Transform general-purpose AI into specialized experts for your industry. Boaweb AI delivers custom LLM fine-tuning that understands your terminology, workflows, and business logic with enterprise-grade accuracy.
General LLMs struggle with specialized vocabulary in healthcare, legal, finance, and manufacturing. They misinterpret technical terms, generate inaccurate responses, and create compliance risks when precision is non-negotiable for your business.
Your competitive advantage lies in proprietary processes, internal documentation, and years of accumulated expertise. Off-the-shelf models can't access this knowledge, limiting their ability to deliver business value without custom training.
Generic models produce variable results that require constant human review. Fine-tuned models deliver predictable, consistent outputs aligned with your quality standards, reducing review time by up to 80% in production environments.
Large prompt contexts and repeated instructions to guide generic models cost 3-5x more per API call than fine-tuned alternatives. At enterprise scale with thousands of daily queries, this translates to hundreds of thousands in unnecessary AI costs annually.
We audit your existing data sources—documentation, support tickets, past projects, internal communications—to identify high-quality training data. Our team cleanses, structures, and annotates datasets to create instruction-tuning pairs that teach the model your business language.
Deliverables: Data quality report, curated training dataset (typically 1,000-10,000 examples), annotation guidelines, validation dataset
We evaluate leading base models (GPT-4, Claude, Llama 2/3, Mistral) against your requirements for accuracy, cost, latency, and deployment constraints. Our engineers configure optimal hyperparameters including learning rate, batch size, and training epochs.
Deliverables: Model comparison analysis, architecture recommendations, cost projections, performance benchmarks
Using supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), we train the model on your data. Multiple training runs with different configurations allow us to optimize for your specific metrics—accuracy, response style, or safety constraints.
Deliverables: Fine-tuned model checkpoints, training logs, performance metrics, ablation studies
We conduct comprehensive testing using held-out validation sets and real-world scenarios. Subject matter experts from your team evaluate outputs for accuracy, appropriateness, and alignment with business requirements. Red team testing identifies edge cases and potential failure modes.
Deliverables: Validation report, performance comparison (base vs. fine-tuned), error analysis, safety evaluation
We deploy your fine-tuned model to production infrastructure (cloud or on-premise) with monitoring, version control, and A/B testing capabilities. Ongoing data collection enables periodic retraining to maintain accuracy as your business evolves.
Deliverables: Production deployment, monitoring dashboards, retraining pipeline, maintenance documentation
Receive a detailed analysis of your use case including data requirements, expected performance improvements, cost projections, and recommended approach. Free for qualified enterprise prospects.
Accuracy improvement for domain-specific tasks vs. base models
Reduction in API costs through smaller context windows
Faster processing for specialized document analysis tasks
A 450-attorney law firm needed AI assistance for contract review and legal research specific to Swedish and EU regulations. Generic LLMs produced unreliable results with hallucinations and misinterpretation of legal terminology.
Training approach: Fine-tuned GPT-4 on 8,400 annotated legal documents (contracts, case law, regulatory filings) with expert review by senior partners. Implemented continuous learning pipeline to incorporate new precedents and regulatory changes.
The ideal amount varies by use case, but generally: 1,000-5,000 high-quality examples for basic fine-tuning, 5,000-20,000 for complex domain adaptation, and 20,000+ for highly specialized applications. Quality matters more than quantity—curated examples from subject matter experts outperform larger datasets of mediocre quality. Boaweb AI helps you assess your existing data and create synthetic training data when needed.
Fine-tuning requires upfront investment ($15,000-$80,000 depending on scope) but delivers 60-80% lower per-query costs. For enterprises processing 100,000+ queries monthly, ROI typically occurs within 3-4 months. Fine-tuned models use smaller prompts, process faster, and require less compute. We provide detailed cost modeling during the assessment phase to calculate your specific break-even point.
Absolutely. Open-source models like Llama 3, Mistral, and Falcon offer cost advantages and full control over deployment. They're ideal when data privacy requires on-premise hosting or when you need customization beyond what API-based services allow. However, they require more infrastructure expertise. Boaweb AI handles both approaches and recommends the optimal path based on your requirements for performance, cost, compliance, and control.
A typical timeline is 8-14 weeks: 2-3 weeks for data preparation and annotation, 1-2 weeks for base model selection and architecture design, 2-4 weeks for training and optimization iterations, 1-2 weeks for validation and testing, and 2-3 weeks for deployment and integration. Complex use cases requiring extensive data curation or multiple model variants may extend to 16-20 weeks. We provide detailed project timelines during scoping.
Yes, but we build this into the solution. Boaweb AI establishes continuous learning pipelines that make retraining straightforward. As you collect new data (from user corrections, updated documentation, or new product lines), we periodically retrain the model—typically quarterly or bi-annually. The infrastructure we deploy supports versioning, A/B testing, and gradual rollouts so model updates don't disrupt operations.
Deploy general-purpose ChatGPT solutions before considering custom fine-tuning.
Learn More →Combine fine-tuned models with retrieval for maximum accuracy and freshness.
Learn More →Optimize prompts for fine-tuned models to maximize performance gains.
Learn More →Book a technical consultation with Boaweb AI's machine learning team. We'll evaluate your use case, data readiness, and expected performance improvements with a detailed feasibility report.