Platform capability · Benchmark representation across ontologies
Control what your benchmark’s conclusions rest on.
Design the situations your benchmark should represent and their proportions across ontologies. Coverage Engineering connects that design to existing cases, missing combinations, and the expert clarification needed to construct a more useful benchmark.
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“In a demo, you control the inputs. In production, you don’t. That’s when unexpected cases start breaking the system.”
Designing representation
A larger dataset can still omit the conditions that determine whether AI is useful. Coverage Engineering makes the intended representation explicit across ontologies, including the combinations and proportions that matter. Lemon helps develop that design into work your team can inspect and direct.
Ontologies
Organize the task space using dimensions and ontology values, alongside the topics your AI needs to handle. Product objectives and expert experience guide which distinctions matter. Reusable coverage knowledge gives teams a common basis for describing the work while each benchmark retains its own purpose.
Acting on coverage
Coverage connects the benchmark’s purpose to construction and elicitation. It also gives production evidence a place to inform offline development as user behavior changes.
Direct Lemon Weave toward the relevant ontology tuples and construction patterns. Review the resulting cases and worlds against the representation need that motivated them.
Prepare an expert contribution around the unresolved decision. The findings can clarify both the rubric and the coverage conditions in which it applies.
Ingested interactions can reveal new intents, semantic regions, and changes in the case mix. Use those findings to revise ontologies, coverage priorities, and the next offline benchmark.
Interpreting the benchmark
A benchmark intended to reflect common use needs a different mix from one designed to expose the hardest failures. Coverage supports deliberate emphasis on complex cases while keeping that emphasis visible in the conclusions your team draws.
Production records show situations users have encountered. Experts can identify rare combinations and changing conditions that those records do not contain. Both sources inform coverage, with a stated reason for what the benchmark includes.
Start with the objectives and distinctions relevant to one development decision. Existing cases, expert findings, and trial evidence can then refine the coverage. The team does not need an exhaustive ontology before it can investigate an important gap.
How interacting conditions, changing state, and recovery decisions shape the coverage an operational agent needs.
Connected products and capabilities
Start from the work you have
We can examine what it is meant to establish, what its cases actually represent, and which missing distinctions deserve construction or expert attention.