The deterministic layer your inference engine doesn't have.

LLM INFERENCE

Confident. Uncalibrated. Jurisdiction-unaware. Probabilistic in the wrong sense — the model is uncertain about which token to emit, not about the actual likelihood of a legal outcome.

SPLITIFI MODELS

Calibrated. Trained on labeled outcomes. Jurisdiction-specific. A 70% prediction means 70% of comparable cases resolved that way — not that the model is 70% confident in its output.

MCP INTEGRATION

90+ tools. Structured schemas. Model-agnostic.

Splitifi's MCP server exposes outcome prediction as callable tools with typed inputs and validated outputs. Works with Claude, GPT, open-weight models, and any host supporting the Model Context Protocol specification.

Every tool returns a structured response with calibration metadata — so your model can reason about confidence, not just output.

AVAILABLE MCP TOOLS

splitifi_predict_custody

splitifi_judge_profile

splitifi_settlement_probability

splitifi_case_strategy

splitifi_award_ranges

splitifi_asset_division

+ 84 additional tools...

Legal Research AI

Ground citations in calibrated outcome probability. Move research from precedent lookup to probabilistic outcome framing grounded in real court records.

Litigation AI

Add win probability and settlement zone to case analysis. Your inference engine reasons about facts; Splitifi tells it what comparable cases actually resolved at.

Document AI

Generate outcome-aware documents that know what works with a specific judge. Demand letters, settlement proposals, and motions informed by behavioral models.

Structured schemas. Typed outputs.

Every tool call returns a validated JSON schema with probability value, confidence interval, calibration, feature attribution summary, and jurisdiction metadata.

Your model can inspect confidence, explain uncertainty to users, and route to human review when calibration score indicates low reliability.

EXAMPLE TOOL CALL + RESPONSE

{
  "tool": "splitifi_judge_profile",
  "input": {
    "judge_id": "jd_fl_15th_001",
    "case_type": "custody_modification",
    "jurisdiction": "FL"
  },
  "output": {
    "grant_rate": 0.41,
    "median_timeline_days": 147,
    "disposition_bias": "status_quo",
    "confidence": 0.88
  }
}