On-premise AI appliances for manufacturers and industrial enterprises in Southeast Asia. Your data stays inside your facility. Your senior engineers' judgment becomes a company asset, not a single person's.
Industrial engineering teams today are where factories were forty years ago. The judgment lives in the senior engineers' heads. When they retire, decades of judgment walks out the door.
Up to USD $3–5.5M (S$ 4–7M) of design judgment lost per senior engineer who retires. The succession plans aren't ready. Every CTO in industrial knows this. Few know what to do about it.
Reviews depend on whoever happens to be available. The same manufacturability issues are caught by the same three senior engineers, over and over.
Last quarter's production issues don't make it into next quarter's designs. The NCRs repeat. The scrap repeats. The post-mortem documents sit on a shared drive.
Each senior engineer carries decades of design judgment. When they retire, the company loses an asset that was never on the balance sheet — and never insured.
Today four product lines — OS, Intelligence, Vault, Sphere. The family is designed to grow. New appliances enter as customer engineering workloads evolve. Each appliance is purpose-built, replaceable, and engineered for a five-to-seven year deployed lifecycle.
Identity, workflow, integrations, external ingress. The orchestration layer that connects every other appliance to the customer environment.
AI workloads — co-pilot, AutoReview, manufacturability, cost engine. Industrial-grade GPU. On-box fine-tuning at the highest tier.
Tamper-evident audit, HSM-secured keys, signed engineering records. FIPS 140-2 Level 3 at Vault-M and above. Customer-owned retention.
Browser-streamed multi-user design review of complex assemblies. Real-time co-presence for distributed engineering teams.
Every output traces back to the engineering record that produced it. No hallucination on safety-critical work. Models run on-box, fine-tuned on the customer's own engineering history. The intelligence is theirs, owned, and inspectable.
Models train on the customer's own drawings, decisions, NCRs, and standards. The intelligence is fitted to the customer's engineering reality — not a generic baseline. Fine-tuning runs on-appliance at the Full XL tier.
Every AI answer carries citations back to source — the specific drawing, the original NCR, the approved standard. Engineers verify before they trust. No black-box recommendations on safety-critical work.
The knowledge graph holds the customer's structured engineering memory. Models reason against it, not in spite of it. Hybrid symbolic + neural — the right tool for safety-critical work.
Fine-tuned weights ship as customer assets, delivered at contract end if needed. No vendor lock to a proprietary cloud. The customer's intelligence belongs to the customer.
Full XL tier supports air-gap operation. No external network dependency for inference, training, or model updates. For defence, aerospace, and regulated precision customers who can't allow outbound traffic at all.
Every model version is signed, traceable, and reversible. Bring back yesterday's model in minutes. Critical for regulated environments where model behaviour must be reproducible across audits.
Every customer engagement signs three measurable success criteria at the start. Production acceptance happens against ten defined criteria, not subjective satisfaction.
Different industries deploy LogosLab differently — different timelines, different rule packs, different acceptance criteria. These are the three configurations we ship today.
Six-month product cycles. Engineering teams of 75 to 300. Design review cycle time is the gating constraint. AutoReview shipped early gives compounding lift across every revision.
Multi-year product lifecycles. Engineering teams of 30 to 150. Senior engineer judgment is the irreplaceable asset. Knowledge graph + co-pilot make it transferable, queryable, and survivable.
Aerospace tier 2-3, medical CDMO, defence suppliers. Engineering teams of 200 to 500+. Compliance is not a feature — it is the engagement. FIPS-graded vault, signed records, air-gap operation, reproducible model versions.
Appliances sit inside the customer facility, integrated to existing systems. No data egress. Optional outbound for signed software updates only. Air-gap deployment supported at Full XL tier.
Each tier bundles hardware, software, implementation, and first-year support. Standard is the recommended starting point for most engineering organisations. Detailed pricing in the commercial proposal.
Small teams. Knowledge management with light AI capability.
Knowledge management focus. AI design review can wait.
Full platform — AutoReview, manufacturability, cost engine.
Multi-discipline. 3D collaboration. Multi-site capable.
Aerospace, defence, regulated. On-box fine-tuning. Air-gap.
Built by people who have shipped industrial systems before. We are senior engineers and product builders, not consultants. Field Engineers deliver the platform. Founders write the code.
Every claim traceable. We build for citation, audit, and reproducibility — not for confident-sounding answers without provenance. Every AI output cites the engineering record that produced it.
No monoliths, no black boxes. Each appliance is a piece in a larger system, replaceable independently. Customer-owned models, knowledge graph, and rule packs delivered as customer assets.
Structured knowledge isn't built in a quarter. Five-to-seven year deployed lifecycle. Source escrow protects customer continuity. We commit to long horizons — with our customers, our craft, and our own products.
The first three contracted LogosLab customers receive material Year 1 commercial benefit in exchange for a five-year initial term and reference rights. Strategic recognition as a marquee LogosLab reference customer. Influence on product roadmap. First-mover advantage in your sub-vertical.
Walk through what LogosLab would look like for your engineering organisation. Founder-led, NDA-friendly, no slide-flip — we talk about your team, your pain, and what an eight-week deployment would deliver.
Our approach is centered on long-term collaboration with engineering organizations to ensure the platform aligns with real operational, manufacturing, and engineering environments.