Sales AI v3 — Mission Control

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

English name/label>

—

Behaviour type/label>

—

When this fires/label>

—

Recommended action/label>

—

Relationship impact/label>

—

Explanation template (decision rationale)/label>

—

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

Market Model — LME Aluminium Price

Recalculate

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LME — USD/t— market shocks

Parameters

Defaults from configs/market_sourced.yaml · adjust to explore scenarios

LME base price (USD/t)

2396

Price trend (% per month)

+0.3%

Noise σ (% daily)

1.4%

Seasonality amplitude (%)

25.0%

Market shocks (count / 26 months)

1

Seed

42

Min—

Max—

Avg—

Volatility σ—

LME price — Jan 2024 to Mar 2026 (26-month simulation) Price Black swans

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Account Timeline — Daily Decision Log

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— 30 days shown

Select account

Expert type

Known patterns

—

Blind spots (missed)

—

Expert actions & pattern analysis — first 30 days, seed 0 · "missed" = expert failed to act on a trigger

Select an account to see its decision log

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

🔒

Stage 5: RL Agent

Belief state estimator · Policy network (PPO/DQN) · Training loop · LLM layer 7

Gate: unlocks after Stage 4 ✓

Results & Visualization

Planned

🔒

Stage 6: Visualization & Demo

AI vs Expert margin chart · Portfolio Σ · Belief state evolution · 5-min investor demo

Gate: unlocks after Stage 5 ✓