Know whether a training-data change is safebefore you spend GPU money.
SignalLedger is the local-first MLOps governance layer for training-data changes: risk, readiness, compliance evidence, and no-payload proof before the model trains or ships. It runs as a Dockerized GitHub Action and CLI — no SaaS account, no raw datasets, prompts, embeddings, or weights ever leave your environment.
# Install the source-available alpha (git tag)
python3 -m pip install \
"ecl-trainer @ git+https://github.com/Intelligent-Context-AI-Inc/signalledger.git@v0.1.0-alpha.4"
# Local-only, no SaaS account required
ecl-trainer scan --changed-only
ecl-trainer verify-log --ledger-path .ecl-trainer/events.jsonl
ecl-trainer passport --ledger-path .ecl-trainer/events.jsonlTry the alpha in 5 minutes
- 1.Add the GitHub Action.
- 2.Open a PR that changes training metadata.
- 3.Inspect
risk-report.md,compliance-passport.md,verification.json,pr-comment.md, andevents.jsonl. - 4.Add a payload-like field and watch the no-payload gate fail closed.
Pain, feature, and outcome — before the deep architecture
Every risky training-data change gets caught, evidenced, and gated without exposing raw payloads.
Engineered for scale. Validated by the community. No SaaS overhead.
The blind spot in modern LLM development.
You spend weeks filtering web data, curating SFT pairs, and configuring synthetic pipelines. You kick off a massive training run. Weeks later, your model's financial reasoning or compliance accuracy drops by 8%.
The bottleneck isn't the code; it's the memory. Traditional tools show you hardware metrics and raw loss curves, but they treat your data like a black box flat file. You are left running expensive, repetitive data ablation cycles or guessing which specific dataset mix, synthetic cluster, or shifting alignment policy caused the regression.
As AI transparency obligations phase in—including California AB 2013 training-data transparency requirements effective January 1, 2026, and EU AI Act obligations applying across 2025–2026 phases—teams need reproducible evidence of training-data lineage and governance.
The missing link between Data Lineage and Model Behavior.
ecl-trainer is the first developer tool that bridges the gap between what your model eats and how it performs. It hooks seamlessly into your training and testing stacks, creating an immutable, append-only memory of your data supply chain.
Strict No-Payload Policy
We never ingest, read, or upload your raw training text or model weights. The SDK hashes and strips everything locally. Default execution is local-only: no SaaS account, API key, dataset upload, raw payload, prompt, embedding, token sequence, model weight, or raw diff is required or transmitted. Optional enterprise/VPC integrations can consume signed local artifacts under customer control.
Pre-Seeded Intelligence (The Data Atlas)
Skip the first 3 to 5 blind ablation runs. The ledger launches pre-seeded with structural metadata footprints derived from millions of dollars of public open-science training trajectories (FineWeb-Edu, Dolma, DCLM), giving you an optimized starting recipe on Day One.
Closed-Loop Attribution
We link data history directly to your downstream evaluation framework scores (Braintrust, Galileo, custom suites). The ledger automatically isolates which dataset properties correlate with capability improvements or regressions.
A local, metadata-only pre-flight shield before training starts.
A single GitHub PR produces a training-data risk report, compliance passport, hash-chain verification, PR comment body, and append-only ledger — locally. No SaaS account. No dataset upload. No raw payload in the audit trail.
A training-data PR looked like a diff and a reviewer checklist. Risk lived in someone's head, and proof was a hand-written note stapled to a static CI artifact.
The PR becomes a local, cryptographically verifiable training-data risk event. One CI run produces the risk report, compliance passport, verification file, PR comment, supply-chain evidence, and append-only ledger. Reviewers get an actionable signal before GPUs spin up; security teams get proof that no raw payload crossed into the vendor path.
Multi-Industry Sovereignty with Cross-Domain Intelligence.
The ecl-trainer architecture splits the difference between localized domain specificity and universal machine learning mechanics. It runs a dual-layer intelligence substrate that secures your pipeline.
The Global Core Ledger
Always OnTracks universal token-level failure mechanics, loss-spike signatures, structural deduplication markers, and lineage feedback-loop metrics aggregated across 20 distinct industries. If a structural failure vector is discovered in a high-entropy medical corpus, ECL surfaces the cross-domain structural risk signal for your financial model.
Flexible Domain Toggles
User-ControlledActivate or deactivate industry-specific compliance layers at will. Launching with Domain #1: Financial Services, the system instantly configures your workspace to validate against:
- SEC EDGAR & XBRL structural taxonomy boundaries.
- FINRA regulatory compliance documentation requirements.
- Federal Reserve 2026 Stress Test macro-scenario structural metadata mapping.
Shift compliance and data safety left. Automatically.
We wanted the PR experience to feel boring in the best possible way: open a GitHub pull request, and get a training-data risk report without creating a SaaS account, uploading a dataset, or exposing a single raw payload line. So we put the whole thing in Docker. The container builds directly from your repo, embeds a high-performance local DuckDB seed index, runs the data plane scanner inside the checked-out workspace, and emits only local artifacts.
1Run the CLI on changed files
No polished init flow required. The public alpha runs local-only from the CLI or GitHub Action, scanning changed metadata and writing local evidence.
# Local-only, no SaaS account required
ecl-trainer scan --changed-only
ecl-trainer verify-log --ledger-path .ecl-trainer/events.jsonl
ecl-trainer passport --ledger-path .ecl-trainer/events.jsonl2Zero-SaaS Local Execution
The scanner runs completely local-only inside your CI pipeline. It doesn't execute untrusted repository code. It handles real-world CI wrinkles out of the box—like Git safe.directory host/container ownership conflicts—using automated boundary overrides.
It generates raw, machine-verifiable artifacts directly inside your local workspace:
.ecl-trainer/
├── events.jsonl
└── reports/
├── risk-report.md
├── compliance-passport.md
├── verification.json
├── pr-comment.md
├── mlops-governance-pack.md
├── mlops-governance-pack.json
├── catalog-drift-snapshot.json
└── manifest.jsonWhat the SDK records (and what it leaves behind).
These are sanitized examples showing the shape of the local artifacts — without exposing private Atlas rows or raw training data. No dataset rows. No raw diffs. No prompts. No embeddings. No model weights. Just metadata, hashes, policy assertions, and enough evidence for a reviewer to make an executive decision.
### Local Evidence
- SaaS account: not required
- Dataset upload: not performed
- Payload policy: passed
- Ledger verification: valid
- Supply-chain evidence: generated
### Intelligent Context Atlas
- Oracle status: completed
- Active seeded domain: financial_services
- Atlas source records: 65
- Atlas registered domains: 20Add the GitHub PR shield to your repo in 5 minutes.
No SaaS account. No dataset upload. No raw payload. Just a Dockerized GitHub Action that writes local evidence and compliance metrics into every PR.
1Add the workflow
.github/workflows/ecl-trainer.yml
name: ECL Trainer PR Shield
on:
pull_request:
paths:
- "configs/**"
- "data/**"
- "datasets/**"
- "schemas/**"
- "training/**"
- "finetune/**"
- "recipes/**"
- "model_card.md"
- "dataset_card.md"
workflow_dispatch:
permissions:
contents: read
pull-requests: write
issues: write
jobs:
ecl-trainer:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 2
- uses: Intelligent-Context-AI-Inc/signalledger/.github/actions/ecl-trainer-scan@v0.1.0-alpha.4
with:
project_namespace: ${{ github.repository }}
ledger_path: .ecl-trainer/events.jsonl
risk_policy: block_on_payload_violation
domain_selection_mode: explicit
enabled_domains: financial_services
changed_only: "true"
post_pr_comment: "true"2Choose your policy mode
report_onlyAlways report metadata changes, never interrupt pipeline runs.warnFlag structural anomalies, contamination risks, or lineage feedback-loop patterns without halting execution.block_on_high_riskFail the PR check if data mixtures match known failure models or loss-spike signatures.block_on_payload_violationDrop the build if the outbound validator catches raw text or unhashed embeddings crossing the boundary.3What happens on every PR
Pull request opened
↓
Dockerized ECL Trainer scan
↓
No-payload validator
↓
Local risk report + compliance passport
↓
PR comment + GitHub artifact bundle4No account required
Try it on a real PR
Copy one workflow into .github/workflows/ecl-trainer.yml, open a PR that changes training metadata, and inspect the local artifacts under .ecl-trainer/reports/. Start in report_only. Graduate to block_on_payload_violation once the team is comfortable with the signal.
See it fail closed before you trust it.
The fastest way to believe the product: clone the demo repo, open two PRs, and watch the same local pipeline pass a metadata-only change and reject a payload-like one — all without a SaaS account, dataset upload, or raw text leaving your runner.
PR #1 — Safe manifest
safe-manifest.json
Metadata-only change passes the No-Payload Policy. ECL posts a report_only PR comment and uploads the local artifacts.
- Risk report + compliance passport generated
- Ledger event appended to events.jsonl
- verification.json confirms a valid hash chain
PR #2 — Payload-like field
bad-manifest.payload-demo.json
A deliberately unsafe key trips the outbound validator. ECL raises PayloadExfiltrationException and fails closed before writing to the ledger.
- No ledger append — the chain stays clean
- No raw payload, prompts, or weights recorded
- PR check blocks with an actionable remediation list
"We opened a PR with only metadata. ECL generated a risk report, compliance passport, PR comment, verification file, and append-only ledger without a SaaS account. Then we opened a second PR with a payload-like metadata field; ECL failed closed before writing to the ledger."
The No-Payload Policy validates every output before it is written.
The Dockerized action runs inside your CI workspace. It reads safe metadata files, validates them against the local Atlas, writes local reports, appends a ledger event, and verifies the hash chain. It never crosses these lines:
Blocked from every artifact
- Raw training text
- Prompts & completions
- Embeddings
- Token sequences
- Model weights
- Raw diffs
- Dataset rows
- Secrets
What the action never does
- Execute repository code
- Load or inspect raw datasets
- Send metadata to an ECL SaaS backend
- Require customer credentials
Public runtime, private intelligence.
The runtime you install is source-available and runs local-only. The proprietary intelligence that makes the signals sharp stays private.
Public runtime
- CLI
- GitHub Action
- No-Payload validator
- Local ledger
- Reports
- Public / synthetic Atlas scaffolding
Private intelligence
- Curated Atlas packs
- Scoring weights
- Private source update logic
- Customer overlays
- Fabric bridge
Start free locally. Scale into governance.
The local PR shield and learning ledger are available in the source-available alpha. Paid tiers add private Atlas packs, enterprise review workflows, and deployment support.
Pre-Flight Shield
Local Dockerized GitHub Action. Metadata-only risk reports and PR comments on every training-data PR.
Local Learning Ledger
Append-only, hash-chained ledger with compliance passports and verification.json — all generated locally.
Private Atlas Packs
Curated, domain-specific structural metadata packs (starting with Financial Services) for higher-conviction risk flags.
Fabric Admission Bridge
Gate training-data promotion on ledger signals, wiring ECL verdicts into your data fabric admission controls.
Enterprise Audit Bundle
Regulator-ready passport exports, retention policy, and evidence packaging for the EU AI Act and AB 2013.
Air-gapped VPC Tier
Compiled core ledger engine and full data Atlas in obfuscated local binaries that call home to nowhere.
Build models that remember.Protect your training data supply chain.
The GA alpha path is no-account and local. Drop the GitHub Action into any training repo, deploy the PR shield inside your workflow, and generate compliance-support evidence with zero infrastructure overhead.
Looking for Enterprise Sovereignty?
For systemic financial entities, central banks, and institutional AI teams requiring total network isolation, defense-grade data sovereignty, or completely offline execution, deploy our Air-gapped Enterprise VPC Tier. It packages our entire multi-industry data atlas and compiled core ledger reasoning engine into highly optimized, obfuscated local binaries that interface with local PostgreSQL/TimescaleDB environments, calling home to absolutely nowhere.
Request Air-gapped VPC Technical Blueprint