Know whether a data change is safe for your model—before merge.
Connect GitHub, DataHub, your model evaluator, and Slack. Ahead traces the real blast radius, tests the proposed change, blocks unsafe pull requests, and routes the evidence to the team that owns the risk.
DataHub3-hop lineage
Model runner9,000 evaluations
Slack alert ready · owner, lineage, evidence, remediation
The web app is where engineers review evidence. The actual decision runs inside the repository and returns a GitHub pass/block status.
Four systems. One accountable release decision.
Ahead does not ask you to upload production data into this website. It connects from your CI environment using credentials your team controls.
GitHub
Where the gate runsA pull request changes a semantic contract. Ahead runs as a required GitHub Check and can stop the merge.
- You provide
- Repository, PR diff, workflow token
DataHub
Where context comes fromThe official MCP server returns the real schema, owners, policies, and downstream model lineage for the changed field.
- You provide
- MCP URL or GMS URL + token
Model evaluator
Where impact is measuredYour existing evaluation job, model endpoint, or approved sample runs baseline and proposed inputs on the same observations.
- You provide
- Runner command or model endpoint
Slack
Where owners respondREVIEW and BLOCK arrive as structured Block Kit alerts with the DataHub blast radius, model evidence, GitHub state, remediation, and action links.
- You provide
- Incoming webhook selected for one channel
Your catalog and evaluation data stay in your environment. Only the release decision and approved evidence appear in GitHub, Slack, or the review UI.
Open connection guideWhat an engineer actually does.
Open a pull request
A contract or pipeline change describes a new meaning for a field.
Wait for one check
Ahead asks DataHub who depends on it and invokes the configured model evaluator.
Merge or fix
A safe change passes quietly. An unsafe change blocks GitHub and alerts the responsible Slack channel with evidence and remediation.
$ ahead --real-data --live-datahub --write-backBLOCK · risk 71/100Real records. Trained model. Honest labels.
The proposed unit change is intentionally simulated because it is being tested before release. The observations, target labels, model training, and measurements are real and reproducible.
Default of Credit Card Clients
Created by I-Cheng Yeh and published by the UCI Machine Learning Repository.
- Records
- 30,000
- Features
- 23
- Missing values
- 0
- DOI
- 10.24432/C55S3H
TWD dollars→TWD cents
Same integer schema · value ×10030,000 source records, target labels, trained model, held-out predictions, published checksum.
The proposed dollars-to-cents release, tested before it can reach production.
Replace the included evaluator and sample DataHub graph with your model endpoint and catalog lineage.
- get_entities
- 25 fields + provenance
- get_lineage
- 3 impact hops
- writeback
- tag + document verified
Inspect the run, then reproduce it.
No login and no hidden API. The dataset attribution, model configuration, decision JSON, DataHub integration, and CI workflow are all in the repository.