Approach
Measure before making claims. Commercial leaderboards rank models on general trivia; real production systems succeed or fail on hard physical constraints: memory bandwidth, latency jitter, and data sovereignty.
Every recommendation Rochon makes is backed by empirical testing on physical silicon, and every engagement follows the same disciplined, five-stage lifecycle.
Five disciplined stages
Fixed-fee pilot to full productionWeeks 1–2
Discover
Workflow mapping, sample data review, baseline criteria.
Deliverable: Technical scope brief, data boundary map, and hardware testing criteria.
Weeks 2–3
Benchmark
Candidate models measured on real hardware targets.
Deliverable: Empirical benchmark matrix comparing latency, throughput, power, and quality on real silicon.
Weeks 3–6
Deploy
Air-gapped or edge deployment with security gateways.
Deliverable: Production runtime installed inside client perimeter with zero-egress verified.
Month 2
Optimize
Quantization, caching and latency trimming.
Deliverable: Quantized weights, custom tokenizers, and 30–50% compute reduction profile.
Handover
Manage
Patching, driver updates, hardware health monitoring.
Deliverable: Full operational handover, reproducible configs, runbooks, and weights ownership.
The empirical testing harness
We evaluate candidate architectures on physical silicon under real concurrency, never solely on synthetic benchmarks or simulators.
01 / SILICON
Physical hardware
Testing runs on bare-metal server GPUs, NVIDIA Jetson modules, or low-power embedded microcontrollers in your operating envelope.
02 / LOAD
Adversarial concurrency
Tail latency (p95/p99), memory saturation, and thermal throttling measured under sustained bursts rather than idle single calls.
03 / NETWORK
Zero-egress verification
Continuous packet monitoring confirms that zero prompt data, embeddings, or telemetry cross outside client perimeters.
What we measure
Core evaluation dimensionslatency.p95/p99
→Captured under sustained concurrent request bursts on target silicon, not idle single-stream synthetic tests.
memory.bandwidth
→VRAM and bus saturation profiling before expensive GPU racks or embedded modules are purchased.
thermal.draw
→Sustained wattage, junction temperatures, and thermal throttling limits inside the device's physical enclosure.
cost.task
→Per-unit economics, token amortizations, and operational expenses across model size and quantization grades.
Operating commitments
100% Client Ownership
All fine-tuned weights, custom tokenizers, deployment scripts, and configs belong exclusively to you. We leave you with operating assets, not a recurring API tax.
Zero Vendor Kickbacks
We hold no reseller agreements with cloud providers, chipmakers, or SaaS platforms. System recommendations are driven strictly by workload data.
Honest Feasibility
If an operational task does not need a heavy neural model, or if current open weights cannot reliably hit your accuracy requirement, we tell you immediately.
Deliverables, Not Decks
Every engagement produces runnable code, containerized runtimes, and measured benchmarks on physical hardware. We do not sell strategic slide decks.
Have a defined workload to test?
Every engagement starts with a defined, fixed-fee pilot: a measured answer to one specific question on your real data, delivered in 2 to 6 weeks.
Deploy custom AI on your terms.
Tell us about your target silicon, data residency constraints, or latency requirements. Rochon engineers will provide a clear evaluation of what is viable, with power and latency models and deployment blueprints.