FlowRunner for data teams
Automate the syncs, loads, migrations, and index rebuilds that fill your on-call rotation, with an agent that runs the pipeline and a human who owns every write that hits production or cannot be rolled back.
The pipelines nobody wants to babysit
Your day runs on jobs that mostly work until they do not. A reverse-ETL sync drifts and half the accounts in the warehouse go stale. A nightly load ingests a malformed file and the numbers in every downstream report are quietly wrong by morning. A backfill you kicked off by hand runs long, and now you are watching a terminal at 2am hoping the row counts land where they should. The routine is boring right up to the moment it is not, and the not is always expensive.
The connectors are not the problem. You already run Postgres and MySQL for the operational side, Snowflake or BigQuery for the warehouse, a vector store for the AI features, and analytics on top. The problem is the manual glue and the judgment calls in the middle. A DELETE without a WHERE, a bulk write past the point of easy recovery, a schema change, a drop-and-rebuild of a live index. That is exactly the work you cannot hand to a cron job that has no idea when it is about to do something irreversible.
- A reverse-ETL sync drifts and the warehouse goes stale.
- A malformed file loads and every downstream report is wrong by morning.
- Backfills run long while you watch row counts at 2am.
- A schema change ships without anyone signing off on it.
- The one destructive write is exactly what a blind cron job should not run.
What you can automate
Each of these runs as a flow. The agent does the extraction, validation, and routine loads on its own. It stops and asks a human at the one point where a wrong write is expensive or impossible to undo. Every tool named here is a FlowRunner connector, built and verified against the vendor's official API.
- 01
Reverse-ETL sync to the warehouse
The operational data lands in the warehouse. The production write-back waits for a human.
TriggerA schedule fires, or a change-data-capture event lands from the operational database.
AgentThe agent reads the changed rows from PostgreSQL, reconciles them against the current state in Snowflake, maps the fields, and stages the delta so the warehouse mirrors the source without a full reload.
Human checkpointReads and staging happen on their own. When the sync would write back into the production PostgreSQL tables, or the reconciled delta is larger than the row threshold you set, it stops and hands the engineer the exact diff before anything is committed.
ResultThe warehouse stays in step with the operational store on every run, and a write back into production always has a named human who saw the diff first.
- 02
Validation before the load
Bad data never reaches the warehouse. The agent quarantines it and asks.
TriggerA new file or batch arrives on the ingestion path, or an upstream job signals it is ready.
AgentThe agent profiles the batch against the expected schema, checks row counts, null rates, types, and referential keys against MySQL, and separates the clean rows from the ones that fail the contract.
Human checkpointClean batches load into BigQuery on their own. When a batch fails validation, or the failure rate crosses the bar you set, it holds the load, quarantines the bad rows, and routes the profile to the owner rather than poisoning the warehouse.
ResultClean data flows through untouched and a bad batch is caught before the load, not discovered three reports later when the numbers already went out.
- 03
Bulk migration and backfill
The migration plans and dry-runs itself. The cutover write waits for a go.
TriggerA migration ticket or a backfill request kicks it off.
AgentThe agent reads the source records from Oracle Database, transforms them to the target shape, runs the mapping against a sample, and reports the row counts, the diffs, and any records that will not map cleanly.
Human checkpointIt never bulk-writes past your threshold on autopilot. The agent stages the full backfill into Snowflake and stops for a sign-off, showing the exact volume and the failed-mapping list, before it commits a write of that size.
ResultA migration that used to mean a hand-watched terminal at 2am becomes a reviewed plan with the destructive bulk write gated behind a human go.
- 04
RAG index build and refresh
The vector index rebuilds itself from source. The drop-and-swap waits for a person.
TriggerSource content changes, or a scheduled refresh of the knowledge base fires.
AgentThe agent pulls the changed documents from MongoDB, chunks and embeds them with your model keys, and stages the new vectors so it can upsert incrementally into Pinecone without touching what is already serving.
Human checkpointIncremental upserts run on their own. When a change calls for dropping and rebuilding the whole index, which takes the RAG feature offline while it repopulates, the agent stops and waits for an engineer to confirm the rebuild rather than swapping a live index blind.
ResultThe vector index tracks the source without a manual reindex, and a full drop-and-rebuild that would interrupt retrieval always has a person who chose the moment.
- 05
Ad-hoc data pull and report
The pull assembles itself across systems. The write to production waits.
TriggerA stakeholder request lands, or a recurring report is due.
AgentThe agent runs the read queries across Redis and PostgreSQL, joins them against Databricks, shapes the result into the requested cut, and drafts the report with the figures and the queries that produced them.
Human checkpointRead-only pulls it completes and delivers on its own. If the request asks it to persist the result back into a production table or overwrite an existing dataset, it stops and shows the target and the payload to an owner before writing.
ResultOne-off pulls stop landing on whoever knows the schemas, and the only step that touches production is the one a human explicitly approved.
The pattern that makes it safe to automate
Data automation fails when a job writes something it should not. FlowRunner's answer is the digital andon cord: the agent runs the pipeline and pulls it the instant a step carries real consequence. It reads, validates, transforms, and stages the routine work on its own. A DELETE or UPDATE without a WHERE, a bulk write past your threshold, a schema or DDL change, a write into a production database, or a drop-and-rebuild of a live index always stops and routes to a person through the channel they already watch, with the diff, the row counts, and the query attached. Read and staging happen immediately; the write that cannot be rolled back waits for a human. That is the difference between automation you trust with production data and automation you do not.
The digital andon cord →What your team gets
No unsupervised destructive writes
An agent never runs a DELETE without a WHERE, bulk-writes past your threshold, or drops a live index on its own. The write that cannot be undone always has a named human behind it.
Bad data caught before the load
Every batch is profiled against its contract before it lands, so a malformed file is quarantined at the door instead of being discovered three reports downstream.
The warehouse stays in step
Reverse-ETL syncs reconcile the operational store and the warehouse on every run, so downstream numbers stop drifting between manual reloads.
Migrations you can review
Backfills and migrations arrive as a dry-run plan with row counts and failed mappings, so the cutover write is a reviewed decision, not a hand-watched terminal at 2am.
Vector indexes that track the source
RAG indexes refresh incrementally from the source database, and a full rebuild that would interrupt retrieval only happens when a person picks the moment.
No coverage gaps
The agent connects to the operational databases, warehouses, and vector stores you already run, every connector verified against the vendor API, so there is no store it cannot reach.
Built for data teams' requirements
The controls you would demand of any automation with credentials to your production databases and warehouses are native to the platform, not add-ons.
Complete audit trail
Every query, every write, the user or agent that ran it, the approver, and the timestamp, recorded and exportable, so you can answer exactly who changed what data and when.
RBAC and SSO/SAML
Role-based access to the platform itself, with SSO and SAML, so who can approve a production write is governed by your identity provider, not a shared credential.
Bring your own keys
Your model providers and your database credentials, your keys. Embeddings and inference run on keys you control, and sensitive rows never route through a vendor's account.
Self-hosted option
Deploy inside your own infrastructure so regulated and proprietary data never leaves your environment on its way through a pipeline.
The complete data and platform stack
Operational databases, warehouses, vector stores, and analytics, from PostgreSQL, MySQL, and Oracle to Snowflake, BigQuery, and Databricks, alongside Pinecone, Weaviate, and the rest of the vector layer. Every connector is built and verified against the vendor's official API, so an agent queries and writes the way the vendor's API actually allows.
Every connector verified against each vendor's official API.
Put your data pipelines on autopilot, safely
$100 in credits. No card required. Every integration here is built and verified against the vendor's official API, with a human in the loop where it counts.