Qualtron
4M-context composite model architecture
A composite of specialized small models that compose into a 4M-token working context behind the QAG Engine. Designed to replace the generic LLM tier in regulated generation — where domain precision beats raw scale every time.
Qualtron — waitlist open
Target: later in 2026
Join the waitlist and we will notify you at first availability — including the early-access tier for teams already running Q-Prime or the QAG Engine in a pilot.
Specialists, composed at inference time
A single giant model collapses every decision domain into one set of weights — so the same model that writes a haiku decides which mortgage overlay applies. Qualtron takes the opposite stance: narrow specialists, composed deterministically, under an engine that can prove which specialist is relevant.
Generic LLM
One model, every domain
- Web-scrape training data with uncertain provenance
- Domain drift between prompt and ground truth
- Scaling laws demand billions of parameters for marginal gains
Qualtron
Specialists, composed
- Each specialist trained on licensed, regulated-domain corpora
- QAG signals decide which specialist generates — deterministically
- Domain alignment beats parameter count on regulated benchmarks
The generation layer, by design
Composite architecture
Qualtron is not a single large model — it is a composite of specialized small models that compose at inference time. Each specialist is trained on a narrow regulated domain (policy, contract, regulatory, clinical). The composition enforces deterministic hand-off between specialists so the reasoning chain stays inspectable.
4M-token working context
The composite spans a 4M-token working context — enough to hold the full body of guidelines, investor overlays, and a multi-year loan file (or the analogous corpus in insurance, healthcare, government) in a single reasoning pass.
Designed for QAG
Qualtron is the generation layer of the stack. It plugs in behind the QAG Engine so the seven HSC signals control which specialist speaks on which decision. You get domain precision without the hallucination tax of a general-purpose foundation model.
Regulated-domain specialization
Each specialist model is trained with regulated-domain objectives — compliance fidelity, contract reasoning, investigator-grade narrative. Generic-LLM scaling laws do not apply the same way: domain alignment outperforms raw parameter count on the benchmarks that matter to regulators.
Replaces the generic LLM tier
In a classical stack, you pair retrieval with a general-purpose LLM and hope it stays on-policy. In the QGI stack, Qualtron replaces the generic LLM tier with specialist models that the QAG Engine can prove are relevant, non-conflicting, and within coverage for the decision at hand.
License-safe by construction
The specialist mix and training data are designed to be regulated-industry clean — no uncertain web-scrape provenance, no unlicensed commercial content, no brittle opt-out compliance. The result is a model you can ship into credit, claims, and underwriting without a legal exposure footnote.
What teams evaluating Qualtron usually ask.
What is Qualtron?
How is Qualtron different from a standard large language model like GPT or Claude?
Why a 4M-token working context?
How is Qualtron trained, and is the training data license-safe?
When will Qualtron be available?
Want Qualtron in your evaluation early?
Teams already piloting Q-Prime or the QAG Engine get first access. Join the waitlist and tell us what you plan to generate — we will tune the specialist mix to the workflow.