Syntropy · Ground-truth data engine
Ground truth,
delivered.
Every serious AI system is only as good as the ground truth it learns from and is judged against. Syntropy is our engine for producing it — not a labeling tool you operate, but a corpus of verified truth we deliver, configured to your domain and wired into your pipeline.
Why it matters
Training, fine-tuning, and — increasingly — evaluation all depend on a trustworthy record of what the right answer actually is. Producing that record is the unglamorous bottleneck: slow, expensive, and usually stitched together from spreadsheets, one-off scripts, and mismatched annotation tools.
The market gives you two bad options: enterprise labeling platforms you must license and operate yourself, or managed data services with opaque per-project pricing. Syntropy is the third: a schema-driven engine, delivered by the engineers who built it, around your files and your downstream systems.
What teams use it for
- → Golden datasets for evaluating AI systems and agents
- → Training and fine-tuning corpora from your documents
- → Contract and policy review at corpus scale
- → Compliance, coding, and audit ground truth
How it works
You define the shape of the truth. Syntropy captures it.
Three inputs drive everything — the schema is the contract. It shapes the annotation UI, the validation, and the JSON you get back. A new use-case is a new schema, not a new product.
Categories, with definitions
Your label taxonomy — each category with a written definition, so every annotator (human or model-assisted) applies it the same way.
Fields, with types
The structured attributes to capture on every annotation: a date, an amount, a party, a value from a controlled list. Typed fields prevent free-text drift.
Your files
The corpus itself. Syntropy presents the right annotation surface for each file and anchors every label to a precise location in it.
The output is the product: the full annotation set as clean, versioned JSON — ready to drop into a training pipeline, an eval harness, or any downstream system. Document annotation (pages and page ranges) is in production today; text-span and region selection, and time-range annotation for audio and video, are next on the roadmap — one schema across all of them.
Delivered, not licensed
You get ground truth — not another platform to operate.
Configured for you
We encode your domain into the schema, stand up the workflow, and run the setup — you never operate a labeling platform.
Wired into your pipeline
The run doesn’t end at export. Syntropy can trigger your downstream workflow — retraining, indexing, review routing — and we build that integration for you.
You own the output
Versioned, machine-readable JSON, engagement-priced with a fixed scope. No per-label metering, no platform lock-in.
Need ground truth your AI can be judged against?
Tell us about your corpus and what the right answers need to look like. We’ll tell you how we’d encode it, what the engagement looks like, and what you’d get back — no decks.