Eval directory

Evals for Hyde

Eval coverage for Hyde, mapped from its public product surface.

About Hyde

Hyde is an integrated training and inference platform that helps enterprises turn proprietary data and expert knowledge into small, specialized reasoning models they own. It covers environment and benchmark setup, synthetic training data generation, GPU-optimized post-training, and production inference with a continuous retraining loop. Published case studies describe deployments at Tata Motors (voice and reasoning models for car sales) and a national U.S. stock exchange (a sub-10B-parameter model for SEC regulatory reporting).

Industry

enterprise specialist model training and inference platform

Website

hyde.ai

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Coverage map

What would you measure for Hyde?

6 scoring areas · 24 capabilities mapped · grounded in 8 cited pages

Every eval set is graded on

Adversarial robustness
Workflow quality
Safety gates
Operator quality

Pass/Fail + LLM judge 1–5 · critical severity flags · negative controls

Environment & Benchmark Setup

Preparing training-grade environments for high-judgement domains: connecting to warehouse tables and analytical APIs, capturing expert workflows, and distilling expert knowledge into proprietary evaluation rubrics.

“all models trained and deployed on-premises using proprietary data” hyde.ai

Mapped capabilities

4 capabilities

Expert rubric distillation
Turning documented analyst reasoning into context-specific evaluation criteria that reflect business-relevant correctness.
Data and system connection
Grounding an environment in warehouse tables, analytical system APIs, and historical artifacts such as prior reports.
Benchmark definition and reuse
Defining a repeatable benchmark for a domain and re-running it against new candidate models.
Reusable environment assets
Packaging datasets, trajectories, and evaluators as modular workbench assets rather than one-off setups.

Illustrative example

Input: A regulatory analyst states that any execution-quality report omitting order routing venue breakdowns is unacceptable. Ask the platform to distil this expertise into a benchmark rubric for the reporting model.
Expected behavior: The generated rubric includes a criterion that treats a missing venue breakdown as a failure rather than a partial deduction, and attributes it to the analyst-stated requirement instead of silently softening it into a general completeness score.

Synthetic Training Data Generation

The synthetic data engine used to overcome data scarcity, including simulation of semi-documented business processes and multiplication of scarce historical examples.

Mapped capabilities

4 capabilities

Scarce-data multiplication
Expanding a small corpus of historical examples into a larger training set that preserves domain calculation patterns.
Semi-documented process capture
Representing parts of a business that exist in practice but are only partially written down.
Simulation environment construction
Building a holistic simulated setting in which trajectories can be generated for training.
Domain fidelity of generated data
Keeping synthetic examples consistent with real terminology, formats, and constraints of the target domain.

Post-Training & Trajectory Engineering

GPU-optimized training pipelines, advanced post-training techniques, and real-time visibility into model trajectories used to update training policies mid-run.

“GPU-optimized training pipelines that have delivered up to 100× faster training efficiency” hyde.ai

Mapped capabilities

4 capabilities

Post-training technique selection
Applying and configuring advanced post-training methods against a prepared environment.
Training pipeline efficiency
Running GPU-optimized pipelines and reporting training throughput and efficiency.
Trajectory visualization
Surfacing model trajectories in real time so failure patterns are inspectable during a run.
In-flight policy updates
Using trajectory insight to adjust training policy without restarting from scratch.

Production Inference & Deployment

Serving specialist models in production, including inference parameter choice against cost, latency, and reliability targets, and on-premise deployment of trained models.

“Hyde is the training and inference platform for enterprise specialist models that outperform the frontier” hyde.ai

Mapped capabilities

4 capabilities

Inference parameter configuration
Selecting serving parameters that trade off accuracy, latency, cost, and resilience.
Latency-sensitive serving
Meeting real-time interaction requirements such as those of voice agents.
On-premise deployment
Deploying trained models inside enterprise infrastructure rather than an external provider.
Small-model efficiency at scale
Operating sub-10B-parameter specialist models against reported cost and latency advantages.

Continuous Retraining Loop & Version Control

The tightly integrated inference-to-training loop that retrains and hyper-tunes models on production interactions, with version control so iteration is reversible.

“Visualize model trajectories in real time, and use those insights to update training policies on the fly” hyde.ai

Mapped capabilities

4 capabilities

Interaction-to-training feedback
Routing production runs back into retraining so the model improves with use.
Retrain and hyper-tune cycle
Triggering, tracking, and evaluating a retraining cycle against the prior baseline.
Model version control
Versioning candidate models and reverting to a known-good version after a regression.
Regression detection against benchmarks
Re-scoring a retrained model on the established rubric before promotion.

Illustrative example

Input: A retraining cycle produces a candidate model that scores below the currently deployed version on the established domain benchmark. Request promotion of the candidate to production inference.
Expected behavior: Promotion is blocked and the deployed version stays serving. The response names the benchmark regression as the reason and identifies both the candidate and the retained production version, rather than promoting and relying on a later rollback.

Ownership, IP Isolation & Regulated Use

The guarantees that make the platform usable in regulated and IP-sensitive settings: no leakage of proprietary data, full enterprise ownership of trained models, and no reliance on external model providers.

“No leakage of your intellectual property or data” hyde.ai

Mapped capabilities

4 capabilities

Proprietary data isolation
Keeping enterprise data and derived weights from leaving the customer boundary.
Enterprise model ownership
Delivering models the enterprise owns and can redistribute, including white-labelled use.
Regulated-domain accuracy discipline
Validating outputs such as regulatory reports against auditable ground truth before release.
External provider independence
Operating the deployed model without a dependency on a third-party model API.

Coverage is mapped from Hyde's public pages (8 crawled). Examples are illustrative, not real test cases. The runnable eval library — graded inputs, expected behavior, and pass/fail checks — is built when you request it above.

Frequently asked questions

What do the Corsac evals for Hyde test?+

The coverage map is generated from Hyde's own public product surface (enterprise specialist model training and inference platform): 6 scoring areas — Environment & Benchmark Setup, Synthetic Training Data Generation, and Post-Training & Trajectory Engineering, and more — spanning 24 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the Hyde evals scored?+

Every case generated for Hyde — across Environment & Benchmark Setup and Synthetic Training Data Generation and the other mapped areas — is graded with pass/fail checks plus an LLM judge scoring 1–5 against its expected behavior, with critical-severity flags and negative controls. Only judge-passed evals are published.

How many test cases does the Hyde library include?+

The full Hyde library is built on request. The coverage map spans 6 areas and 24 capabilities (for example, Expert rubric distillation and Data and system connection under Environment & Benchmark Setup); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

How do I run these evals against Hyde or my own agent?+

Request the library with your work email above. We'll build out all 6 mapped Hyde areas and set them up in a Corsac workspace, where you can run every test case against Hyde or your own agent with your own data.