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Picking the Wrong AI Architecture
You're using a general-purpose LLM for a task that needs a fine-tuned classifier. Or vice versa. The result is either over-engineered, under-performing, or both.
Expert guidance on selecting, training, and integrating the right machine learning models for your specific product — whether that's a local deployment, a managed cloud service, or a third-party API.
You're using a general-purpose LLM for a task that needs a fine-tuned classifier. Or vice versa. The result is either over-engineered, under-performing, or both.
Your use case requires sending sensitive user or financial data to an external AI API. Legal and compliance have blocked the project entirely.
You've trained a model but have no standardised way to measure if it's actually improving, regressing, or producing biased outputs in production.
We map your use case requirements to a capability matrix covering accuracy, latency, cost, data privacy, and vendor risk — and recommend the exact model stack for your constraints.
For sensitive use cases, we deploy open-source LLMs (LLaMA 3, Mistral, Phi-3) on your own infrastructure via Ollama or vLLM — zero data leaves your environment.
We establish ground-truth test sets, automated evaluation harnesses, and production drift monitoring — so you know the moment a model update degrades quality.
You're using a general-purpose LLM for a task that needs a fine-tuned classifier. Or vice versa. The result is either over-engineered, under-performing, or both.
We map your use case requirements to a capability matrix covering accuracy, latency, cost, data privacy, and vendor risk — and recommend the exact model stack for your constraints.
Your use case requires sending sensitive user or financial data to an external AI API. Legal and compliance have blocked the project entirely.
For sensitive use cases, we deploy open-source LLMs (LLaMA 3, Mistral, Phi-3) on your own infrastructure via Ollama or vLLM — zero data leaves your environment.
You've trained a model but have no standardised way to measure if it's actually improving, regressing, or producing biased outputs in production.
We establish ground-truth test sets, automated evaluation harnesses, and production drift monitoring — so you know the moment a model update degrades quality.
Every engagement starts with a free architectural consultation. No commitment.
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