Every month you route industrial knowledge work to a frontier API, you pay a tax and you compound a dependency. For hard-tech domains — maintenance engineering, MES operations, technical documentation, PLC and SCADA logs, quality inspection records — the generic API was the right starting point; it becomes the wrong long-term choice once you have accumulated proprietary data that encodes the tacit knowledge your domain experts spent years building. This is the Build stage of the Hyperion Lifecycle: an 8-week bespoke fine-tuning engagement that produces a domain-expert model trained on your proprietary industrial corpus, evaluated against frontier APIs on your actual task, and deployed on sovereign infrastructure you control. Our AI runtime uses Mistral-first, open-weight models — we apply what we build. I architected Auralink — 1.7M lines of production code, approximately 20 autonomous agents, arXiv 2603.08736 — on open-weight models because the economics and the control position required it. I've shipped 10 AI ventures where fine-tuned open models outperformed frontier APIs on the domain task. This is not a theoretical capability.
Frontier APIs do not know your maintenance manuals, your MES event codes, or your PLC fault nomenclature. A generic model will hallucinate part numbers, misinterpret fault codes that look like generic text, and produce maintenance recommendations that are plausible-sounding but wrong for your specific equipment configuration. The gap between a useful answer and a wrong-but-confident answer is invisible to a user who does not already know the answer — which is exactly the situation where the model is supposed to add value. A domain-expert model trained on your maintenance corpus, your MES logs, and your technical documentation does not have this problem.
Your industrial data is building someone else's moat. Every MES query, fault-code lookup, or maintenance Q&A your engineers send to a frontier API passes through the provider's infrastructure. Your proprietary maintenance corpus, your fault-resolution history, your equipment-specific calibration records — these encode decades of operational knowledge that differentiates your site from a competitor's. Sending that data to a frontier API does not fortify that knowledge advantage; it dilutes it. In regulated industrial environments it also creates data residency and sovereignty problems.
You have no recourse when the provider changes the API behaviour. A frontier API model update changes the fault-code interpretation behaviour and your maintenance copilot starts producing different recommendations for the same input. You have no engineering response — only a procurement one. For safety-adjacent use cases, inconsistent model behaviour is not a minor inconvenience; it is a potential liability.
Your engineering team has run the fine-tuning tutorial but has not shipped a model that wins on a production eval. The distance between 'I fine-tuned a model on our maintenance manuals' and 'I shipped a model that outperforms the API on our fault-diagnosis task with statistical significance' is where most industrial fine-tuning projects stall. It is not a compute problem or a data-volume problem; it is a judgment problem — base model selection, data mix, evaluation methodology — that requires pattern recognition from multiple industrial deployments.
The engagement runs in four two-week phases. I work embedded with your ML and domain-expert teams — your engineers do the work, I bring the decisions and the pattern library. No work happens on vendor infrastructure we do not control. You own the data, the weights, the eval harness, and the deployment at every step.
The model is as good as the data and as measurable as the eval harness. I audit your proprietary industrial corpus for coverage, quality, contamination, and licensing: maintenance manuals, MES/PLC event logs, technical documentation, quality inspection records, SCADA historian exports, engineering change records. We define the evaluation tasks that map to your actual production workload — fault diagnosis accuracy, maintenance-step correctness, part-number precision — not generic LLM benchmarks. We build the eval harness against the incumbent frontier API first, establishing a real baseline to beat.
Base model selection across Mistral, Llama 3, and Qwen families based on your task profile — instruction-following for maintenance Q&A, reasoning depth for fault diagnosis, context length for long technical documentation. We run structured experiments — LoRA versus full fine-tune, data mix ablations across maintenance manuals and event logs, checkpoint ensembles — and evaluate every run against the week-two baseline. We document which industrial data types drive the most improvement and which are noise for your specific task.
We deploy inference on infrastructure you control: your own GPUs on-premise, a sovereign-cloud deployment in your region, or a dedicated inference provider under a data processing agreement that matches your industrial and regulatory requirements. Quantization, batching strategy, KV cache handling, serving framework — optimised for the latency and cost envelope your operations require. For air-gapped environments or OT-adjacent deployments, the inference path is designed to operate without external API calls.
Working sessions with your ML and domain-expert teams so they own the eval harness, the training pipeline, and the inference deployment. I document all judgment calls — base model selection, data mix, quantization trade-offs, which maintenance corpus sections drove the most improvement. When I leave, your team can train the next version without me. The model, the weights, the code, the eval — all yours.
Manufacturers, energy operators, automotive OEMs, and aerospace primes with proprietary maintenance manuals, MES/PLC event logs, technical documentation, or quality inspection records that encode tacit domain knowledge not available in generic training data. Engineering teams where the head of ML or VP Engineering has already run the math on frontier API costs at 3x–5x current usage and knows the unit economics do not hold. Industrial operators with data residency, sovereignty, or OT-security requirements that make frontier API dependency a compliance liability. This is not for teams without proprietary industrial data — generic fine-tunes do not outperform frontier APIs and should not be attempted without a defensible proprietary corpus.
Because we measure it in week two, before any training starts. The eval harness is built against the frontier API baseline first, so we know exactly what winning requires on your specific industrial task. If the baseline is already at the ceiling your task allows, I will tell you in week two and we stop — you keep the eval harness and the diagnostic. On narrow domain tasks with real proprietary industrial data — fault diagnosis, maintenance-step retrieval, MES event interpretation — a well-trained open model consistently wins on task accuracy and dominates on cost and sovereignty.
Industrial documentation is almost always multilingual and multi-format: PDFs, DOCX, structured MES exports, proprietary historian formats, and handwritten log scans. The data curation phase in weeks 1–2 handles this explicitly — format extraction, OCR where needed, deduplication, language tagging, and licensing review. We document which corpus sections drive the most task improvement and which are noise, so the training data investment is targeted.
Yes — and for many industrial operators, this is a requirement. The deployment phase in weeks 6–7 explicitly covers air-gapped and OT-adjacent inference: quantized models running on on-premise hardware, no external API calls in the inference path, and state synchronisation designed for intermittent or no connectivity. The sovereignty story is a deliverable, not an afterthought.
The data curation phase is designed to minimise domain-expert time: structured document ingestion from existing sources (maintenance manuals, MES exports, technical docs) first, targeted Q&A sessions with domain experts only for gap-filling and eval-task definition. Typically 4–6 hours of domain-expert time in week one is sufficient to define the eval tasks and validate the data scope. The ML team handles the rest.
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