Correctness changes with context
The right behavior depends on asset and process state, site and configuration, signals and evidence, hazards, limits, and escalation. Generic scoring misses those interactions.
Solutions · Industrial
Industrial AI that reflects how the operation actually works
Turn engineering and frontline judgment into benchmarks for consequential industrial behavior.
Industrial correctness is local, physical, and constraint-heavy. Teammately helps teams capture the procedures, exceptions, signals, and safety boundaries that distinguish useful assistance from risky automation.
The domain reality
Generic quality is not enough for consequential industrial behavior.
The standard lives across specialist teams
Process engineering, Production operations, Maintenance and reliability, Safety and quality each hold part of the judgment the system needs to behave well.
Real work is full of exceptions
A benchmark must represent edge cases, uncertainty, conflicting goals, and escalation—not only the most common industrial path.
Priority workflows
Start where expert judgment materially changes the answer.
Engineering copilots
Benchmark technical synthesis and recommendations against requirements and constraints.
Maintenance assistants
Test diagnosis, evidence requests, procedural fit, and safe escalation.
Operations agents
Evaluate responses to changing conditions, deviations, and handoffs.
Quality investigation
Assess causal reasoning, traceability, and corrective-action support.
Correctness blueprint
Connect specialist judgment to the benchmark and the build.
Your experts define
- Process engineering
- Production operations
- Maintenance and reliability
- Safety and quality
The benchmark covers
- Asset and process state
- Site and configuration
- Signals and evidence
- Hazards, limits, and escalation
Your AI team receives
- A deliberate coverage map
- Explicit policies and binary rubrics
- Targeted cases, variants, and exceptions
- Inspectable evaluation and improvement evidence
One connected workflow
Five product capabilities, applied to one domain standard.
Coverage Engineering
Design the combinations of asset and process state, site and configuration, signals and evidence, hazards, limits, and escalation the benchmark must represent.
Correctness Elicitation
Turn judgment from process engineering, production operations, maintenance and reliability, safety and quality into policies, applicability conditions, and binary rubrics.
Weave
Create targeted industrial cases, variants, artifacts, and worlds from the coverage plan.
Trialground
Run candidate models and agents in controlled environments and preserve the behavior-level evidence.
Coevolve
Explore parallel improvement directions and return newly discovered gaps to the right specialists.
What good looks like
A development team that can improve behavior without losing domain intent.
Coverage you can defend
Know which industrial conditions, exceptions, and risks the benchmark represents—and which it does not.
Judgment that scales
Reuse every specialist decision across policies, rubrics, evaluation, and future cases.
Evidence for every iteration
Compare model, prompt, harness, and agent changes against the same domain-grounded standard.
A controlled learning loop
Route unresolved questions and newly discovered gaps back to accountable experts.
Teammately supports industrial AI evaluation; control actions, safety procedures, and regulated decisions stay with validated systems and accountable personnel.
Build a benchmark around how your industrial experts actually judge quality.
Start with a consequential workflow and the specialists already accountable for it. Teammately turns their judgment into reusable development infrastructure.