Sales AI v3 — Mission Control
Stage 4 active · Monte Carlo complete ✓ · Stages 1–3 complete ✓
Current stage
3 / 6
Expert Baseline
Gates passed
10 / 10
Stages 1 & 2 ✓
Patterns defined
60
SP30 + BP30 ✓
Client archetypes
18
8 industries ✓
Behaviour Pattern Library
+ Add pattern
↓ Save to dataset
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Reset
60 patternsSP30 seller · BP30 buyer0 confirmed
ID Name (RU) Behaviour type Trigger / Condition Action Status
SP01 —
×
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Trigger × Response Matrix
↓ Save to dataset
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Reset to defaults
20 triggers12 response types— active links
Strength:
None
Weak
Medium
Strong
Click cell to cycle strength
Coverage & Balance Check
—
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— coverage— balance
Account Profiles
+ Add archetype
— accounts— industries
Card view
Relationship editor
Market Model — LME Aluminium Price
Recalculate
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LME — USD/t— market shocks
Parameters
2450
+0.3%
0.8%
6.0%
3
42
Min—
Max—
Avg—
Volatility σ—
LME price — Jan 2024 to Mar 2026 (26-month simulation) Price Black swans
Account Timeline — Daily Decision Log
Run 100-day simulation
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No account selected— events logged
Select account
Data access boundary
Run to verify
Event log —
Select an account and run simulation to see the decision log
Stage 2 gate checks
Reproducibility — same inputs produce identical results
Accounting identity — revenue − costs = margin (always)
Plausibility — 100-day account log reviewed and confirmed
Data access boundary — agent never sees hidden state
Edge cases — zero budget, inactive accounts, all silent
Margin range — values within realistic PLN range for aluminium B2B
Expert vs AI Scoreboard
Export CSV
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Expert baseline: — AI system: pending Delta: —
Expert model parameters
Known seller patterns
20 / 30
Pattern miss rate
15%
Expert bias type
Accounts managed
15
Daily capacity (hours)
8h
Monthly budget (PLN)
25,000
Performance comparison — 26-month simulation Simulated · not real data
Method Total margin (PLN) vs Random vs Expert Accounts retained Status
Cumulative margin over 26 months Random Expert AI system Upper bound
Outcome distribution (50 runs)
Statistical significance
Run scoreboard to see results
Monte Carlo Explorer
Complete ✓
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Random policy (lower bound)
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Expert policy MC
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Outcome distribution (real)
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Replay buffers
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RL Agent
Planned
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Stage 5: RL Agent
Belief state estimator · Policy network (PPO/DQN) · Training loop · LLM layer 7
Gate: unlocks after Stage 4 ✓
Results & Visualization
Planned
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Stage 6: Visualization & Demo
AI vs Expert margin chart · Portfolio Σ · Belief state evolution · 5-min investor demo
Gate: unlocks after Stage 5 ✓