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          ],
          "claim_ids": [
            "afterquery.claim.round2-product-expert-signals",
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          ],
          "aliases": [
            "Rubric and Verifier-based RL"
          ]
        },
        {
          "name": "Enterprise model services",
          "description": "Identifies gaps in a model’s abilities and prepares training data. Further training adapts a specialist model or agent for deployment.",
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          "claim_ids": [
            "afterquery.claim.round2-product-enterprise-model-work",
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          ],
          "aliases": [
            "Custom Evals and Training Datasets"
          ]
        },
        {
          "name": "Atlas and Raine Search",
          "description": "Atlas helps label and organize data. Raine Search finds past deals using everyday-language queries. Deployed for a named client; general self-service availability is undisclosed.",
          "record_ids": [
            "afterquery.product.round2-atlas-raine-search"
          ],
          "claim_ids": [
            "afterquery.claim.round2-product-atlas-raine-search"
          ]
        }
      ],
      "relationships": [
        {
          "name": "NVIDIA",
          "relationship": "dataset_user",
          "description": "NVIDIA used AfterQuery tasks to train models for office work. Model-generated action records supported initial student-model training and later training with rewards.",
          "record_ids": [
            "afterquery.customer.round2-0"
          ],
          "claim_ids": [
            "afterquery.claim.round2-relationship-0"
          ]
        },
        {
          "name": "Thinking Machines Lab",
          "relationship": "dataset_user",
          "description": "AfterQuery says it supplied training data for Inkling.",
          "record_ids": [
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          ],
          "claim_ids": [
            "afterquery.claim.round2-relationship-1"
          ]
        },
        {
          "name": "Thinking Machines Lab",
          "relationship": "technology_vendor",
          "description": "AfterQuery used Tinker for further terminal-agent training and office-task distillation (training one model with outputs from another model).",
          "record_ids": [
            "afterquery.customer.round2-2"
          ],
          "claim_ids": [
            "afterquery.claim.round2-relationship-2"
          ]
        },
        {
          "name": "Legora",
          "relationship": "research_collaborator",
          "description": "Legora credits AfterQuery with co-creating a public synthetic BAR case. AfterQuery also reviewed how Legora prepared its data.",
          "record_ids": [
            "afterquery.customer.round2-3"
          ],
          "claim_ids": [
            "afterquery.claim.round2-relationship-3"
          ]
        },
        {
          "name": "The Raine Group",
          "relationship": "implementation_partner",
          "description": "An investor and Raine Search deployment partner. AfterQuery worked with Raine bankers to search past deals using Atlas and deal categories specific to the firm.",
          "record_ids": [
            "afterquery.customer.round2-4"
          ],
          "claim_ids": [
            "afterquery.claim.round2-relationship-4"
          ]
        },
        {
          "name": "Motif Technologies",
          "relationship": "dataset_user",
          "description": "AfterQuery identifies itself as the sole data partner for Motif 3.",
          "record_ids": [
            "afterquery.customer.round2-5"
          ],
          "claim_ids": [
            "afterquery.claim.round2-relationship-5"
          ]
        }
      ],
      "people": [
        {
          "person_id": "afterquery.person.spencer-mateega",
          "role": "Co-founder · CEO",
          "bio": "Studied finance and statistics at Wharton, with computer science at Penn. His internships spanned finance and software engineering.",
          "claim_ids": [
            "afterquery.claim.round2-person-spencer-mateega"
          ],
          "portrait": "assets/people/afterquery-spencer-mateega.jpg",
          "portrait_source_url": "https://www.ycombinator.com/companies/afterquery"
        },
        {
          "person_id": "afterquery.person.carlos-georgescu",
          "role": "Co-founder · CTO",
          "bio": "Previously founded an education-technology business that was acquired. His software-engineering internships included Citadel Securities and Google.",
          "claim_ids": [
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          ],
          "portrait": "assets/people/afterquery-carlos-georgescu.jpg",
          "portrait_source_url": "https://www.ycombinator.com/companies/afterquery"
        },
        {
          "person_id": "afterquery.person.danny-tang",
          "role": "Early founding team · FinanceQA coauthor",
          "bio": "The launch describes his Wharton connection and an earlier startup with Spencer. Current title and employment status are unverified.",
          "claim_ids": [
            "afterquery.claim.round2-person-danny-tang"
          ]
        }
      ],
      "notes": [
        {
          "text": "Y Combinator’s current founder list names Spencer and Carlos. Launch material and the CEO’s account also name Danny Tang as an early team member. Danny’s current title and employment status are unverified.",
          "claim_ids": [
            "afterquery.claim.round2-early-team-context"
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        }
      ],
      "performance_chart": {
        "title": "NVIDIA’s GDPval warmup comparison",
        "subtitle": "Nemotron 3 Ultra · Table 4: initial training comparison",
        "series": [
          {
            "label": "Without warmup",
            "value": 35.3
          },
          {
            "label": "With warmup",
            "value": 46.7
          }
        ],
        "qualification": "The 11.4-point difference compares the warmup stage within NVIDIA’s training method. It does not isolate the effect of changing data suppliers.",
        "claim_ids": [
          "afterquery.claim.round2-research-nvidia-warmup"
        ]
      },
      "operating_case": {
        "title": "Legal evaluation with Legora",
        "description": "Legora’s full evaluation set has 5,161 cases and 11,075 documents across 28 practice areas. BAR uses a subset of hundreds. One co-created synthetic case is public.",
        "claim_ids": [
          "afterquery.claim.round2-research-legora-bar"
        ]
      },
      "combine_round_and_valuation": true,
      "capital_note": "The company says the round closed several months earlier. The exact closing date is undisclosed.",
      "metric_ids": [
        "afterquery.metric.round2-financial-2",
        "afterquery.metric.oct14-2-expert-network"
      ],
      "metric_context": {
        "afterquery.metric.oct14-2-expert-network": {
          "scope": "Professional network",
          "note": "Undated company claim"
        }
      }
    }
  ]
}
