Case Study - Full-Stack Edge AI Product Engineering — Hardware to Market

Results at a Glance

Product Engineering: Delivered the full product stack for an edge AI hardware device — from firmware-level optimization and model tuning to a production-grade product site with interactive performance benchmarks, a 5-year TCO calculator, and investor-facing materials. Unified the engineering, brand, and market narrative into a single cohesive product identity.

Impact: Positioned Luna AI as a credible entrant in the enterprise edge AI market with a vertically integrated product story — custom hardware, proprietary firmware, tuned models, and a market-ready digital presence — ready for investor engagement and early customer acquisition.

The Challenge

Edge AI hardware is a field dominated by established players like NVIDIA. Luna AI had a genuine technical breakthrough — a compact device delivering 400 tokens/second prefill speed on a 1–5W power envelope — but needed to translate raw hardware R&D into a complete, market-ready product.

The specific challenges:

• Hardware-software integration — Custom hardware with an 8-core CPU and 3-core NPU required proprietary firmware optimized for AI inference workloads
• Performance positioning — Demonstrating 6x speed advantage over NVIDIA Jetson Orin Nano required rigorous benchmarking and clear communication
• Cost narrative — The zero-cloud, zero-recurring-cost model needed to be quantified against AWS/Azure alternatives over enterprise deployment timelines
• Investor readiness — Technical capabilities needed translation into market terms: TAM ($24B), unit economics (65–75% gross margins), and regulatory compliance (GDPR/HIPAA)
• Product identity — Moving from engineering prototype to a product with a compelling digital presence and brand

The Solution

BeeNex engineered the complete product stack — taking Luna from hardware prototype to market-ready product with a unified technical and commercial identity.

Hardware-Software Integration

Worked alongside the Luna engineering team to integrate custom hardware with proprietary firmware, ensuring the 8-core CPU and 3-core NPU architecture was fully optimized for AI inference. The tuned software stack delivers 400 tokens/second prefill and 20–25 tokens/second generation for 1B parameter models — all within a 62-gram, 1–5W power envelope.

Performance Benchmarking & Competitive Positioning

Built rigorous benchmark infrastructure comparing Luna against NVIDIA Jetson Orin Nano (67 t/s prefill) and cloud solutions. Engineered interactive visualizations that make the 6x speed advantage immediately tangible, with side-by-side latency and throughput comparisons.

Product Site & Interactive Tools

Designed and built the Luna product site featuring an interactive TCO calculator that lets prospects model 5-year deployment costs against AWS/Azure, a competitive analysis matrix spanning latency, privacy, resilience, and cost dimensions, and a savings calculator for fleet deployments. The site serves both investor and customer audiences with a single, cohesive narrative.

Investor Materials & Go-to-Market Infrastructure

Translated technical specifications into investor-ready materials — addressable market sizing, unit economics modeling, competitive moat articulation, and regulatory compliance documentation. Built the waitlist and investor meeting scheduling infrastructure to support early traction.

Results

Luna AI went from a hardware breakthrough to a market-ready product with a unified identity — custom hardware, proprietary firmware, tuned models, and a digital presence that speaks to both investors and enterprise buyers. The edge doesn't need the cloud. It just needed the right product engineering.