From October 8–10, LBAI is exhibiting at NexTech Week in Tokyo (Booth 44-71) and will deliver a keynote on October 10, 12:00–12:45: “Evolving into Future-Ready Enterprises—Driving Frontline Transformation with AI Post‑training.” Over the first two show days, LBAI’s booth has seen sustained, high engagement from advanced manufacturing, finance, healthcare, semiconductor, and energy leaders. Onsite, LBAI is showcasing its latest “AI Post‑training + Agent‑Native Closed-Loop Automation” solution and previewing field‑proven results with KONE Elevator across three key areas: compressed design cycles, optimized component sourcing, and knowledge-to-AI asset conversion—enabling week‑level delivery of AI‑native applications with auditable ROI.
Onsite observations: from “Can it work?” to “How to go live safely and fast”
Sharper demand profile: Most enterprises have moved beyond “is it usable?” to “how do we go live quickly within compliance boundaries,” “how do we make ROI auditable,” and “how do we use post-training to precisely fill scenario gaps and evolve week over week.”
Cross-industry concerns are highly aligned:
Manufacturing/Semiconductor: multi-SKU scheduling, quality inspection, equipment health, and supply-chain resilience; emphasis on on-prem/private deployment and non-disruptive upgrades.
Finance/Healthcare: data sovereignty, residency, and auditable compliance (APPI/GDPR/CCPA/HIPAA); black boxes are unacceptable.
Energy/Public Utilities: cross-system data integration, real-time command with security governance; rollbacks and accountability are must-haves.
LBAI booth highlight: Many visitors booked 1:1 deep-dive sessions after the demo, focusing on “how to compile natural-language requirements into governed applications and drive SFT and private fine-tuning in a closed loop.”
Core solution: Agent-Native closed loop, week-level delivery, auditable ROI
Natural language to governed apps (GELA, formerly wei-pro): Business users describe needs in natural language; the agent team plans → generates code and UI → validates → deploys. Strict JSON contracts, tool whitelists, “one-fact-two-views” receipts (HTTP response + SSE timeline), and DOM-patch idempotency ensure control, rollback, and auditability. ActivationPack enables Zero-Touch alignment across schemas/tables, page bindings, and versions for rapid replication across business lines.
Gap-driven SFT (wei-plus): Identify model “gaps” from real app usage and auto-synthesize precise SFT data pairs—train only what’s needed. Each training round is gated by baseline benchmarks, red-team checks, and regression tests; ship only when criteria are met.
Private Llama fine-tuning for enterprises: Training/inference/deployment run entirely within the customer’s private environment to meet APPI/GDPR/CCPA requirements; LTS/Canary routing with one-click rollback ensures production stability and controlled upgrades.
End-to-end observability and compliance audit: Four-stream logs, versioned manifests, and deterministic receipts build an “evidence chain” suitable for CIO/CISO review.
Onsite demo and validation with a leading elevator company: three proven outcomes
Shorter design cycles: Agent orchestration + post-training significantly compress design iteration, achieving week-level version evolution.
Optimized parts procurement: Cross-system data is integrated within compliance boundaries to improve procurement recommendations and inventory matching accuracy.
Knowledge into AI assets: Codify tacit know-how into “AI knowledge” that evolves with business KPIs, enabling auditable ROI.
Five key questions the October 10 keynote will answer
Making ROI auditable and reproducible: KPI-anchored iteration with benchmarking/regression gates—how to quantify improvements and form an evidence chain.
Operationalizing compliance and data sovereignty: Practical paths for private deployment, tool whitelists, write/delete confirmations with optional preflight, and versioned receipts.
From months to days: How ActivationPack and Zero-Touch minimize integration complexity and accelerate replicated rollouts.
SFT that “trains only what’s needed”: Scenario-first data selection and automated SFT pair generation to reduce budget and lift outcomes.
Production continuity and self-healing: How LTS/Canary routing, a kill-switch, and DOM-patch idempotency enable zero-downtime optimization.
Voices from the floor (selected)
Dr. Hashiba (Founder, LBAI Japan): “We emphasize ‘governance first’ and ‘closed-loop evolution’: enabling enterprises to generate and continuously improve production-grade AI applications within compliance boundaries—backed by an evidence chain for auditable ROI. This aligns strongly with Japanese customers’ high standards for quality and privacy.”
Dr. Qian Wang (Senior Advisor, North America & Investments): “Post-training turns ‘real business gaps’ into small, precise datasets and upgrades, each tied to KPIs—converting model gains into measurable business outcomes.”
Another invitation: lock in the October 10 keynote and a 1:1 booth demo
Talk title: Evolving into Future-Ready Enterprises—Driving Field Transformation with AI Post-training
Time: Friday, October 10, 2025, 12:00–12:45
Book a 1:1 session at Booth 44-71 to get an industry-tailored plan, reference architecture, and rollout checklist. Bring your specific scenarios or data-architecture questions—we’ll provide targeted answers and action recommendations during the talk and onsite meetings.
Key Information
Event: NexTech Week
City: Tokyo
Dates: October 8–10, 2025
Venue: Makuhari Messe, Japan | Tokyo Big Sight (subject to the organizer’s notice)
LBAI Booth: 44-71
LBAI Keynote: October 10, 2025, 12:00–12:45
Media contact: service@lbai.ai
About LBAI LBAI is an enterprise AI-native app factory that compiles natural-language business intents into runnable, reversible, and auditable production applications. Using strict contracts and agent teams, LBAI delivers Zero-Touch deployment and closed-loop self-evolution. Core products include:
GELA: Transforms natural-language requirements into “governed” code and UI with auto-deploy—featuring strict JSON contracts, “one-fact-two-views” receipts, DOM-patch idempotency, and tool whitelists.
wei-plus: Converts live application “gaps” into precise SFT data pairs to drive private Llama fine-tuning and controlled releases—“train only what’s needed” for targeted gains.
Enterprise private model training: Training/inference/deployment run within the customer’s private environment, with LTS/Canary and one-click rollback to meet APPI/GDPR/CCPA requirements.