Glassline

Glassline turns plain-English questions about Postgres into interactive answers you can trust and keep exploring. Problem: LLM data answers fail three ways. Models retype query numbers into prose and get them wrong. Answers are black boxes with no SQL to check. Every follow-up click starts a new slow chat turn. Onboarding a new database is equally painful — undocumented schemas and unreviewed AI descriptions. We split the work. Offline, Muse Wonder prepares the dataset once: it inspects the read-only schema, drafts notes, glossary, comments and Boards, with every factual claim backed by SQL the tool re-runs. A person reviews and exports before Glassline ever sees it. Nothing writes to the database. Live, Muse Spark answers: it writes read-only SQL under guardrails (single SELECT, allow-list schemas, timeout, 5k cap), stores Results as r1, r2, then writes layout referencing Result IDs — never typing values. The server expands Macros (BarChart, DataTable, Metric, Cite) from stored rows and shows the SQL under every visual. Cached prompts keep typical answers to 2 calls, ~6s median. Exploration then needs no model: Boards, Controls, Drill-downs and Entity Boards re-run prepared queries in ~0.01s with tabs and an investigation log. Parallel track, not in demo: Glimmer tuning for compact A2UI. LoRA SFT of Muse Glimmer 30B on Fireworks serverless (rank 16, 325 steps → s225) cut outputs 792→332 tokens. Muse Spark Open via Baseten was tested as teacher/judge for distill-and-filter best-of-8. Quality gap was inconclusive (0.505 vs 0.523 F1), driving new filtered data. Built with A2UI. Demo runs Spark-only on AusTender and VicProp.