The integrity & trust layer for AI training data
Prove what your model learned from.
Agentiks sits at the point of intake and judges every source, checks every sample in the embedding space, and signs a tamper-evident record of what entered your model, before it ever trains.
- Source trust at intake
- Per sample, in the embedding space
- Software-only, any cluster
How it works
Everything that has to happen before your data trains, in one gate.
Lineage tools just map it. Observability tools just watch it, after the fact. Agentiks does both. It also judges the source, checks every sample, and signs the record, the moment data arrives, before it can reach training.
Map · judge · check · sign. One pass, before the data lands.
Proof 01 · Source trust
A credit score for every data source.
A live trust score for every place your data comes from, earned over time and able to fade, scored across the signals that matter. So you train on sources you judged, not sources you assumed were fine.
Earned, fading reputation is proven: Spamhaus (email, since 1998) · BitSight · Sift.
Proof 02 · Every sample, at intake
Every sample gets a verdict before it ever trains.
Each sample is examined the moment it arrives and given a clear verdict, let in, hold, or reject, before it can reach training. The decision is binding: if a check can’t run, the sample stays out.
Where bias and drift show up first
Every sample also lands somewhere on a map of meaning, its embedding. We watch that map closely: it’s where bias creeps in and a drifting source shows first, as a point sitting off on its own before any label ever looks wrong.
How we embed
Each sample, text or image, is run through a production embedding model, the encoder, on NVIDIA GPU nodes. That places it in the same representation space the model learns in, inline at intake, so the check is on meaning, not surface statistics.
Proof 03 · Tamper-evident ledger
A record nobody can quietly rewrite.
Every sample gets a unique fingerprint, and each record is locked to the one before it in an add-only log. Periodic Merkle checkpoints roll each batch into a single root, so anyone can prove a given sample is in the ledger, and unchanged, with a short proof instead of replaying the whole chain. Software-only, a PostgreSQL hash chain with optional Merkle checkpoints and S3 Object Lock, verifiable with psql and aws s3.
Merkle-tree audit logs are battle-tested: Certificate Transparency (10.9B certs, every browser padlock) · AWS CloudTrail · Guardtime (Estonia/NATO) · SEC 17a-4 · FINRA 4511.
The Integrity Certificate
The thing you hand the auditor.
Any one proof alone proves little. All three together is the certificate: a signed bundle for every sample and batch, holding where it came from, how trusted the source was, the checks it passed, and a seal proving none of it changed afterward.
source trust + sample verdicts + signature chain = integrity.
We deliver all three.
Built for your seat
Judge. Check. Sign. All three, at the gate.
Frontier & ML platform teams
Source trust at intake, a sub-second binding verdict, and embedding-space drift caught early. A software SDK that runs on any cluster, even air-gapped.
Fraud, credit & trading ML
Adversarial sources scored from behavior alone, with dedup and drift checked on every retrain, before bad data can move the model.
Governance & model-risk
A signed Integrity Certificate per sample and batch, tamper-evident lineage, and data-plane evidence a regulator will accept.