Throughput
0 tok/s
Unovie is an AI-engineering studio. We design, build, and operate custom edge-AI systems — fixed scope, fixed cost, on hardware you own. From architecture to production, we accept full responsibility.
Not advisory decks. Working systems — assessed, designed, built, optimized, and supported within committed timelines.
Reference architecture & solution blueprint, scoped to a fixed outcome.
A graded proof against your data — value proven before you commit further.
Production-shaped build: data, models, testing, monitoring.
Hardened, observed, and operated — on hardware you own.
Self-learning loops, skill optimization, and frozen-base adaptation that compounds accuracy on your tasks — without retraining risk.
Small models on Blackwell silicon. PLE-safe NVFP4 quantization, unified-memory budgeting, ~4× decode throughput.
Capex, not a metered bill. Marginal query cost ≈ electricity; accuracy compounds while cost stays flat.
Gated, reversible, regression-safe deployments. Every self-improvement is logged, scored, and revertible.
Agentic edge-AI blueprints with quantified goals — deployed in weeks, not quarters.
Edge-AI fleet monitoring & predictive maintenance.
VIN-level twin for connected-EV fleets.
Account scoring, leakage & auto-drafted travel deals.
Automated inspection & quality control on the line.
Inventory management & real-time tracking.
Risk analytics with real-time alerting.
Real-time parameter monitoring.
Chemical & pharma batch processes.
Real-time expert guidance to teams.
A typed graph + vector memory of your domain that grows continuously, grounds every answer in source records, and supplies the reward the self-learning loop optimizes against. Accelerated on TPU · NPU · GPU, where the data is.
Full responsibility, committed timelines — every phase with enterprise-grade execution.
Workshops, ROI, value outcomes.
Roadmap, reference architecture, blueprints.
Data, models, testing, monitoring.
KPI tracking, feedback loops, reviews.
Enablement & ongoing adoption.
How AI models actually learn — gradient descent, backprop — and why fine-tuning is the wrong reflex on the edge. 25 chapters, diagrams, real results.
The frozen-base doctrine: keep the weights fixed, adapt in context, verify against a frozen baseline — and let throughput compound the gains nightly.
Turnkey Edge-AI — fixed time, fixed cost, full responsibility. Tell us the outcome you need; we'll engineer the path to production.