Correctness is contextual
The same response or action can be right, wrong, or incomplete depending on customer, product, policy, setting, evidence, risk, or intended use.
Solutions
Correctness infrastructure for high-judgment domains
Where correctness is domain-specific, your benchmark must be too.
The closer AI moves to consequential work, the more its behavior must reflect the standards, exceptions, and judgment held inside your organization. Teammately connects those experts to the systems your AI team is engineering.
Starts inside your teamDomain judgment and operating context
Moves into the buildExecutable benchmarks and evidence
The common problem
Open-ended AI meets standards that were never written for machines.
The standard lives inside the organization
Your strongest definition of quality is distributed across specialists, internal materials, prior decisions, exceptions, and operating practice.
The target keeps moving
Products, policies, models, tools, environments, and expert understanding change. The benchmark must evolve without losing provenance.
By domain
Start with the experts already accountable for quality.
01 · Customer · merchandising · operations
Retail
Make brand, policy, commercial, and operating judgment testable across assistants, copilots, content systems, and agents.
Explore Retail02 · Voice · content · experience · governanceBrands
Turn brand voice, creative judgment, claims guidance, and customer experience standards into behavioral benchmarks that scale.
Explore Brands03 · Risk · compliance · advisory · operationsFinancial Services
Evaluate contextual behavior with the standards, evidence, uncertainty, and escalation boundaries your specialists require.
Explore Financial Services04 · Underwriting · claims · service · complianceInsurance
Benchmark policy interpretation, evidence, customer communication, and escalation across underwriting, claims, and service workflows.
Explore Insurance05 · Clinical · scientific · quality · operationsHealthcare
Design behavioral benchmarks around specialist intent, evidence conditions, uncertainty, appropriate scope, and accountable escalation.
Explore Healthcare06 · Guest experience · disruption · operationsAirlines
Ground airline assistants and agents in fare rules, service recovery, operational constraints, and real-time disruption context.
Explore Airlines07 · Discovery · planning · booking · serviceTravel
Benchmark discovery, itinerary, booking, and service behavior against traveler intent, product truth, and operational reality.
Explore Travel08 · Planning · exceptions · dispatch · serviceLogistics
Turn planning, dispatch, exception, and customer-commitment judgment into benchmarks for logistics copilots and agents.
Explore Logistics09 · Engineering · quality · service · mobilityAutomotive
Benchmark engineering copilots, quality systems, service assistants, and in-vehicle experiences against specialist standards.
Explore Automotive10 · Engineering · production · maintenance · safetyIndustrial
Test engineering, production, maintenance, and field-service AI against local operating knowledge and safety boundaries.
Explore Industrial11 · Product · support · success · operationsEnterprise SaaS
Benchmark product copilots, support agents, implementation assistants, and operational automation across complex customer environments.
Explore Enterprise SaaS12 · Marketplace · safety · support · operationsRide sharing & Food delivery
Benchmark marketplace, safety, support, and operational AI across riders, drivers, couriers, merchants, and changing local conditions.
Explore Ride sharing & Food delivery13 · Network · service · field · commercialTelecommunications
Benchmark network copilots, service agents, field assistance, and commercial AI against technical and customer standards.
Explore TelecommunicationsOne platform pattern
Carry the same domain standard from benchmark design into improvement.
Coverage Engineering
Design what the benchmark must represent across the dimensions, conditions, risks, and exceptions that matter in your domain.
Correctness Elicitation
Turn specialist decisions into policies, applicability conditions, escalation rules, and binary rubrics.
Weave
Create the targeted cases, variants, multimodal artifacts, and worlds the coverage plan requires.
Trialground
Run candidate models and agents in controlled environments and preserve inspectable behavior-level evidence.
Coevolve
Explore parallel improvement paths and return newly discovered coverage or correctness gaps to the right experts.
When Teammately fits
Your hardest AI problem is defining and carrying forward the right behavior.
01
You are building a specialist copilot or agent whose mistakes depend on domain context—not only factuality.
02
Internal experts can recognize good behavior, but their judgment is too scarce to become an annotation operation.
03
Generic LLM judges are useful for a quick start but cannot represent your policies, exceptions, and priorities.
04
You need one benchmark to guide model, prompt, tool, harness, and agent changes across development.
05
A single score is insufficient; teams need the cases, trajectories, rubrics, and decisions behind it.
06
New failures reveal missing standards, and you need a controlled path to bring those questions back to specialists.
Begin with one behavior your generic evals cannot specify.
Bring the workflow, the specialists who understand it, and the evidence your team already has. We’ll map the coverage, correctness, and development loop around them.