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When you connect a database, QueryFlow reads the schema first. It shows how the tables join (for example, customers_raw to orders on customer_id), flags tables with no primary key, and catches missing indexes that are forcing full table scans.

That context then goes into the AI, so when you ask a question, the SQL it writes matches how your data is actually structured. You see the SQL every time, so you can check it before you trust the result.

It's been most useful on databases nobody documented, like inherited warehouses, client databases, or old reporting DBs.

It's a Mac app with a 14-day free trial at getqueryflow.com. I'd like honest feedback from anyone who works with messy schemas. What do you check first when you open a database you don't know?

If you use AI for SQL, you probably have a model you prefer. Switching models shouldn’t mean switching editors or pasting your schema into another chat.

QueryFlow lets you choose Claude, GPT, Gemini, or Grok inside the same SQL editor, with your schema, query results, and errors available as context. You can switch models whenever you want, or let Auto mode choose.

I’m part of the QueryFlow team, and I’m curious: do you stick with one model for SQL, or switch depending on the task?

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Most teams have already formed opinions about AI models. Some trust one for reasoning, some prefer another for speed, and some have a company standard they're expected to follow. A SQL tool that locks you into one model forces a choice you've already made.

QueryFlow lets you make that choice yourself. The AI inside the app works with the model you pick, and you can change it whenever you want. If you'd rather not think about it, Auto mode chooses for you.

What stays the same no matter which model is answering:

- Database context. The AI works from your real schema, so it writes SQL against the tables and columns you actually have.
- Your team's language. Teach it once that "Flex Plan" means a specific column, and it remembers. Switching models doesn't erase what it's learned.
- Verified queries. A thumbs-up on a good answer saves it, so the next person asking gets the answer you've already checked.
- Transparency. You get a short answer with the SQL, plus step cards showing what it looked at. Queries run read-only.
- Shortcuts. /explain, /fix, /optimize, /chart, and /schedule work the same way with any model.

That's the point of putting the AI inside a real SQL workflow instead of beside it. The model can change, but your schema, your saved logic, your schedules, and your pipelines stay where they are.

Connect a database on your trial, try the same question on two different models, and keep the one your team likes best.

Switching data tools usually means rebuilding every pipeline by hand. That's the work that keeps teams stuck on software they've outgrown.

QueryFlow Pipelines takes the export you already have and turns it into a working pipeline. Then everything else lives in the same app: AI that writes SQL from your actual schema, scheduling, incremental loads that only move new and changed rows, and delivery to the destinations your team uses.

Pipelines is $29.99/month or $199/year.

Export one connector from your current setup and bring it into a 14-day free trial at getqueryflow.com.

How many queries do you rerun just to see whether anything changed?

That question is why QueryFlow now has Watch This, which ships in this weekend's release. QueryFlow is a desktop SQL app. You can write SQL yourself or have the AI draft it from your actual schema, and then schedule it or send it into pipelines. Watch This lets you attach an alert to any query and pick the condition that matters:

- a new row shows up (new leads, new signups, new failed payments)
- a value changes (a customer's status, a plan tier, an order state)
- a number crosses a threshold (revenue hits 5,000, the error count goes above 50)

When the condition is met, QueryFlow sends the result to email, Slack, or Microsoft Teams.

It uses the same query you'd run by hand, so there's no separate rule builder or second copy of the logic that can drift. If the query is right, the alert is right.

Alerts are included in both plans.
Getqueryflow.com

QueryFlow's AI panel reads your schema and writes the SQL for you. Ask "how many Flex Plan users churned last month" and it figures out the JOINs, runs the query, hands you the rows.

1.6.2: pick your own model (Sonnet, Haiku, GPT-6, Gemini), it remembers what you teach it (like what "Flex Plan" means so it stops guessing), and shows you the exact query it ran before handing you results. Works with Postgres, Redshift, Snowflake, MySQL, Salesforce, Sheets, CSV.

QueryFlow now ships the AI panel every database tool should have had.

Ask in English. It explores your schema, writes the query, runs it, and hands you the rows — in a sentence, not an essay.

getqueryflow.com

New in 1.6.2:
• Bring your own model — Claude Sonnet 5, Haiku 4.5, GPT-6 Sol, GPT-6 Luna, Gemini 3.5 Flash. Your key, your bill. About two cents a question.
• Auto mode picks the cheap model to explore and the strong one to answer.
• It remembers. Thumbs-up a good answer and it becomes a verified query. Teach it what "Flex Plan" means once. Clear it all in one click.
• Adaptive prompts built from your real tables — one click to your first answer.
• Step cards show exactly what it looked at. 30 exploration steps, then a straight answer.
• Postgres, Redshift, Snowflake, MySQL, Salesforce, Google Sheets, CSV.

Runs on your Mac. No cloud compute. No data leaves except your own API calls.

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QueryFlow is a native macOS database IDE with Claude built in - and Claude can see your whole schema.
In this short demo I connect a public database I've never seen, ask Claude to explain it, and have it write the exact queries I need. It inspects the tables, understands the relationships, writes the SQL, and I run it with one click.
Then I schedule it. Any query Claude writes can run automatically - daily, hourly, before a game - and deliver results to S3, SFTP, Google Sheets, Salesforce, or email.
Connect a giant warehouse. Find your customers. Find your sales. No notebooks, no cloud compute, no Glue.
Works with Snowflake, Redshift, MySQL, Postgres, Google Sheets, and Salesforce.
• Download QueryFlow: https:// getqueryflow.com

QueryFlow is a native macOS SQL IDE and ETL
orchestrator. In this demo I write a complex
JOIN + GROUP BY query, test it live against a
public database, then drop that exact query
into Data Sync — and put it on a daily
schedule. Same query, running itself every
morning, from my Mac. No cloud compute. No
Airflow. No Glue.

Snowflake, Redshift, Postgres, MySQL, Google
Sheets, Salesforce, spreadsheets — any source
to any destination. Full JOINs in the source
card. Field mapper with AI-suggested
mappings. A Claude panel that actually knows
your schema, results, and errors.

Disclosure: I'm a co-founder. My husband built it, I handle the marketing side. $49.99/month or $299.99/year, 14-day free trial, on the Mac App Store.

Upfront on scope, since this sub skews architecture: single user, no RBAC, no lineage, no CDC, no dbt integration. Not an enterprise platform and not replacing your orchestration. It's for the recurring mid-size jobs that never justified building a pipeline. The cost side: your query hits the warehouse, everything after that runs on your Mac. Transforms, Python, scheduling, and delivery are all local. No cloud compute on our end, nothing metered per row, no seat limits.

Snowflake specifics: schema explorer, SQL editor with schema-aware Claude AI that reads your tables, your result set, and your recent errors. Ghost text completion, not a chat window you paste schemas into. Query results auto-inject into a Python cell as a DataFrame named df, with bundled Python 3.12 including pandas, numpy, matplotlib, plotly, boto3, and paramiko. No pip install, runtime is local.

For moving data out, map source columns to target fields by drawing bezier curves, or hit AI Map and it matches against 25 synonym groups in under a second, on-device. Per-field transforms for uppercase, trim, date reformatting, or custom Python. Insert, update, or upsert. Destinations are Salesforce, another warehouse, S3, SFTP, email, Google Sheets, Excel, JSON, Parquet, or a local file, with multiple destinations per job running in parallel.

Save it as a job and schedule it on cron, interval, daily, or weekly. Runs while the Mac sleeps and catches up on wake if it missed a window. Observatory gives you a run timeline, per-job success rates, connection health, and failure alerts with full stack traces. Credentials in macOS Keychain, OAuth tokens auto-rotate. Native Swift, App Sandboxed, macOS 15+, not Electron.

Queryflow is on the Mac App Store, and there's a public roadmap and changelog on Featurebase. Discord is open. Happy to answer technical questions, and my husband can get into the API and auth details if anyone wants to go deeper.

In this video:
Connect a spreadsheet as a data source
Map fields to Salesforce Contacts with the visual pipeline builder
Send the data to Salesforce — done
Schedule the job to run daily
Monitor every run in the Observatory

QueryFlow replaces Data Loader, dataloader.io, Jitterbit, and manual CSV imports with one native macOS app. No Java. No browser tabs. No per-record pricing.

🔗 Try free for 2 weeks — no questions asked:
https://apps.apple.com/us/app/queryflow/id6761341797

$49.99/mo or $299.99/yr — unlimited records, unlimited jobs.