Part of the DEPLOY Method — Engineer phase
This is not the bespoke Domain-Expert LLM Lab. It is the SME adaptation of it. A small or mid-sized business with a specific vertical use case — contract review, product catalogue enrichment, expense categorisation — should not pay for eight weeks of bespoke research when the pipeline for that vertical is already 80% built. The packaged engagement uses a curated base model, a retrieval layer, and an evaluation harness that Hyperion has already assembled for a small set of supported verticals, and applies them to your proprietary data. You keep the weights and the eval harness; Hyperion keeps the pipeline template. The result is a domain-expert model that runs on your infrastructure or a sovereign-cloud tenant, at a fixed fee per vertical, delivered in four weeks instead of eight. The verticals supported today are narrow by design — legal clause extraction, retail catalogue enrichment, and accounting invoice extraction — because the judgment calls that make a packaged offering viable require the same pipeline to have been validated across multiple clients before it becomes a product. Outside those verticals, the bespoke Lab is the correct entry point.
The bespoke engagement is priced for enterprises and you are not one. Eight-week fine-tuning programs with embedded ML engineers are correctly priced for companies with seven-figure AI budgets and a real ML team to absorb the knowledge transfer. For an SME with a single vertical use case and a two-person technical team, the bespoke engagement is overkill. What the SME actually needs is the 20% of the work that is unique to its data, riding on the 80% that is common across businesses in the same vertical. That economics only works if the common 80% is already built.
Frontier APIs keep getting better on general tasks and worse on yours. GPT-4 and Claude improve on broad benchmarks every quarter, and your specialist task — contract clause extraction in French commercial law, SKU-level catalogue enrichment for fashion retail, VAT-aware expense categorisation for Belgian accounting — does not move with them. You are paying a premium for general intelligence that was never going to win on your narrow task, and the gap between 'generic API output' and 'output your domain expert would sign off on' is not closing. At some point the honest answer is that your vertical requires a specialist model and the generalist API was always a stopgap.
Your team cannot build a fine-tuning pipeline from scratch and it would be a bad use of their time if they could. Fine-tuning a model correctly — data curation, base model selection, eval harness construction, quantization trade-offs, deployment — is a multi-week workstream for an experienced ML engineer. If you have that engineer, they should be building your product. If you do not, the tutorials will get you a model that looks trained but loses the eval, and you will not know why. The packaged offering collapses the multi-week workstream into a four-week fixed-fee engagement with a pre-validated recipe for your specific vertical.
You need the model to run somewhere that is not a frontier-API provider. Your clients — law firms, accountancies, regional retailers — have data residency concerns, client-confidentiality obligations, or sectoral regulation that makes sending their data to a US hyperscaler a commercial problem even when it is technically allowed. A model you own, deployed on your infrastructure or a European sovereign tenant, is a structural answer to those concerns in a way that a frontier-API vendor contract never will be. For an SME, that posture is a genuine commercial differentiator, not a compliance checkbox.
The engagement is the ENGINEER phase of the DEPLOY Method, compressed to four weeks by the pre-built pipeline for your supported vertical. Your team provides the proprietary data and the subject-matter expert who grades the output. The pipeline — base model, retrieval, eval template, inference stack — is already assembled. The first conversation confirms your vertical is in the supported set; if it is not, the bespoke Lab is the correct engagement and we do not start this one.
Your data lands on the pipeline. We audit coverage, licensing, and quality against the requirements of the packaged vertical — legal, retail, or accounting. The eval harness is instantiated against the task definition for your vertical and a baseline is run on the incumbent frontier API, so we know what winning looks like before any training starts. If the data coverage is thin or the task definition falls outside the supported vertical, we stop here and refund the balance; the packaged offering only works when the fit is real.
The pre-selected base model for your vertical — a specific Llama 3, Mistral, or Qwen variant chosen for this task profile — is fine-tuned on your curated data using the pipeline recipe. We run the eval harness every day of the week and iterate on the data mix where the numbers demand it. By end of week two the model either beats the frontier API baseline on your task-specific eval or we revert to the next-best configuration and document the ceiling honestly. The packaged offering is only worth paying for if the model actually wins.
Inference is stood up where you will actually run it — a sovereign-cloud tenant, a small on-premise GPU, or a dedicated inference provider that keeps data in your jurisdiction. The latency and cost envelope is fixed for the packaged verticals, so we tune against a known target rather than explore the full design space. The subject-matter expert on your side signs off on the deployed model's output across a sample of real production cases; that sign-off is the acceptance criterion.
Your two-person technical team is walked through the training recipe, the eval harness, and the deployment runbook. The model, the weights, the data pipeline, and the eval are yours to keep. The pipeline template — the cross-client scaffolding that made the four-week timeline possible — remains Hyperion intellectual property; you are paying for the specialised application of it to your data, not for the underlying framework. When a better base model ships, your team can re-run the recipe on the new base in under a week without further engagement.
Small and mid-sized businesses in legal services, retail, or accounting — the three verticals the packaged pipeline supports today — with a specific task (contract clause extraction, catalogue enrichment, invoice or expense categorisation) and a proprietary dataset at least large enough to fine-tune against. Teams where the existing frontier-API solution has plateaued on domain quality and the cost is material at current volume. Businesses where data residency or client confidentiality makes a self-hosted or sovereign-cloud model a genuine commercial preference rather than a box-ticking exercise. This is not for SMEs whose use case falls outside the supported verticals — the bespoke Domain-Expert LLM Lab is the correct entry point for those engagements, at its own timeline and pricing. It is also not for teams without proprietary data; without the data asset, a fine-tuned vertical model has no durable advantage over the frontier API, and the Readiness Audit is the right first conversation.
Not as the packaged offering, no. The three supported verticals are supported because the pipeline has been validated across enough prior engagements to be priced as a product. Outside those verticals, the bespoke Domain-Expert LLM Lab is the correct engagement — eight weeks, bespoke fine-tuning, priced accordingly. If your task is close to a supported vertical but not quite inside it, the first conversation is free and I will tell you honestly whether the packaged pipeline applies or whether the bespoke Lab is the right fit.
Because the pre-built pipeline for your vertical — base model selection, retrieval layer, eval template, inference stack — is already assembled from prior engagements. In the bespoke Lab, those decisions are made fresh for each client, which is correctly priced for enterprises with novel tasks. In the packaged offering, those decisions are reused, which is correctly priced for SMEs with tasks that look like the patterns the pipeline was built on. The four weeks you pay for is the specialised application to your data, the eval against your baseline, and the deployment on your infrastructure — not the framework underneath.
We find out in week two, and if the answer is no, the engagement terminates at that point and you are refunded the balance. The pre-built pipeline for a supported vertical has a known success rate on representative data; the week-two eval is explicitly the checkpoint where we confirm the pattern holds for your specific data. If the data is too thin, the task is outside the pipeline's validated scope, or the frontier API is already at the ceiling your task allows, I will say so in writing. The packaged offering is priced to assume the fit is real; when it is not, the honest outcome is to stop rather than force a result.
Usually no. For the packaged verticals, inference is small enough to run on a modest GPU in a European sovereign-cloud tenant — Scaleway, OVHcloud, or similar — or on a dedicated inference provider like Together or Fireworks that keeps data in-region. Training is done on rented GPUs and does not require a hardware purchase. The fixed-fee pricing includes a cost envelope for inference at typical SME volumes; heavier workloads push the model toward on-premise GPUs, but that is an exception rather than the default.
Not usually. Your team owns the eval harness, the data pipeline, and the recipe, which means re-running the training on a new base model — Llama 5 when it lands, a new Mistral release, a stronger Qwen variant — is an internal exercise your team runs without further engagement from Hyperion. Most SMEs bring the retraining in-house after the first engagement; some choose to run a short refresh engagement with Hyperion when a new base model is materially better, but that is optional and priced separately. The ownership position is deliberate: the packaged offering is one engagement, not a retainer.
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