Models that work
on your own facts
We put large language models inside products that already run — an in-world narrator whose canon lives in the database, and biomarker analysis that becomes a personal roadmap. Two of our own platforms depend on this working, which is why we can say what it costs.
We ship AI in our own products first
Lonevi reads genetics, biomarkers and lifestyle into a personalised longevity roadmap, and offers that analysis to clinics as consultations, a REST API and embedded widgets. VARGATES Virtual Worlds runs an LLM as narrator and arbiter over a live information model. Both are ours, both are in production, and both had to solve the unglamorous parts — where the truth is stored, what a wrong answer costs, what an interaction costs per user.
That is the whole argument for hiring us on this rather than a team whose AI work has never had to survive its own users.
What we deliver
In-world narration and arbitration
A model that narrates a scene and rules on what a participant just tried to do, in the moment. Running in VARGATES Virtual Worlds, where it drives the text-quest layer over a live information model.
Canon in the database, not in the model
What is true and what has happened is stored and versioned in your data, and the model reads it. That is the difference between a system you can audit and a chatbot that improvises history.
Reading measurements into a plan
Genetics, biomarkers and lifestyle turned into a personalised roadmap rather than a page of values — the analysis layer behind Lonevi, our own longevity platform.
Delivered as API and embedded widgets
The same engine offered three ways, as Lonevi is to clinics: consultations in the product, a REST API for your backend, and widgets embedded in a site you already run.
Training content that answers back
Our XR training scenarios are scripted; a model on top turns a fixed script into a conversation that reacts to what the trainee actually did. Built on the simulation work the other five practices deliver.
Model-agnostic by configuration
The provider is a setting, not an architecture. Virtual Worlds runs with the narrator model configurable, so a change of vendor, price or hosting is a config change rather than a rewrite.
From a decision to a system that keeps it
Five phases, and the first one is not technical. Most failed AI projects were never attached to a decision anyone was making.
Find the decision
Which judgement is being made by a person today, and how it would be checked afterwards. A model without a decision to serve is a demo, and demos are where most AI budgets go to die.
Fix the ground truth
What the model may treat as true, where it is stored, who edits it, how it is versioned. This is the step that separates Virtual Worlds' canon from a system that quietly invents its own history.
Prototype against your data
A narrow slice, running on your real records rather than a curated sample, with the failure cases written down before anyone is impressed by the successes.
Integration
Into the product: API, widgets, or in-world. Latency and cost budgets set explicitly, and the provider kept swappable by configuration.
Operation
Logging what was asked and answered, tracking spend per interaction, and reviewing the cases the model got wrong — because the second month is what decides whether it stays.
Our AI stack
Chosen per project against latency, cost and where the data is allowed to live — not fixed to one vendor.
Where this lands first
What would you want a model to judge?
Tell us the judgement and where the facts behind it live. We will say what is feasible, what it would cost per interaction, and what we would refuse to promise.