In plain Hebrew or English. No data warehouse, no ETL, no report developer. The query runs inside your Priority, so the number you read is today's.
Two languages, one engine. Figures shown are illustrative; in your workspace they come from your Priority.
Priority holds the answer. Getting it out is the project.
Every new number is a ticket behind other tickets. By the time the report lands, the meeting it was for has already happened.
A spreadsheet is a photograph of last Tuesday. It gets mailed around, edited, and argued over long after the ERP has moved on.
Point generic text-to-SQL at Priority and it sees thousands of cryptic table names with no map of which one means what. It answers confidently, and it answers wrong.
The fix is not a better guesser. It is a system that has read your install, and asks when it is unsure.
Four steps, and you watch all four happen.
The question is matched against the map of your install. The topic is detected automatically, or you name one: Sales, Finance, Inventory, CRM, HR, Manufacturing, Logistics, Projects, Procurement, Service.
One Priority query, written against the columns your install actually has — not the ones a general model expects to find.
Dialect and column inventory are checked before anything runs. A query that fails the check is corrected and retried, never shipped.
You approve the KPIs. The dashboard renders on live rows and stays clickable, down to the records behind each bar.
When a question could mean two things, you get a question back rather than a number. Ambiguity is never resolved silently — that is a design rule, not a setting.
Each generated query is validated against the real column inventory and Priority's own query dialect. Mistakes are caught on our side of the wire.
A guard stops runaway queries before they load your server, and explains the fix in one sentence instead of an error code.
A one-time bootstrap of about thirty minutes reads your install and builds a graph database of it — including the tables, columns and forms your implementer added. Every question after that is answered against your map, not against a generic idea of Priority.
Measured on a reference install. Your bootstrap reads your own Priority, so your map is your own — custom entities included.
Most Priority installs stopped being standard years ago. The bespoke table your implementer built for quality checks, the four columns somebody added to the order line, the form that exists nowhere else — the bootstrap reads them from your own metadata, so they sit in the graph beside the standard entities, with the same field meanings and the same join paths. A tool that only knows stock Priority cannot answer a question about them at all.
Ask for A-type invoices and the map already knows which table that is, which filter makes it that kind, and how to reach the customer and the lines from there.
4,641 forms mapped to the tables behind them, together with the filter that makes each one different. That is how an invoice question comes back with the right kind of invoice.
11,390 foreign-key edges, header-to-line relationships, and second-hop routes. The joins are correct because the route was mapped in advance, not improvised at question time.
The bootstrap reads the help text your install ships — 80.4% of forms carry it — and classifies every column as a code, a description, an amount, a quantity, a date, a status, or a flag.
A dashboard is a short conversation before it is a picture. The assistant suggests four to six measures and says why each one belongs. Nothing renders until you choose.
Sales, margin and stock cover for our top ten customers
One compound question, planned into four sub-questions, returned as four tabs. Six is the cap.
Illustrative.
Click a bar or a slice and the rows behind it open in place. The dashboard is the summary and the drill-down at once.
A question with several parts is planned into separate sub-questions and returned as a multi-tab dashboard, up to six tabs.
KPI cards, bar, line and pie charts, and tables. Export the rows as CSV, the whole dashboard as one self-contained HTML file, or the dashboard definition itself.
Every company has its own nouns. Define them once and every query after that respects them.
No movement for twelve months or more.
Applied to every question that mentions dead stock.Approved KPIs become one-click tiles at the top of the chat.
Illustrative.
Give it your website address or a paragraph about the company. It writes a summary and names the industry it detected. An administrator corrects anything it got wrong.
One click drafts up to 25 business terms and 15 KPIs. Every draft is grounded in tables that exist in your install; invented names are discarded before you ever see them.
Whatever your team calls a thing gets mapped to the form that actually holds it. Ask in house language and the query still lands on the right data.
A distill session replays a conversation together with the retrieval trace behind it, then proposes corrections to the map. Nothing is applied on its own.
Questions about part descriptions on order lines needed a second query to answer. One mapped edge removes that hop.
From the chat: “why is the part description empty here?”
Every applied edit is audited and reversible.
Illustrative.
Each one arrives with a confidence, a rationale, and the exact line from the chat that prompted it. You are approving a change you can trace, not a black box.
Fix a relationship, route a concept to the right form, bring back a form that was hidden, or stage a new business term or KPI. You approve them one at a time.
Every distillation is audited as a unit, so a change that turned out wrong comes back out whole.
Switch between the companies you are allowed to see, or put them in a group and ask all of them at once. The group runs in parallel and comes back as one table with a company column.
| Company | Orders | Revenue |
|---|---|---|
| IL | 612 | 2,140,800 |
| US | 341 | 1,286,200 |
| DE | 288 | 1,004,500 |
| NL | 173 | 640,900 |
Illustrative.
Six subsidiaries, one question, one table. The company column travels with the rows, so the split is visible in every chart built on them.
When one company does not answer, the result says which one and offers a retry for that company alone. A missing subsidiary never hides inside a total.
The shortest security review is the one where nothing was copied.
There is no warehouse and no nightly extract. Each question becomes one query that runs inside your Priority and returns rows. Nothing is staged, so nothing can be stale.
Connecting runs through a gateway-hosted popup and a server-to-server token exchange. The page you are looking at never handles the password.
Each workspace supplies its own AI key, OpenRouter or Anthropic. There is no shared pool to fall back on and no cross-tenant sharing.
The workspace itself runs on Amazon Web Services, in Amazon's Tel Aviv region, with Cloudflare in front of every request: TLS, DDoS protection, a web application firewall, rate limiting and bot filtering. The servers accept no inbound connections — they dial out to Cloudflare and traffic comes back down that tunnel, so there is no public port to scan, and admin surfaces sit behind a second gate. Your AI key and the rest of the workspace secrets are held encrypted in AWS Parameter Store, never in a database or a config file, and deploys run from a versioned pipeline.
BI Generator is invite-only while the first workspaces come on board. Access is requested through the licensing portal, and registration takes about five minutes.
The first person to connect an install runs the bootstrap. Everyone after that joins the workspace that already exists and starts asking questions immediately.
Drop a JSON, CSV or Excel file and get the same dashboard treatment with no Priority connection at all. Useful for a one-off, and for trying the product on data you already have.
A single-tenant Windows edition installs as a service for customers who keep everything inside. Its schema graph is built centrally and ships with the install.
One registration connects your team to Priority through AI. BI Generator reaches Priority through the same Priority Gateway every product in the suite uses, and the schema-graph engine it is built on ships into that gateway too.
Describe an app or a change to your Priority and get working Priority code back. Same sign-in as BI Generator.
chat.flow-chain-ai.comThe REST and MCP layer BI Generator queries through, plus a drag-and-drop n8n node on the same foundation for automation work.
One registration provisions the whole suite. Tenants, users and access tokens are managed there in self-service.
licensing.flow-chain-ai.com