We provide businesses a cost-effective and quick way to model their most consequential decisions — graded against public record.
Businesses make their most consequential decisions without systematic analysis. Human advisors are expensive and slow. Internal analytical capacity is inaccessible to most organizations. Our wager — the entire point of Aktara — is that AI has crossed the threshold where it can simulate business outcomes, learn from past decisions, and make any operator meaningfully more informed.
Worked cases
0
Direction hits
0.0%
A deterministic, nine-section structured representation of the company — identity, financial state, structure, dynamics, position, constraints, risks, opportunities, decision signals — built from the uploaded inputs before any reasoning happens.
10,000 iterations per decision on a deterministic seed. Returns the full P10/P25/P50/P75/P90 distribution, not a single point estimate.
RAG over the natural-experiments ledger — 1,118 graded cases spanning 100+ industries and 127 waves. Every claim cites the precedents that informed it.
GPT-5 writes both a plain-English summary for the operator and a partner-grade memo for the boardroom — from the same evidence stack.
Every prediction gets graded against the realised outcome and folded back into the substrate. The 80% forecast interval (P10–P90) captures the realised outcome 92% of the time.
The engine surfaces 7–13 high-value decisions specific to the company before running reasoning on any of them. Pricing is one decision class of roughly sixty.
Outcomes are sourced from public SEC filings, merchant-level revenue reports, and first-party customer data supplied under consent. Each prediction is scored against the realised revenue change in the period following the decision. Every case in the ledger is counted — no outliers removed, no misses dropped.
The substrate currently spans 1,118 cases across 100+ industries and 127 waves. Direction accuracy is 89.7% on the 156-case pricing bench, with a structural ceiling near 90% on the expanded ledger.
The engine forecasts an 80% confidence band (P10–P90) for every decision. In practice the realised outcome lands inside that band 92% of the time — meaning Atlas is slightly more conservative than promised, which is the safer way to be wrong about uncertainty.