▰PipelineWerksBook a working session
THE DIGITAL TWIN FOR YOUR DATA STACK

Fix data pipeline failures.
Faster. With evidence.

See the failure. Follow the evidence. Test the next move.
Get from a broken pipeline to a fix your engineers can trust.

Break a pipeline. Try the twin.Bring us your hardest incident
Built around the modern data stackdbt❄ SnowflakeGitHubFivetranAirflow
A SMALL FAILURE. A BIG RIPPLE.

Don’t watch the demo. Run it.

Break it. Understand it. Bring it back.
All in about a minute. No signup.

Commerce pipelineINTERACTIVE SANDBOX
Flowing normally
Pipeline health100%
Affected assets0 / 10
Oldest data2 min
Next delivery deadline10:30 AM
Your pipeline, in motion Healthy Failed Downstream risk
SOURCES
INGESTION
WAREHOUSE
TRANSFORM
BUSINESS
10:00 · healthy10:08 · change11:00
Everything is flowing. Inject a failure to see how one change travels through the stack.
✧ Ask the twin
Guided questions in this demo
Simulated data · Browser-based model · No connections to your systems
UNDER THE HOOD

A working model.
Transparent assumptions.

The demo propagates failures through an explicit dependency graph. The time slider replays a source change at 10:08, detection at 10:10, and downstream impact at 10:12.

Forecasts run 2,000 seeded Monte Carlo trials in your browser. Each samples repair success, repair duration and downstream refresh time. The deadline is 10:30. Your response delay, chosen intervention and pipeline load change the results.

Recovery ranges are the 10th–90th percentiles. No-action trials assume no recovery within the next two hours. Unsuccessful repairs also exceed that horizon. These are illustrative assumptions, not calibrated customer predictions.

The repair checks actually run against a small synthetic dataset. No AI service, GitHub repository or production system is connected.

ONE TWIN. THE WHOLE PATH TO RECOVERY.

The alert is the beginning.
Recovery is the job.

Your tools hold the logs, code and lineage.
PipelineWerks brings them into one reasoning workflow, with your engineers in control.

01 / SEE
● ━ ● ━ ● ━ ●

See the whole system.

A living map from source to business impact. Rewind the pipeline to the change that started it.

02 / EXPLAIN
EvidenceInferenceUnknown

Know why you believe it.

Connect errors, deployments and dependencies. Separate observed facts from conclusions and open questions.

03 / PREDICT
╱ ╱ ╱

Try the next move.

Compare waiting, rollback and a targeted repair. Make risk and uncertainty visible before acting.

04 / FIX
− order_status+ status AS order_status

Review. Repair. Verify.

A proposed change with context and validation. Human approval, followed by evidence that the pipeline recovered.

BUILT FOR THE WORK BETWEEN YOUR TOOLS

Less reconstructing.
More resolving.

01

One incident. One shared understanding.

Give engineering, analytics and data leadership the same causal path and business impact.

02

Every recommendation has a receipt.

Keep the evidence, assumptions, code change and decision together. Engineers retain approval authority.

03

The next incident starts with memory.

The product vision turns verified resolutions into reusable context, so teams stop investigating the same failure from scratch.

EARLY DESIGN PARTNERSHIPS

Start with one
painful pipeline.
Prove the value.

For data teams running dbt, Snowflake and GitHub—and the consulting partners who help them succeed.

Book a 30-minute working sessionBring an incident. Leave with a focused pilot plan.
01

Replay a real failure.

Choose one recurring incident. Start with agreed, sanitized logs, a dbt manifest and relevant code context.

02

Work alongside your stack.

Scope access with your team. Begin with a bounded evidence set; expand into read-only connections when justified.

03

Measure the path to recovery.

Agree on investigation time, engineer effort, resolution time and repeat failures. Prove the workflow before expanding it.

The sandbox demonstrates the product direction. Live integrations, forecast calibration and pilot scope are agreed with each design partner.

A few good questions.

Does PipelineWerks replace our observability tools?

It is designed to work alongside them. The focus is the path from an incident to an evidence-backed explanation, a reviewable repair and verified recovery.

Will it change production on its own?

The workflow keeps engineers in control. Changes are proposed for review and approval. This sandbox only changes synthetic data in your browser.

Are these forecasts real customer performance claims?

No. The sandbox computes outcomes from an illustrative stochastic model. A live deployment needs assumptions and probabilities calibrated to your environment.

Can we explore this through our consulting partner?

Yes. A working session can include your trusted consulting or implementation partner, with a focused use case, clear access boundaries and shared success measures.