AfterQuery diagnostic for enterprise agents

This entry point matters because it makes AfterQuery look less like a data vendor and more like an AI implementation partner that starts by mapping where expert judgment actually lives inside a firm. In practice, that means finding the moments where analysts, lawyers, or operators rely on tacit know how, then turning those steps into datasets, evals, and tool using agent environments. That is a much stickier wedge than selling a generic benchmark or labeling project.

• The Raine work shows the pattern in concrete terms. The project centered on ingesting historical deal materials, structuring them into a searchable index, and using that internal knowledge base to support agent workflows. The value is not just a smarter model, it is a system wired into how a firm already works.
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• This sales motion fits the enterprise agent bottleneck. McKinsey found 62% of organizations are experimenting with AI agents, but only 23% are scaling them. The gap is usually not access to a model. It is the hard work of grounding agents in company specific data, workflows, and approval logic.
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• Competitively, this puts AfterQuery on a different flank from broad data platforms like Scale and marketplace driven peers like Mercor. Those players also sell RL environments and evals, but AfterQuery is differentiated by starting with failure mode analysis inside one workflow, then expanding into adjacent tasks like diligence, compliance review, and valuation QA.
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The next step is a move from one off diagnostics to a repeatable vertical playbook. If AfterQuery can turn finance, legal, and healthcare projects into reusable workflow templates, rubrics, and environments, it can become the layer enterprises use to make agents reliable in real work, not just impressive in demos.