Thin, specialised models that match frontier quality at a fraction of the cost — from audit through orchestration, adaptation, and automation.
Six phases from audit to autonomous operation.
Analyse AI pipeline — model choices, spend, latency, accuracy — plus operational workflows across HR, Admin, Development, and Design. Output: a prioritised roadmap targeting 40–60% cost reduction by replacing general-purpose LLMs with purpose-built thin models.
Break workflows into distinct agent roles, each with a narrow scope and a purpose-built model. Every agent is independently deployable and improvable. Domain-specific data trains each model from day one — catalogues, queries, compliance rules, or clinical codes.
Agents go live in your existing infrastructure with zero data leakage. All models run on your hardware — on-premise, VPC, or air-gapped. No third-party APIs, no training on your data. Each agent learns from every task, compounding accuracy without manual retraining. As demand grows, agents simply multiply — costs scale linearly, not exponentially.
The orchestration layer — Kubernetes for thin AI agents — handles creation, deployment, steering, monitoring, and retirement of thousands of agents across a single grid. One control plane, the Lamina Grid Portal, to route tasks, track performance, and optimise every agent in real time.
Turn every agent into an Adaptable Agent. Durable decisions, conventions, and lessons from real sessions are harvested and consolidated into a knowledge base, then injected into every turn — alongside continuous learning. Your agents stop re-deciding the same architecture and re-diagnosing the same issues; instead they compound in capability.
Run the recurring work on schedule with standing orders and an in-process scheduler. Instructions are plain, versioned, and fully reviewable — so autonomous operation is auditable, not a black box. When a trigger is missed, re-issue it with one command.
Five operational layers, from AI infrastructure to human workflows.
Model architecture, token spend, latency, accuracy benchmarks, hosting. Identifying where thin models replace frontier LLMs.
128–462× cost reductionRecruitment screening, onboarding, employee queries, performance reviews. Automating repetitive HR workflows with specialised agents.
Hours saved per week per teamInvoicing, reporting, compliance docs, scheduling, data entry. Agents handle structured admin tasks end-to-end.
40–60% cost reductionCode review triage, ticket classification, doc generation, test creation, bug analysis. Reducing developer overhead with lightweight agents.
Faster release cyclesAudit of data flows, vendor risk, model supply chain, and regulatory alignment. Identifying where frontier LLM usage exposes sensitive data and recommending on-premise alternatives.
100% data sovereigntyOur own platform in production — plus the industries we deploy into.
Vibe Property turns Singapore property data into scroll-stopping, data-driven content — generated by a deterministic template engine on top of our orchestrated agents. Agents pick a template, set parameters, and the system produces a provenance-tagged, data-backed output. Zero prompt-burn — accuracy from code, narration from thin models. Proof that our platform ships real enterprise products, not just demos.
50,000+ listings daily by role-specific agents — compliance, image quality, pricing. 70% fewer tokens than a monolithic approach.
Identity verification, risk scoring, policy matching as independent agents. Dramatically reduced error rates. Retains institutional knowledge beyond staff tenure.
55 decision nodes, 6 hubs compiled into one 8B model. Failure rate 17% → 9%. Cost: <$0.001/claim. Runs on local hardware — no PHI leaves site.
Per-seller pricing, per-category inventory, moderation pool — each agent <$0.001/decision. Linear cost scaling at any volume.
Collaborative agents screen filings, evaluate financial health, track sentiment — encoding proprietary criteria. System retains knowledge across personnel changes.
Tell us about your current AI stack and we will recommend the right approach — whether a full audit, a targeted deployment, or a proof of concept.
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