The Decision Engine: From Data to Actionable Intelligence

Data is not intelligence. Intelligence is data that has been analyzed, scored, and contextualized to support a specific decision. Prismatic’s Decision Engine is the system that performs this transformation through a 6-stage pipeline.

#The Pipeline

The Decision Engine processes intelligence through six stages:

Raw Data → Scoring → Hypothesis → Recommendation → Uncertainty → Explanation

Each stage adds structure and confidence to the analysis.

#Stage 1: Data Ingestion

Raw results from OSINT adapters, document analysis, and manual inputs enter the pipeline as unstructured evidence:

evidence = %{
  source: :ares,
  entity: "Target s.r.o.",
  finding: %{
    type: :financial,
    metric: :annual_revenue,
    value: 150_000_000,
    currency: :czk,
    period: 2025
  },
  confidence: 0.95,
  retrieved_at: ~U[2026-03-23 10:00:00Z]
}

#Stage 2: Scoring

The Scoring Engine evaluates each piece of evidence against configurable criteria:

defmodule PrismaticDD.Decision.ScoringEngine do
  def score(evidence, criteria) do
    Enum.map(criteria, fn criterion ->
      weight = criterion.weight
      value = evaluate(evidence, criterion)
      %{criterion: criterion.name, score: value * weight, raw: value}
    end)
  end
end

Criteria are domain-specific. For M&A due diligence:

• Financial health (revenue trend, profitability, debt ratio)
• Legal standing (litigation, compliance, insolvency)
• Operational quality (employee count, asset base)
• Governance (ownership transparency, director history)

#Stage 3: Hypothesis Generation

Rather than presenting raw scores, the engine generates testable hypotheses:

hypotheses = [
  %{
    statement: "Target company is financially healthy",
    supporting_evidence: [revenue_growth, positive_ebitda, no_debt],
    contradicting_evidence: [declining_margins],
    confidence: 0.78
  },
  %{
    statement: "Ownership structure is transparent",
    supporting_evidence: [registered_ubo, domestic_holding],
    contradicting_evidence: [offshore_intermediate],
    confidence: 0.62
  }
]

Each hypothesis is supported and contradicted by specific evidence. This forces the analysis to consider both sides rather than cherry-picking favorable data.

#Stage 4: Recommendation

Based on scored hypotheses, the engine produces recommendations:

%Recommendation{
  action: :proceed_with_conditions,
  conditions: [
    "Verify offshore entity ownership chain",
    "Obtain latest audited financials",
    "Complete sanctions screening on UBO"
  ],
  risk_level: :medium,
  confidence: 0.72,
  reasoning: "Financial indicators positive but ownership complexity requires additional verification"
}

Recommendations are one of: :proceed, :proceed_with_conditions, :defer, or :decline. The engine never produces a binary yes/no – every recommendation includes conditions and confidence.

#Stage 5: Uncertainty Estimation

The Uncertainty Estimator quantifies what we do not know:

defmodule PrismaticDD.Decision.UncertaintyEstimator do
  def estimate(pipeline_state) do
    %{
      epistemic: calculate_epistemic(pipeline_state),
      aleatoric: calculate_aleatoric(pipeline_state),
      bootstrap_interval: run_bootstrap(pipeline_state, n: 1000),
      leave_one_out: run_loo_sensitivity(pipeline_state)
    }
  end
end

Bootstrap analysis – resample the evidence 1000 times and measure score variation. High variation means the conclusion depends heavily on specific pieces of evidence.

Leave-one-out sensitivity – remove each evidence source and re-score. If removing a single source changes the recommendation, that source is a critical dependency.

#Stage 6: Explanation

The Explanation module produces a traceable reasoning chain:

%Explanation{
  summary: "Medium risk. Proceed with conditions.",
  key_factors: [
    %{factor: "Revenue growth 15% YoY", impact: :positive, weight: 0.25},
    %{factor: "Offshore intermediate in ownership", impact: :negative, weight: 0.15},
    %{factor: "No sanctions exposure", impact: :positive, weight: 0.20}
  ],
  data_gaps: [
    "Latest audited financials not available",
    "UBO verification incomplete for Cypriot entity"
  ],
  confidence_breakdown: %{
    epistemic: 0.12,
    aleatoric: 0.06
  }
}

Every recommendation is fully traceable: which evidence supports it, which contradicts it, what information is missing, and how confident we are.

#Reconciliation Loop

After a decision is made and outcomes are observed, the Reconciliation module feeds results back into the system:

PrismaticDD.Decision.Reconciliation.reconcile(%{
  case_id: case.id,
  prediction: :proceed_with_conditions,
  outcome: :successful_acquisition,
  outcome_notes: "Offshore entity verified as legitimate tax structure"
})

This feedback improves future scoring by calibrating criteria weights. If the engine consistently overweights or underweights certain factors, reconciliation adjusts the model.

#Integration Points

The Decision Engine integrates with three Prismatic pipelines:

• OSINT Pipeline – entity data, sanctions screening, web intelligence
• DD Pipeline – case management, document analysis, entity profiles
• Investigation Pipeline – deep investigations, evidence chains

Each pipeline can invoke DecisionEngine.run_full_pipeline/2 with its domain-specific criteria.

#Running It Yourself (April 2026 update)

The single-entry orchestrator PrismaticDd.QuickInvestigate wraps the full 10-stage pipeline into one call. From an IEx session:

iex> {:ok, envelope} = PrismaticDd.QuickInvestigate.run("29155142")
iex> envelope.recommendation.verdict
# :risk
iex> envelope.scorecard.total_score
# 67
iex> envelope.recommendation.reason
# "Legal compliance risks detected; 3 cross-validated flags from 5 sources"

The default preset :top5_czech queries ARES (business registry) + ISIR (insolvency) + CEDR (subsidies) + OrRegistry (commercial) + CNB (financial sector). The DDLevel L5 gate enforces cross-validation: only claims backed by 2+ independent sources with confidence 0.7 pass. The resulting EvidenceBundle feeds into the pipeline exactly as described above.

Preset options:

• :top5_czech (default) — fastest, covers 90% of Czech corporate DD
• :all_czech — all registered Czech adapters for the target type
• :registry_only — ARES + OrRegistry + RejstrikFirem (sub-5s)
• Custom module list: run("29155142", [Czech.Ares, Czech.Cedr])

For LiveView integration use QuickInvestigate.run_async/2 which returns immediately and broadcasts {:quick_done, envelope} or {:quick_error, reason} on a per-run PubSub topic.

#Conclusion

The Decision Engine transforms Prismatic from an intelligence collection platform into a decision support system. By combining scoring, hypothesis testing, uncertainty estimation, and traceable explanations, it provides analysts with the structured analysis they need to make confident decisions – while being transparent about the limits of that confidence.

Explore the {{ cross_link(path=”/hub/dd/decisions”, text=”DD Dashboard”) }} or learn about the Nabla Confidence Framework for uncertainty quantification.