agent.train(
data=enterprise,
env=hyde)

Own your intelligence

Partnering with enterprises to turn their data and expertise into reasoning engines

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Hyde is the training and inference platform for enterprise specialist models that outperform the frontier

Extract deep institutional context from business operations and package it into custom AI models. Outperform the frontier with private data and integrated post-training and inference loops

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The next era of enterprise AI is small, owned, and highly specialized

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Superior performance

That improves with time.

Superior agent accuracy and contextual understanding

Defensible edge over generalist AI that improves with use

No leakage of your intellectual property or data

Superior scalability

That gets more efficient with time

Up to 10x lower inference cost and latency at scale

Up to 7x lower long term cost of ownership

Zero reliance risk on external model providers

Our partners are generating incredible results

Reasoning
Forecasting
Code
Browser
Voice
Vision

Tata Motors, a $50B global automaker, built specialist models to automate car sales

4x improvement
in sales conversion rate
World's best STT model
across Indic languages

“Hyde has been instrumental in shaping our vision within the customer domain... Their expertise and collaborative approach have been valuable in initiating our early steps in this journey.”

Rajesh Kannan
President & CIO, Tata Motors
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reasoning
voice

A national US stock exchange built a <10B parameter specialist model for regulatory reasoning and reporting

Approaching 100% accuracy
in generating SEC reports
$20M+ revenue opportunity
to distribute model as a white-labelled product

reasoning
code

$10B retailer built an agentic time-series model to improve demand forecast accuracy

>10pp improvement
in forecast accuracy vs traditional models across 20,000+ SKUs

forecasting
code

$2B CPG company built specialist models to surface sales opportunities and evaluate field performance

>5% revenue uplift
in pilot markets

reasoning
voice

Built an AI F1 race engineer to reason across terabytes of sensor data from simulator sessions and races

~$1-3M
in direct operational efficiency
~$7-10M
in indirect upside

reasoning
code

Encoded RL environments to understand and run calculations for a complex prop-trading pricing library

<1 week
to build codegen agents
10x better performance
than OTS context-based coding assistants

reasoning
code

Built by the top minds in AI

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