Same task, three engines, side by side — speed, tokens, cost, accuracy, and hallucination rate against known ground truth.
50 synthetic invoices, native PDF/XLSX (no OCR needed — text extracted directly via pdfplumber/openpyxl).
Pick invoices on the left, then run — results stream in here live.
50 synthetic emails across 7 categories — select some, classify with all three engines.
Pick emails on the left, then run — results stream in here live.
Rerank a corpus by relevance to a query. TypeSafe asks all N relevance questions in one call (true intra-request parallelism); DeepSeek/gpt-5-nano each rank all N candidates in one generative pass.
Pick a corpus and query on the left, then run — rankings stream in here live.
A real support-inbox pattern: TypeSafe gates every email (cheap, always runs) — only the ones it
flags needs_reply get an expensive DeepSeek-generated draft. Compare that against the
hypothetical cost of generating a draft for every email regardless.
Pick emails on the left, then run — gate decisions and drafts stream in here live.
| File | Category | Needs reply | Action | Draft | Gate cost | Generate cost |
|---|
24 synthetic design screens as structured layer metadata (hex/font/spacing/name — never pixels). One call bundles five checks: brand-token compliance, naming convention, breaking-change risk (vs previous version), review-comment triage, and brief-match scoring.
Pick screens on the left, then run — audits stream in here live.
Stats pulled from real measurements taken earlier in this comparison — not marketing numbers.
From TypeSafe's own use-case map — scan for where it could fit in your workflow.
Interleave AI with reliable software you can run a million times unattended. Code owns control flow; TypeSafe handles the semantic decisions.
Frontier intelligence at ~150ms — fast and smart enough to embed into a UI or program into a game loop.
100x cheaper means processing giant datasets: search huge corpora, classify giant agent traces, extract features at scale.
Verify any AI's prompt, extraction, reasoning trace, or tool call — jailbreaks, citation errors, hallucinations — for a fraction of the original call's cost.
Model routing, semantic context retrieval, LLM error detection and guardrails, reasoning-trace classification — at lightspeed and a fraction of the cost.
| Decision shape | Reach for it when | Examples |
|---|
30 synthetic payment transactions (amount, geo-velocity, device signal, account age). Score risk + Choice a route (approve/step_up/block) — the real-time fraud-gate pattern.
Pick transactions on the left, then run — risk decisions stream in here live.
30 synthetic bank-line vs ledger-line pairs — exact matches, FX rounding, banking fees, posting-timing gaps, and real mismatches. Noul match + Choice discrepancy reason.
Pick pairs on the left, then run — match decisions stream in here live.
25 request/message payloads — benign traffic mixed with textbook SQLi, prompt-injection, and PII-leak patterns. Detects each threat type in parallel, one call per payload.
Pick payloads on the left, then run — threat detections stream in here live.
5 real screens captured live from tailwindcss.com, news.ycombinator.com, github.com, vercel.com, and linear.app (Mobbin MCP needed a paid plan we don't have). Since Jev takes text only, each was converted to a structured description by direct visual inspection — same "look, then judge" pattern as the invoice pipeline. No ground truth here (subjective UX quality isn't a right-answer test) — a genuine side-by-side critique, not a score.
Pick screens on the left, then run — critiques stream in here live.