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Roadwise

Tsaikhana

Who: learner drivers in Baku preparing for the state driving exam, and the driving schools that train them. Today a learner gets a fine or an instructor's remark — it says what went wrong, never why, which mistake would fail the exam, or whether they're actually improving. What Roadwise does: the learner drives a 3D simulated city under international road rules, with fines based on Azerbaijan's Administrative Offences Code (İXM): ~1,000 road-sign poles placed by the standard sign rules, signalised junctions with stop lines, zebra crossings, give-way junctions and a 60 km/h town limit. After each drive they get a debrief tied to the exact time and street of every mistake, what to fix first, practice drills, a quiz on their weak rules, and an exam-readiness score tracked across drives. Why the assessment is trustworthy: the AI never judges driving. Rule checks, fines and all metrics come from deterministic, unit-tested code (304 tests); the AI only explains them. Every AI answer is checked against the drive data (event ids, rule keys, no invented numbers, every major fault covered) and replaced by a template if it fails. On our eval: 100% grounded, 0 hallucinations, 100% of major faults covered, $0.002 per debrief. Progress you can audit: readiness is a transparent formula (faults, rule compliance, speeding time, smoothness) with a point-by-point breakdown, not a black-box score. Honest limits and next step: the simulation has no AI traffic yet, and readiness hasn't been validated against real exam results. Our next step is a pilot with a Baku driving school to compare readiness scores with students' actual exam outcomes.