Own your intelligence.
Genysys adapts or builds the model your workflow needs, then our engineers work inside your team until it is deployed and handed over. You keep the weights, the data pipeline and the evaluations.
At the end of every engagement
- Model weightsyours
- Training recipe and data pipelineyours
- Evaluation harness and held-out casesyours
- Deployment, monitoring and runbookyours
- A team of yours that can run it without usyours
Engineering from Templar
Genysys draws on the research and engineering team behind Templar: distributed training at scale, reinforcement-learning infrastructure, and published work on communication-efficient training.
You leave with the weights
Model, data pipeline and evaluation harness are handed over. Nothing stays on our platform.
Measured before it is believed
Every engagement ends with a written comparison of the model you own against the one you started with, on your data, against a bar you set.
Rented intelligence does not know your work, and it is not yours to keep.
A general model gets you most of the way. The last part is your vocabulary, your documents and your rules, and that is where it fails: the clause it misreads, the exception it does not know, the answer that sounds right and is not.
The model changes when the vendor updates it. The improvements you pay for accrue to their product. When the contract ends, you own a prompt library and an invoice.
Meanwhile the knowledge that would make a model reliable is already in your organisation. It is in the filings, the tickets, the case notes and the people who know why the rule exists. Nobody has put it into something you own.
What you end up owning.
The model
Adapted from an existing model or built for your workflow, trained on your tasks, terminology and data. Weights, training recipe and data pipeline handed over. Rights to base models, data and artifacts set out per project.
Fine-tuning and adaptation, or client-specific model development. Training from scratch is scoped separately.
The standard
Before anything is trained, we agree what good looks like: the baseline, the acceptance bar, the held-out cases that matter to you. The evaluation harness is yours, so you can re-run it when the model, the data or the rules change.
Part of every project. It is how a rented model gets replaced without guesswork.
The operation
Our engineers work inside your team to integrate the model into the systems where the work happens, evaluate it in place, deploy it and support it. On site or remote as agreed. Handover is the deliverable: documentation, monitoring, and a team that can run it without us.
Forward-deployed engineering.
Where success can be measured, reinforcement learning.
If outputs can be checked against a reliable standard, an engagement can set up the evaluation and reward first, then test whether reinforcement learning beats the starting model or plain fine-tuning. Pilots are arranged case by case once that readiness is confirmed.
How an engagement runs.
Dates follow scope, team availability and your readiness. A narrow fine-tune, a model built from scratch and an embedded engineering engagement run to different schedules.
Use case, data access, baseline, acceptance criteria and pilot schedule, written down and agreed before anything is trained. This is also where we measure whether a general model already meets your bar.
Built on your real data against the agreed use case, so early results are representative rather than a demo. Duration is estimated after inspecting the data and selecting a model.
The model against the baseline in a representative environment, against the bar agreed in scoping. The pilot answers one question: did it meet the agreed quality, correctness and performance criteria?
Deployed, monitored, documented, with a support arrangement agreed in advance. Your team leaves able to run it, re-run the evaluations and extend it.
Do you need your own model?
Often not, and we will tell you. Many tasks are already well served by a general model behind an API. Scoping starts by measuring whether that is true for yours. If a general model meets your bar, we say so and stop there. Owning your intelligence is worth the work when the model has to be reliable on your material, stable under your control, and yours when the contract ends.
The engineering behind it.
Genysys draws on the research and engineering team behind Templar: a team that trains models across distributed infrastructure, builds reinforcement-learning systems, and publishes on communication-efficient training.
That work is why we are comfortable owning the hard parts of adaptation, training and deployment rather than reselling someone else’s platform.
The people on an engagement, their availability and the commercial arrangement are agreed per project.
Questions we get asked.
You do. Weights, data pipeline and evaluation harness are handed over. Rights to base models are set out per project.
Into an environment you control. We work in your infrastructure where required.
That is the point of handover: documentation, monitoring and a team of yours that can operate and extend the model. Support afterwards is agreed in advance.
Then we tell you in scoping and you have lost a few weeks, not a year.
Yes, as a separately scoped project. Most engagements start from an existing model.
Where success can be measured reliably, yes, as a pilot arranged case by case after the evaluation is in place.
Own the intelligence your business runs on.
Tell us the workflow, who owns the outcome, and what good would look like. We reply with questions, not a proposal.
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