Language and long-context memory
27B and LoRA STEP160 experiments; reliability, memory and reproducibility
05 / AI RESEARCH & DEVELOPMENTAI backed by reproducible tests
Beyond model size.
Built for evidence.
We research commodity vision and capable language agents. GPU capacity matters for repeated training, held-out evaluation, failure recovery and multimodal experimentation—not marketing claims.
Discuss research partnership
FIELD DATA × COMPUTE × EVALUATION
01 / THE RESEARCH PROGRAMWhat we need to validate
Separate research tracks.
One accountable system.
Commodity vision and expert labels
Dataset design and held-out lot validation across physical variation
Safe business actions
Authentication, approvals and auditable authoritative state
Independent evaluation
Separate quality, latency, VRAM and failure-recovery gates
02 / RESEARCH STATUSThe state of the work
A completed training run
is not product qualification.
Our 27B experiments and 160-step LoRA training history inform ongoing long-context reliability, GPU memory efficiency, user validation and independent evaluation work.
Recorded experiments27B · LoRA STEP160 · COMPONENT TESTS
Validation in progressVRAM · LONG CONTEXT · OOS · SAFE TOOLS
RoadmapFIELD BETA · MULTIMODAL · CODING AGENT
03 / WHY GPU INFRASTRUCTUREWhy compute matters
GPU capacity helps us
test what matters.
We need repeated trials for commodity visual models, unseen-lot tests and long-context agent performance, memory and tool reliability.
Government GPU programs may be monitored or applied for; we do not imply a confirmed award or allocation.