Orchestrate Data, Code & Agents End to End.

Build, deploy, and govern agentic data pipelines — from ingestion through transformation to warehouse load — with human review baked in at every step.

Request Access See How It Works

Check our quality plan against the checklist, send critical gaps to a reviewer, and publish an action plan.

  1. Read the planget_planDone
  2. Check 64 requirementscheck_requirement [64]64 of 64
  3. Reviewer signs offreview_criticalApproved
  4. Publish the action planpublish_reportDone

Built from one request, shipped as v12, and run in 7m 12s.

Watch a Pipeline Go from Prompt to Production

Build it with the AI Assistant, ship it through CI/CD, then run it with a reviewer in the loop.

Product walkthrough, about a minute, in two parts. Part 1, Build: a new pipeline called Quality Gap Analysis is created and described to the AI Assistant, which plans 11 tasks and writes six files. The files are reviewed and accepted, pushed to GitHub with a generated commit message, and deployed through four CI/CD stages as version 1. Part 2, Run: the pipeline is triggered, 64 requirement checks run in parallel, four critical gaps wait for a reviewer, who approves them, and the run finishes with a published action plan.

Describe the Pipeline.
The AI Assistant Writes It.

Tell the assistant what the pipeline should do. It plans the tasks, then writes the code, the Dockerfile, and the requirements.

  • Plans Before It WritesIt lays out every task and how they connect, then writes each file.
  • You Review Every FileAccept or reject each change, or edit it in the built-in editor.
  • Start from GitHubImport an existing repository and keep building on it.

Deploy with CI/CD.
Every Build Gets a Version.

One click runs the whole delivery pipeline: upload the code, sync GitHub, build the runtime, and hand the pipeline to its owner.

  • Push to GitHubCommit to your branch with a message the AI writes for you.
  • Four Tracked StagesUpload, GitHub sync, runtime build, and access, each with its own status.
  • A Version for Every BuildEach build is numbered, timed, and kept, so you can see what ran when.

Watch Every Run, Task by Task

See each task as it runs. Requirement checks fan out in parallel, and critical gaps wait for a reviewer before the run finishes.

Example run of quality_gap_analysis, version 12. Three ingest tasks fetch the checklist, the quality plan, and the SOP library. Text is extracted from all three, 64 requirement checks run in parallel, and the gaps are scored. Minor gaps are accepted automatically; four critical gaps wait for a reviewer, who approves them. The action plan is then built, published, and sent to the owners. Every task succeeds, in 7 minutes 12 seconds.

Trace Any Value Back to Its Source

Pick a column to see every step it went through, back to the page it came from.

invoices_etl · lineagerun #4,182 · completed · 0:30
finance.invoices
Traced back fromfinance.invoices.amount
  1. 1
    Found
    Total due: 1,24O.00 USD

    invoice_0412.pdf · page 2

  2. 2
    Readheld → approved
    1,24O.00 → 1240.00

    The "O" looked like a zero. Confidence 0.71, needs 0.82.

  3. 3
    Checkedpassed
    1240.00

    Matches the sum of the line items.

  4. 4
    Loaded
    finance.invoices.amount = 1240.00

    Run #4,182 · 09:41

You Control Sensitive Data

Sensitive data is tagged the moment it's read. Masking and access rules apply before anything is stored.

Personal Data Hidden

Names, emails, and IDs are found and hidden automatically.

Full Audit Log

Every agent decision, human review, and rule change is recorded with a timestamp. Export or query it anytime.

Access by Role

Choose who can edit rules, approve changes, and see hidden data.

Your Rules, Always

Your team sets every masking and access rule. ThinkTrends handles the infrastructure so you never have to.

Security and Compliance Built In

Reading, review, and loading all run on one secure, hosted platform. Every step is recorded.

Certified

HIPAA-compliant
Built to handle protected health information.
ISO/IEC 27001 certified
Information security that's independently audited.

Protected

Hosted by ThinkTrends
No infrastructure for your team to set up or run.
Sensitive data tagged on arrival
Personal fields are masked before anything is stored.

Provable

Every value traced to its source
Each value links back to the page it came from.
A permanent log of every action
Every agent step and review is recorded and can't be changed.

Frequently Asked Questions

Common questions about Agentic ETL Pipelines.

What makes an ETL pipeline "agentic"?+
Agents do the reading. Rather than a parser written per source, ThinkStack agents infer structure from PDFs, scans, forms and semi-structured feeds, so a pipeline adapts to a messy source instead of breaking on it. When a layout or feed changes, the pipeline proposes the schema update and keeps running rather than failing overnight.
Do we have to replace our warehouse or write new parsers?+
Neither. Validated records land in your existing warehouse and vector store with lineage and PII classification attached. Pipelines are declared in plain Python — extraction, schema inference and matching are agent tasks you configure rather than parsers you write — and versioned, reviewed and deployed like any other code.
What stops a bad record reaching downstream tables?+
Inline validation. Rule-based and model-based checks run as part of the pipeline, and failures quarantine the record rather than poisoning the table. Every extracted value carries a confidence score, and anything below your per-field threshold is held — not loaded, and not guessed at.
Where does a human come in?+
On low-confidence records only. A reviewer sees the source page beside the proposed value, with the agent's note explaining the discrepancy, and approves, corrects or rejects it in one click. Each decision feeds back into the rules, so the same question is asked less often. Schema changes require approval before taking effect.
Can we prove where a value came from?+
Column-level lineage back to the source document. Click a cell in the warehouse and trace it to the page, the region and the extraction confidence it came from. Every run keeps its inputs, outputs and agent decisions, and any run can be replayed against a new rule set. Every agent decision, schema change and review action is recorded and exportable.
How do we start?+
Three steps. Point it at your sources — systems, drives and feeds — and agents profile them and propose a schema. Set the rule set: validation rules, confidence thresholds, and where low-confidence records route for review. Then validated records load downstream with full source lineage and classification attached.