Stop renting the modelyour business runs on.
Xantyr turns your company’s data into a specialised model you own outright, fine-tuned for one job, running on your hardware or a server that costs a fraction of your API bill.
From raw data to deployed model. No ML team required.
Renting intelligence has a price tag. Here it is.
The AI that works is the AI you can't afford to scale.
of enterprise AI budget now goes to inference
24x growth in consumption projected by 2030
The AI you can afford doesn't know your business.
of enterprise GenAI pilots show no P&L impact
only 6% convert AI spend into real financial impact
And every fix sends your data somewhere else.
name data privacy as the top barrier to LLM adoption
a third of employees leak company data anyway
One pipeline. Data in, owned model out.
Structure the data
Point Xantyr at what you have: documents, exports, tickets, logs, transcripts. It cleans, structures and labels them into a training-ready dataset, and flags what's missing before you waste a training run on it.
Fine-tune the model
Choose an open-weight base, define the task, train. Evaluation runs against your real work, not public benchmarks. Iterate until it clears your bar. No notebooks, no cluster config, no MLOps hire.
Deploy it anywhere
Download the weights and run locally or air-gapped. Or push to a managed low-cost instance. Export any time. The model is yours as a file, not as a subscription.
What ownership actually costs.
| Monthly spend | Rented cost | Owned cost |
|---|---|---|
| $0 | $0 | $350 |
| $10K | $10K | $382 |
| $20K | $20K | $414 |
| $30K | $30K | $446 |
| $40K | $40K | $478 |
| $50K | $50K | $510 |
| $47K | $47K | $500 |
Rented
- Per-token pricing, scales with usage, permanent
- Data processed by a third party
- Model version and pricing controlled by the vendor
- Zero residual asset
Owned (Xantyr)
- Fixed training cost, then fixed hosting
- Data stays in your environment
- Model version frozen until you change it
- A portable asset that appreciates as your data grows
What’s under the hood.
Model-agnostic by design
Base model chosen for your task and sovereignty constraints, not our margins. Swap it later without rebuilding your dataset.
Evaluation on your work
Accuracy measured against your actual tasks and edge cases. You ship when it beats your current process, not when a leaderboard says so.
Continuous capture
Your model doesn't freeze at launch. New data from live operations flows back in; retrain on your schedule.
Deployment optionality
Local, on-prem, air-gapped, private cloud, or managed. Weights exportable, permanently. No lock-in. We'd rather earn the renewal.
Governance built in
Full lineage on what data trained what model version. Auditable by design, because regulated buyers asked first.
The four profiles where this pays for itself fastest.
High-volume, narrow tasks
Classification, extraction, routing, review, structured output. Highest accuracy gain, biggest cost collapse. If you run one task 10,000+ times a month, this is arbitrage.
Restricted data
PHI, PII, financial records, privileged material, classified work. The workloads currently blocked from AI entirely. Ownership is the only version of this that's legal for you.
Deep-domain firms
Where the expertise is the product. Your archive becomes a model that carries your house standard and doesn't resign.
Small teams and solo builders
No ML hire, no GPU cluster, no DevOps. Bring data, leave with a model file you can run on your own machine.
The questions everyone asks.
Most teams have far more than they think. It's just unstructured. That's step one, and we tell you honestly if you're short before you spend anything.
Own the intelligence your business depends on.
Cheaper to run. Better at your job. Impossible to leak.