Case Study - Edge AI Software & Inference Pipelines

Results at a Glance

Product Engineering: Delivered the complete software execution stack for local AI inference, from compilation-level runtime optimization and model-tuning to a production-grade site with interactive performance benchmarks and a 5-year fleet TCO calculator. Unified the engineering architecture and commercial narrative into a single cohesive system.

Impact: Positioned the client as a credible entrant in the enterprise edge AI market with a fully validated software story, including optimized runtime libraries, tuned local models, and a conversion-ready digital presence ready for early customer acquisition.

The Challenge

Running LLMs locally at the edge has traditionally been bottlenecked by latency and resource limits. Our client had a genuine technical breakthrough: an optimized inference engine delivering 400 tokens/second prefill speed on extremely low-power local machines, but needed to translate raw compilation R&D into a complete, enterprise-ready software product.

The specific challenges:

• Software compilation & runtime optimization: Tuning local model weights and execution schedules to leverage NPU and CPU threads efficiently for concurrent requests.
• Performance positioning: Demonstrating the 6x speed advantage over standard edge setups through rigorous benchmarking and clear visualizations.
• Cost narrative: The zero-cloud, zero-recurring-cost model needed to be quantified against AWS/Azure alternatives over enterprise deployment timelines.
• Enterprise readiness: Technical capabilities needed translation into business terms: TAM ($24B), total cost of ownership (TCO) savings, and regulatory compliance (GDPR/HIPAA).
• Product identity: Moving from a raw console command prototype to a product with a compelling web presence.

The Solution

BeeNex engineered the complete software product stack, taking the client from a CLI prototype to a market-ready platform with a unified technical and commercial identity.

Inference & Model Optimization

Worked alongside the client engineering team to optimize open-weights models and compiler options, ensuring the local runtimes could leverage available multi-core architectures without memory leaks. The tuned software stack delivers 400 tokens/second prefill and 20–25 tokens/second generation for 1B parameter models.

Performance Benchmarking & Competitive Positioning

Built rigorous benchmark infrastructure comparing local runtime speeds against standard setups (67 t/s prefill) and cloud-hosted LLM endpoints. Engineered interactive visualizations that make the 6x speed advantage immediately tangible, with side-by-side latency and throughput comparisons.

Interactive Web Tools & Calculators

Designed and built the 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.

Enterprise Go-to-Market Infrastructure

Translated technical specifications into business-ready materials: addressable market sizing, fleet economics modeling, and regulatory compliance documentation. Built the waitlist and meeting scheduling infrastructure to support early pilot interest.

Results

Our client went from a command-line breakthrough to a market-ready software platform with a unified identity, with optimized runtime libraries, tuned local models, and a digital presence that speaks to enterprise buyers. Local AI doesn't need the cloud. It just needed the right engineering.