Vernex AI Platform

Turn expert knowledge and real-world contexts into verifiable model capabilities

We connect high-quality data development, benchmark research, model optimization, and deployment for foundation models, agents, embodied AI, and AI for science.

Build a continuous R&D loop across data, evaluation, optimization, and application, starting from capability goals.

Explore data assetsView four collaboration paths

Capability loop

From knowledge to capability

Continuous
  1. 01Expert knowledge
  2. 02High-quality data
  3. 03Trusted evaluation
  4. 04Model & scenario capability

Data, evaluation, and delivery form one repeatable R&D system.

Deliver around model capability goals

Experts design tasks, data engineers manage production, and evaluations measure model performance.

01

High-quality data

Expert-authored tasks, real-world collection, multimodal processing, rubrics, and challenging examples.

02

Benchmark evaluation

Capability trees, task sets, executable environments, verifiers, and judges for reliable evaluation.

03

Expert network

Discipline and industry coverage supported by matching, calibration, organization, and quality operations.

04

Models & algorithms

Technical support for training plans, SFT and RL, reward modeling, and error analysis.

05

Research & innovation

Explore research questions and tools across RSI, AI4AI, and AI4S.

06

Scenario deployment

Connect enterprises, research institutions, and technology partners to launch and operate projects.

High-quality data and benchmarks

Build useful training signals and dynamic capability validation that fit model development workflows.

High-quality data engine

  1. 01Capability goals
  2. 02Expert task design & collection
  3. 03Rubrics & annotation
  4. 04Model stress tests
  5. 05Layered quality review
  6. 06Training & evaluation assets

Benchmark evaluation engine

  1. 01Capability definition
  2. 02Task scenarios
  3. 03Environment verifiers
  4. 04Rubric scoring
  5. 05Failure analysis
  6. 06Challenge feedback

Representative products

PhD-level STEM reasoning data & rubrics

Advanced math and physics tasks for process scoring, model stress tests, and fine-grained error analysis.

OpenClaw / Hermes agent data

SFT and RL trajectories preserving multi-turn interaction, tool calls, environment states, and deliverables.

SWE-bench Multilingual

Real software engineering tasks across languages with runnable environments, test patches, and agent traces.

Terminal-Bench & medical evaluation

Terminal task execution benchmarks and professional evaluation tasks in obstetrics and medical safety.

Organize a steady supply of specialized knowledge

Match, calibrate, and manage experts by discipline, experience, and project requirements.

500+

Active experts

60%

Master’s, PhD, and senior-title share

1–2 weeks

Expert matching and team setup

500+

Peak experts on a single project

  1. 01Recruitment
  2. 02Capability profiles
  3. 03Screening
  4. 04Guideline training
  5. 05Production
  6. 06Quality ratings
  7. 07Incentives & retention

Connect R&D capabilities with industry needs

Organize technical and ecosystem resources from algorithm validation to agent engineering and deployment.

Technical services

  • Algorithm design & research engineering
  • Model training & optimization
  • Agent & benchmark engineering
  • Data & model platform development

Industry scenarios

  • Healthcare & pharma
  • Laboratories & AI for science
  • Smart education
  • Embodied AI
  • Technology talent & industry collaboration

Research in progress

RSI

Study models that discover capability boundaries, generate challenges, and improve from feedback.

AI4AI

Explore AI-assisted experiment design, pattern discovery, and algorithm optimization.

AI4S

Explore accelerated computing and bioinformatics foundation models.

AI-native talent platform

Explore AI and agents for technology talent assessment and precision matching.

Track delivery from requirements to acceptance

Use aligned guidelines, layered reviews, and issue feedback to balance task rigor and delivery scale.

  1. 01

    Requirements & solution

  2. 02

    Guidelines & pilot

  3. 03

    Resources & production

  4. 04

    Quality loop

  5. 05

    Acceptance & learning

Quality practices

  • Guidelines first
  • Layered review
  • Model-assisted checks
  • Traceable workflow
  • Issue feedback

Safety practices

  • Tiered access
  • Data de-identification

4 collaboration paths

Start with a real problem

Shape collaboration across data, evaluation, model technology, joint R&D, and industry scenarios.

01

Data & benchmarks

High-quality data, agent trajectories, advanced reasoning, expert review, and executable benchmarks.

02

Models & technical services

Model optimization, agent development, algorithm reproduction, and research engineering.

03

Joint R&D & product building

RSI, AI4AI, AI4S, frontier evaluations, research programs, and shared tools.

04

Industry & ecosystem collaboration

Solution and project collaboration across healthcare, education, embodied AI, and technology ecosystems.

Build long-term collaboration around a real problem

A complete plan is not required. Start with a concrete task and work together on problem framing, resource matching, and a collaboration model.

Explore data assets