Your data trains your competitors
Every prompt sent to a frontier API trains the next generation of that model — including your proprietary data, trade secrets, and customer information. You're funding the infrastructure that could replace you.
Lamina Grid builds Adaptable Agents — purpose-built AI that orchestrates at scale, adapts through persistent memory and continuous learning, and automates deterministic workflows. Thousands of thin agents, each earning its keep.
Most AI systems throw a giant model at every problem — and forget everything between sessions. We take the opposite approach: decompose workflows into distinct roles, each equipped with a thin, purpose-built model, then give those agents persistent memory and continuous learning. The result is an Adaptable Agent that gets better with every task — and a fleet you can orchestrate and automate at enterprise scale.
Explore the platform →Frontier LLMs promise capability. They also demand your data, your privacy, and your negotiating power.
Every prompt sent to a frontier API trains the next generation of that model — including your proprietary data, trade secrets, and customer information. You're funding the infrastructure that could replace you.
Lamina Grid deploys entirely on open-source models running on your infrastructure — on-premise, VPC, or air-gapped. Your data never leaves your environment. We fine-tune thin models on your proprietary data, so your knowledge stays yours.
Open-source models self-hosted at 65× lower per-token cost. Thin architectures use 2–7× fewer tokens per conversation. Savings compound while your data stays behind your firewall — no trade-off between cost and security.
Custom AI models designed for one thing: doing more with less.
The Kubernetes of thin AI agents. Create, manage, and steer thousands of concurrent agents on a unified grid through a single command surface — the Lamina Grid Portal. Each agent purpose-built, all at linear cost.
Agents that get better with every task. Persistent memory preserves durable knowledge across sessions, and continuous learning sharpens each decision — so your agents adapt to your workflows and compound in value over time.
Recurring work on schedule, from reviewable, versioned instructions. Standing orders and an in-process scheduler make autonomous operation auditable — not a black box.
Proprietary fine-tuning that compounds savings — 65× per-token from self-hosted inference plus 2–7× fewer tokens per conversation. Total: 128–462× cheaper than in-context frontier models (Dennis et al., 2026).
Models trained on your industry data — property listings, product catalogues, customer queries — so they speak your language from day one.
We analyse your current AI spend and pipeline, identify inefficiencies, and deliver a roadmap to cut costs 40–60%.
Whether you're running a marketplace, a SaaS platform, or a traditional business exploring AI — we'd love to hear from you.