AI infrastructure studio
The production layerfor AI intelligence
Training data · RL environments · Evaluations · Expert workflows · Applied AI research
We build the data, environments and human feedback systems that help AI labs train and evaluate models for real-world work.
Infrastructure stack
Infrastructure for the next generation of AI
AI models are becoming increasingly capable. The bottleneck is increasingly high-quality training and evaluation infrastructure.
- 01
RL environments
High-fidelity environments where agents interact with software, tools and realistic workflows
- 02
Training data
Synthetic, expert-verified datasets for SFT, preference optimization and post-training
- 03
Agentic tasks
Long-horizon, multi-step tasks designed around real-world objectives
- 04
Evaluations
Capability, reliability and task-completion benchmarks around measurable outcomes
- 05
Expert data
Domain-expert demonstrations, verification, rankings and feedback
- 06
Applied research
End-to-end execution of data and evaluation programs around model capabilities
From research specification to production-ready AI infrastructure
Reinforcement-learning environments
Where models practise real workbefore they do it in the world
Node-Zero builds high-fidelity environments for computer use, tool use and agentic AI systems.
Environment types
Computer-use RL · Coding RL · Tool-use RL · Multi-app RL · Multi-agent RL · Gaming RL · Long-horizon RL · Simulation and physical RL
Enterprise and productivity
Sales CRM; IT ticketing; support desks; contract management; finance systems; project management; HR workflows; team chat; cloud storage; email; calendars; task management; spreadsheets
Coding and software
Software engineering; cybersecurity; ML engineering; SRE; DevOps; network engineering; code review; debugging; repository-level tasks; systems and infrastructure; CAD; cloud infrastructure; data platforms
Business and professional
Finance; accounting; investment research; operations; supply chain; sales; marketing; customer support; HR; compliance; legal research; contract analysis; audit; tax; risk; real estate
Healthcare and physical AI
Clinical workflows; medical research; documentation; drug discovery; genomics; literature synthesis; laboratory workflows; simulation; spatial reasoning; robotics; teleoperation; egocentric perception; gameplay; physical interaction; sim-to-real
From a single application to a full multi-app workspace
Production, QA and delivery
Built, populated,verified, shipped
How every Node-Zero environment is produced, whatever the domain.
- 01
Environment design model
State → Tools → Actions → Constraints → Failure modes → Outcomes
- 02
Synthetic environment data
Synthetically generated, realistic data records, users, histories, relationships, edge cases and failure states modeled on real-world systems. Shipped populated with every environment, or delivered standalone as training and evaluation data.
- 03
Persona-consistent worlds
Environments are populated around a consistent synthetic persona, generated on demand to your specification: any role, domain or seniority. One practitioner’s mailbox, spreadsheets, drive, calendar and account history cohere across every application in the workflow: the same names, threads, files and backlog an agent would meet in a real working life, rather than unrelated data per app.
- 04
Quality assurance
Every environment is thoroughly quality checked by specialised QA engineers and QC orchestrators before delivery, covering functional validation, data realism, task solvability, evaluation correctness and failure-mode coverage.
- 05
MCP interfaces, on request
- Add-on: any environment can be exposed as an MCP server, built to your specification
- Interface: tools, resources and prompts callable by any MCP-compatible agent or harness
- Scope: one server per application, or a single server fronting an entire multi-app workspace
- Control: per-tool permissions, authentication and rate limits, with every call logged for trajectory capture
- 06
Deliverables
Environment source and deployment; seed data and personas; task suites; evaluation harness and rubrics; documentation and handover
Environment design follows the shape of the job, not merely what is easiest to automate or verify.
Service guarantee
All RL environment deliveries include a 1-year commitment to resolve any reported bugs or performance issues.
Agentic tasks and training data
Real tasks. Real workflows.Real execution
Task categories
- 01
Computer use
Navigate apps, operate interfaces and complete multi-step workflows
- 02
Coding
Repository-level engineering, debugging, testing, optimization and long-horizon development
- 03
Research
Search, synthesis, reasoning across sources and structured deliverables
- 04
Enterprise workflows
Work spanning multiple applications, tools, documents and decisions
- 05
Reasoning
Planning, verification and multi-step execution
- 06
Multimodal
Text, images, interfaces, documents and structured data
Task types
Single-step · multi-step · long-horizon · multi-app · tool-use · open-ended · deliverable-graded · expert-rubric verified
Deliverables
Specification
Task specifications; task instances
Execution
Demonstrations; agent trajectories; tool-use traces
Training signal
Preference pairs; expert annotations; verification datasets; reward-modeling data
Continuous provenance trace
Controlled world
Expert or agent work
Verification
Packaged intelligence
All data is synthetic or produced by Node-Zero experts inside controlled environments. Personas reflect patterns of work, never real individuals. Node-Zero does not scrape, purchase or re-license third-party datasets and handles no real personal, patient or customer data.
Realistic by design, clean by construction
Human expertise
Expert intelligence,on demand
Some AI capabilities require people who understand the work. Generalist annotation cannot supply the same judgment.
Expert domains
Software engineering
Engineers, ML, DevOps, security, systems
Finance
Analysis, investment, accounting, tax
Healthcare
Practitioners, clinical research, specialists
Legal
Lawyers, compliance, legal research
Science and research
Researchers, scientists, specialists
Business operations
Operations, supply chain, product, strategy
What experts do
Produce
Create tasks, execute workflows and generate demonstrations
Judge
Rank model outputs, verify deliverables and provide preference feedback
Define
Write evaluation rubrics and identify failure modes
Human expertise where model evaluation demands it
Expert work becomes usable intelligence through controlled tasks, explicit rubrics and verified outcomes.
Evaluation systems
Evals that measureactual capability
Benchmarks can reveal whether a model knows something. Real-world evaluations reveal whether it can do something.
Evaluation dimensions
- 01Capability
- 02Reliability
- 03Completeness
- 04Accuracy
- 05Tool use
- 06Long-horizon performance
- 07Expert quality
Evaluation formats
- Automated
- Human
- Expert-rubric
- Outcome-based
- Trajectory
- Held-out
Capability development loop
Baseline
Training
Evaluation
Measured improvement
Measurement stays anchored to observable completion, quality and reliability rather than proxy activity.
Production pipeline
From specificationto validated intelligence
A repeatable production pipeline for AI capability development.
- 01
Specification
Customer defines capability or research objective.
- 02
Task design
Node-Zero converts the objective into measurable tasks and workflows.
- 03
Environment
Build application, tool or simulation environment.
- 04
Execution
Agents, engineers and domain experts execute workflows.
- 05
Verification
Outputs reviewed against automated and expert criteria.
- 06
Dataset
Validated trajectories, demonstrations, preferences and evaluations packaged for training or benchmarking.
- 07
Evaluation
Model performance measured against held-out tasks.
- 08
Iteration
Failure modes become new tasks, new data and new evaluation cases.
Each verified failure becomes material for the next production cycle
Frontier delivery
We have already shippedat the frontier
Our founding team has designed, built and delivered RL gyms end-to-end for programs serving the world’s leading AI labs.
Delivered end-to-end
Computer-use, coding, multi-app and domain-specific environments scoped, architected, built, populated with synthetic data, quality-checked and shipped.
Have a sneak peek through our deliverables
node-zero.in/products→Who you work with
D. Kushal Kumar Reddy
Founder
Capacity
Built before.Ready at volume.
The same engineers and the same production standard, scoped to your programme.
Our founding engineering team has served teams at
We have built these before. That is why we can build them at volume.
Partner with Node-Zero Labs
Build better AI
We build what models need to learn, practise and improve.
Bring us your client’s requirements. We’ll return with a quote, a delivery plan and an exact list of deliverables, scoped to your volume, domains and deadline, so you can commit with confidence.
For AI labs and AI infrastructure companies
Training data · RL environments · Agentic tasks · Evaluations · Expert workflows · Applied AI research