Redbird AI automates the flow between your cloud storage and code repositories. Stop manually uploading artifacts, tracking dataset versions in issues, or copying release files between S3 buckets and GitHub releases — let AI handle the sync and documentation.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When developers tag a release in GitHub, Redbird automatically snapshots corresponding training data, model files, or configuration datasets in S3 with matching version tags. Every code release gets linked to the exact data version used, creating a complete audit trail without manual tracking in spreadsheets or wikis.
When CI/CD jobs write compiled binaries, documentation bundles, or Docker image manifests to S3, Redbird detects the files and attaches them to the corresponding GitHub release. Release notes stay complete with downloadable assets, and teams don't manually upload artifacts after every build.
When ETL pipelines write files to S3 that fail schema validation or data quality thresholds, Redbird opens GitHub issues in the data engineering repo with error details and S3 paths. Data teams triage pipeline problems where they manage code, not buried in logs or Slack threads.
When PRs merge into production branches, Redbird automatically pushes trained models, feature engineering scripts, and inference configs from the repo to designated S3 paths. Deployment pipelines always pull from the correct S3 location without manual file transfers or version mismatches.
When repos hit major version milestones or security scanning detects vulnerabilities, Redbird automatically backs up complete repository archives to S3 with metadata tags. Compliance and audit teams get timestamped snapshots without running manual git exports or writing backup scripts.
When data teams update schema definitions, API specs, or data dictionaries stored as JSON or YAML in S3, Redbird automatically opens PRs in the docs repository with the changes. Documentation stays synchronized with data infrastructure without engineers manually copying definitions between systems.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Amazon S3 and GitHub with OAuth or API credentials. Redbird never stores your data — it just passes through.
Tell Redbird what to do in plain language — no SQL, no code, no configuration files required.
Redbird shows you exactly what it will do before running anything. Approve the workflow, set a schedule, and switch it on.
Workflows run on your schedule or on triggers. Every run is logged. Adjust with natural language at any time.
Redbird understands S3 bucket structures, object metadata, and file patterns alongside GitHub's repository structure, branch strategies, release workflows, and issue tracking — connecting storage infrastructure with development workflows.
Redbird's AI interprets S3 object keys, prefixes, tags, and metadata alongside GitHub's commit history, PR context, issue labels, and release notes. It understands when a CSV in S3 relates to a data pipeline in a GitHub repo, when model files should be versioned with code releases, and when data quality failures need engineering attention. No hardcoded path mappings or brittle scripts — Redbird learns your naming conventions and workflow patterns automatically.
faster than writing Lambda functions and GitHub Actions workflows to sync artifacts and datasets
Redbird can pull from Amazon S3 and GitHub simultaneously, merge the results, and format a polished report — sent on a schedule or on demand.
Set conditions in natural language. Get notified in Slack or email the moment a threshold is crossed in either Amazon S3 or GitHub.
SOC 2 Type II certified. Data flows encrypted in transit and at rest. Fine-grained permission controls with full audit logs.
Push data from Amazon S3 into GitHub, or from GitHub back into Amazon S3. Resolve conflicts with configurable merge rules.
Every workflow run is logged — what ran, what changed, and why. Replay or revert any individual step at any time.
Start automations from any S3 upload, bucket change, or GitHub commit, PR, release, or issue event.
Triggers when files are added to specific S3 buckets or prefixes, filtered by file type or metadata tags.
Fires when S3 object tags change, useful for tracking data quality status or processing stage transitions.
Detects when S3 creates new object versions, enabling dataset snapshot tracking alongside code versions.
Writes files to S3 with custom tags, metadata, and storage class settings based on GitHub events.
Replicates artifacts or datasets across S3 buckets when code reaches specific deployment stages.
Applies metadata tags to S3 objects linking them to GitHub commit SHAs, release versions, or PR numbers.
Triggers when teams create new releases or version tags, enabling automated artifact archival to S3.
Fires when PRs merge to production, staging, or main branches to sync model files or configs to S3.
Detects when CI/CD pipelines finish, capturing build artifacts, test results, or deployment manifests for S3 storage.
Opens GitHub issues automatically when data quality checks fail, attaching S3 paths and error context.
Adds compiled artifacts, documentation bundles, or deployment packages from S3 to GitHub release pages.
Creates PRs in documentation repos when schema definitions or API specs change in S3 data catalogs.
Sync Amazon S3 and GitHub in minutes. Redbird AI handles the complexity of linking cloud storage with code repositories, so your team can focus on shipping features and building data pipelines instead of writing integration scripts.