jevsql
SQL with natural-language predicates over SQLite: filter, rank, and classify rows by meaning, batched and cost-guarded.
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SQL with natural-language predicates over SQLite: filter, rank, and classify rows by meaning, batched and cost-guarded.
Sifts Parquet and JSONL training data at more than 1,500 rows a second.
Links records across two datasets from a match rule written in plain English, from Python, the shell, Stata, or R, and reports F1 0.73 against 0.69 for tuned string matching on NBER patent assignees to Compustat.
Parses PDF, DOCX, PPTX, or text locally, then asks Jev one closed-set relation question per candidate entity pair and exports a graph with per-edge probabilities and page evidence to JSON, CSV, or Neo4j.
Home Assistant integration: ask a question about your house, get a probability, choice, or score as an entity.
Pre-alpha PostgreSQL extension for categorical classification with Jev.
One request per page picks among 261 IRS forms and seven page kinds; reports 100 percent on its corpus at $0.001 a page, 34 times cheaper than the LLM pipeline it replaced.
Semantic SQL for PostgreSQL, with Jev answering the predicates.
Gives every stored agent memory a lease and asks Jev whether new evidence ends it; [live demo](https://invalidate-playground.vercel.app).
Eight small examples from mechanical and electrical engineering: CAD routing, FEM triage, DFM screening, BOM alignment.
Asks a clinical trial report for systematic-review data by voice, text, or a questions file; every answer is a verbatim quote with its file and place.
Packs 32 items into one request for bulk classification and calibrates the confidence cut that sends the least-sure rows to a person, reporting 533 items a second at 89.2 percent agreement with human labels.
Zero-shot named entity recognition for Chinese and English: code proposes candidate spans at punctuation and Jev choice questions nominate, verify, and fix the boundary of each entity; reports 73.7 average strict F1 on 12 NER benchmarks against 72.1 for Qwen3.8-27B.
PostgreSQL extension that answers plain-language questions about your tables.
DuckDB worker that exposes choice, noul, and score as lateral-joinable table functions in SQL.
Adds Jev Noul, Choice, and Score judgments to SQLite through a loadable C extension and Python wrapper, with scalar functions and batched virtual-table queries.
DuckDB extension that asks a question of every row and returns a real SQL type.
Crawls a site, checks it against 52 SEO rules, has Jev judge every page, and writes PDF, spreadsheet, and Markdown reports.
Ticket triage and file tagging on a typed-judgment model; reports 78 percent fewer tokens and 96.1 percent accuracy against a 93.7 percent baseline.
Scores OpenTelemetry log signal before paying for LLM analysis.