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Finance Analysis via Bottom-Up Estimation + Reconciliation

Pitch

Build a bottom-up financial model from tasks, resources, and unit economics, then reconcile it against top-down estimates to surface gaps and improve accuracy.

Why

Top-down estimates are fast but coarse. Bottom-up estimates are realistic but time-consuming. Combining both gives the speed of top-down with the credibility of bottom-up, while exposing unrealistic assumptions early.

Problem

  • Plans often include partial or inconsistent financials.
  • Bottom-up models are missing or unstructured.
  • Divergence between top-down and bottom-up is not tracked.

Proposed Solution

Implement a bottom-up estimation module that:

  1. Extracts work packages, resources, and timelines.
  2. Builds cost and revenue from unit-level assumptions.
  3. Aggregates to totals and cash flow.
  4. Reconciles differences with top-down estimates.

Bottom-Up Estimation Framework

1) Work Package Extraction

Identify:

  • Tasks and milestones
  • Deliverables and work packages
  • Staffing requirements
  • Duration and dependencies

2) Unit Cost Modeling

Attach costs per unit:

  • Labor: role-based hourly or monthly rates
  • Materials: quantity x price
  • Infrastructure: cloud usage, hardware
  • External services: contractors, vendors

3) Revenue Modeling

Build revenue from:

  • Units sold x price
  • Contract values and timelines
  • Subscription tiers and churn
  • Conversion funnel estimates

4) Aggregation

Produce:

  • Project budget by phase
  • Monthly burn and runway
  • Break-even timing
  • Profit and loss summary

5) Multi-Currency Handling

Plans may involve multiple currencies (e.g., cross-border projects). The bottom-up model should:

  • Track line items in native currency at the work-package level.
  • Roll up to a reporting currency with explicit FX assumptions.
  • Support a third currency when local currencies are unstable.

Reconciliation Layer

Compare bottom-up vs top-down outputs:

  • Total revenue variance
  • Margin variance
  • Capex and opex mismatches
  • Timeline inconsistencies

Reconciliation output:

  • Variance report
  • Recommended adjustments
  • Updated confidence levels

Output Schema

{
  "bottom_up": {
    "total_cost": 2200000,
    "total_revenue": 4800000,
    "burn_rate_monthly": 180000,
    "reporting_currency": "USD",
    "fx_assumptions": [
      {"pair": "BRL/USD", "rate": 0.19, "as_of": "2026-02-10", "volatility": "high"}
    ]
  },
  "top_down": {
    "total_cost": 1500000,
    "total_revenue": 5200000
  },
  "variance": {
    "cost_delta": 700000,
    "revenue_delta": -400000
  },
  "reconciliation_notes": [
    "Bottom-up assumes 12 engineers, top-down assumes 8",
    "Top-down margin range exceeds observed unit economics"
  ]
}

Integration Points

  • Uses CBS generation as input for cost categories.
  • Feeds into investor thesis matching and risk scoring.
  • Drives evidence-based adjustments in financial claims.

Success Metrics

  • Percentage of plans with bottom-up models.
  • Reduction in financial variance after reconciliation.
  • Investor confidence in financial projections.

Risks

  • High data requirements: mitigate with default benchmarks and missing info prompts.
  • Estimation complexity: prioritize major cost drivers first.
  • False precision: publish ranges and confidence scores.

Future Enhancements

  • Automated cost libraries by region and sector.
  • Sensitivity analysis and scenario modeling.
  • Learning system that updates estimates from real outcomes.

Detailed Implementation Plan

1) Bottom-up estimator architecture

For each WBS task, build a cost object: - labor profile (roles, hours, rates) - material BOM (qty × unit cost) - external services - fixed/variable overhead - contingency allocation

Aggregate task costs -> work package -> phase -> plan total.

2) Revenue build-up layer

For plans with revenue: - unit sales model or contract milestone model - churn/renewal assumptions (if subscription) - conversion and ramp assumptions

Link revenue timing to project timeline for cashflow realism.

3) Reconciliation algorithm (top-down vs bottom-up)

Input: - top-down scenario bands (P10/P50/P90) - bottom-up deterministic/ranged total

Compute variance decomposition by category: - labor delta - materials delta - capex delta - schedule-induced delta

Generate reconciliation recommendations ranked by expected impact.

4) Convergence rules

Define explicit convergence status: - green: variance <= 10% - yellow: 10–20% - red: >20%

If yellow/red, require iteration actions before “finance-ready” status.

5) Iteration loop

  1. identify highest variance categories
  2. request missing inputs or benchmark corrections
  3. update assumptions
  4. recompute both models
  5. re-evaluate convergence state

Track each iteration for audit.

6) Integration points

  • Pull CBS line items from Proposal 33
  • Pull benchmark priors from Proposal 34
  • Expose convergence status in plan summary
  • Feed risk module when persistent red variance remains

7) Output package

Produce a finance bundle: - bottom-up ledger - top-down summary - variance decomposition chart - reconciliation action log - convergence status + signoff checklist

8) Rollout phases

  • Phase A: deterministic bottom-up + simple variance
  • Phase B: ranged bottom-up with confidence levels
  • Phase C: automated reconciliation recommendations
  • Phase D: closed-loop learning from actual spend/revenue outcomes

9) Validation checklist

  • Accounting consistency (totals match component sums)
  • Reproducibility under fixed assumptions
  • Reviewer confidence uplift after reconciliation
  • Reduced forecast error on executed projects

Detailed Implementation Plan (Convergence Operations)

Convergence Workflow

  1. Generate bottom-up ledger from WBS/CBS.
  2. Pull top-down baseline from proposal 34 module.
  3. Compute variance by category and timeline bucket.
  4. Trigger correction cycle until convergence status reaches green/yellow threshold.

Action Prioritization

  • Rank correction actions by expected variance reduction per effort unit.
  • Recommend max 5 actions per cycle to avoid analysis overload.

Signoff Policy

  • Red variance blocks investor-ready status.
  • Yellow requires explicit financial waiver.
  • Green auto-advances to packaging phase.

Learning Loop

  • Persist convergence trajectories to improve future default assumptions.