Custom LLM Fine-tuning
Fine-tuned language models trained on your proprietary data, optimized for your specific tasks, and deployed in your infrastructure. Higher accuracy, lower latency, reduced inference cost — and no proprietary data sent to third-party APIs.
Why General-Purpose Models Fall Short for Specialized Tasks
GPT-4o and Claude are extraordinarily capable general models. For specific, high-volume business tasks — classification, extraction, generation with strict format requirements — a smaller fine-tuned model outperforms them on accuracy, latency, and cost.
At 100k+ API calls per month, GPT-4o and Claude costs add up fast. Fine-tuned open-source models running on your own infrastructure cost 5–10× less per inference at equivalent task accuracy.
Sending customer PII, proprietary business data, or regulated content to a third-party API isn't always acceptable. Some industries — healthcare, finance, legal — can't use hosted APIs for certain workloads.
A general model prompted for your specific classification taxonomy or extraction schema will make errors a fine-tuned model won't. The difference matters at scale — even a 2% error rate on 50k daily classifications is 1,000 errors per day.
Real-time applications — live classification, instant response generation, embedded product features — can't absorb 2–4s GPT-4o latency. A well-quantized fine-tuned model responds in 80–200ms.
The accuracy gap between a prompted general model and a fine-tuned specialist model compounds fast at scale. On classification and extraction tasks, fine-tuning consistently delivers 10–20 percentage point improvements — with 80% lower inference cost.
How Custom LLM Fine-tuning Works
Not a prompt engineering exercise. A full model training and deployment pipeline — from raw data to a production-serving model on your infrastructure.
Your training data cleaned, structured, and formatted into instruction-tuning pairs. Quality filtering run to remove noise. Dataset split into train/eval sets.
Base model selected (Llama 3, Mistral, Phi-3) based on task type, size constraints, and hardware. LoRA or QLoRA method chosen to balance quality and compute cost.
Training executed with hyperparameter optimization — learning rate, batch size, LoRA rank tuned for your dataset. Multiple checkpoints evaluated against your task benchmark.
Fine-tuned model benchmarked against base model and prompted GPT-4o on your specific task. If accuracy targets aren't met, training iterated with adjusted data or hyperparameters.
Model served on your infrastructure via vLLM or Ollama, wrapped in a versioned API endpoint. Accuracy monitoring and retraining triggers configured for ongoing quality.
Training data and model weights stay in your infrastructure. Nothing sent to third-party training APIs. GDPR and SOC 2 compatible by design.
Parameter-efficient fine-tuning methods that achieve high task accuracy without full model retraining — reducing compute cost by 70–90% vs full fine-tune.
Every model evaluated against a benchmark built from your actual production data — not generic NLP benchmarks that don't reflect real task difficulty.
Model deployment on vLLM or Ollama, versioned API endpoint, load testing, and monitoring setup included. Not just a model file — a production inference service.
What Teams Fine-tune Custom Models For
All use cases live in production. Metrics are from active fine-tuned model deployments.
Support ticket classification, content moderation, lead scoring, and document routing — tasks where a 7B fine-tuned model outperforms prompted GPT-4o at 1/10th the inference cost. Deployed at 200k+ classifications per day for one client.
Legal clause extraction, medical record parsing, financial data extraction — tasks where domain vocabulary and formatting requirements make prompt-engineering unreliable. Fine-tuned model learns the exact output schema.
Content generation fine-tuned on company's existing copy — emails, product descriptions, social posts. Model learns tone, vocabulary, format, and brand constraints. Marketing teams edit, not rewrite.
Regulatory compliance screening, contract risk flagging, and content policy enforcement — tasks where recall matters more than cost. Fine-tuned model trained on real compliance examples from the client's domain.
Custom Fine-tuning Results Across Deployments
Aggregated from 25+ fine-tuned model deployments. Measured against prompted general model baseline.
Where Custom Fine-tuning Delivers the Most ROI
By use case, based on cost reduction and accuracy improvement at volume.
Average ROI across all client types
Everything Included in Custom LLM Fine-tuning
End-to-end delivery — data preparation, training runs, evaluation, deployment, and ongoing retraining support.
Every workflow, configuration, and script is yours — with full documentation and Loom walkthroughs. Zero lock-in
It depends on the task. For classification and extraction tasks with a narrow output space, 500–2,000 high-quality examples per class are typically sufficient. For generative tasks like brand voice or document drafting, 1,000–5,000 examples produce good results. We'll audit your existing data in discovery and tell you exactly what's usable and what needs to be created.
We primarily work with Llama 3 (8B and 70B), Mistral 7B and Mixtral, and Phi-3 for smaller deployments. Model selection is driven by your task type, latency requirements, and hardware constraints. We benchmark two or three candidates before committing to a base model.
No — and for most clients, data sovereignty is the primary reason they're fine-tuning rather than using a hosted API. We run all training on infrastructure you control: your AWS, GCP, or Azure environment, or a dedicated compute environment we provision and hand over to you. Your data never touches third-party training APIs.
Fine-tuning is worth it when: (1) you're running 50k+ inferences per month and hosted API cost is a concern, (2) your task has strict latency requirements below 300ms, (3) you have a narrow, well-defined task where a specialist model will consistently outperform a generalist, or (4) your data can't leave your infrastructure. For exploratory or low-volume tasks, better prompting is usually the right first step.
We use LoRA (Low-Rank Adaptation), which adds task-specific weight adapters rather than modifying the base model's weights. This preserves general capability while injecting task specialization. For use cases requiring both general reasoning and task specialization, we can also use the fine-tuned adapter selectively and fall back to the base model for out-of-domain queries.
Models drift when real-world input distribution shifts away from the training data. We set up accuracy monitoring against a labeled sample of production traffic that runs weekly. When accuracy drops below a configurable threshold, a retraining trigger fires. Monthly or quarterly retraining on new production data keeps the model current without manual oversight.
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