a failed case, in one view.
A/B test a candidate against the current model on live traffic before full rollout.
Every reported issue becomes a new eval case, so the next run starts from a higher bar.
Served fast, connected to your systems, and watched after launch.
Explore Deploy →High-throughput inference for a compact model, with low latency where customers notice it.
Your systems exposed to the model as MCP tools, the same ones it trained with.
Brand, accuracy and policy rules trained into the weights, with deterministic fallbacks where you need certainty.
Live conversations graded by the same judge, so drift is caught before customers report it.
Built for regulated categories, where a wrong claim is a liability. Every control below is enforced in the model and checked in the evals.
Facts are graded against your own data. An unsupported claim fails the case, and the trace shows why.
Brand, policy and compliance rules are trained in, with deterministic responses where you need certainty.
Across 56,000+ conversations in a regulated category. Every one graded by the same judge the model was trained against.
One model per business. Nothing you share is used to train anyone else’s.
Tell us a little about your business and we’ll walk you through a model trained on your data, your policies and your goals.
Someone from Envive will reach out within one business day to set up your walkthrough.
