We adapt foundation models (GPT, Claude, Llama, Mistral) to your specific industry using supervised fine-tuning, instruction tuning, and reinforcement learning from human feedback (RLHF). Our process begins with task definition, then builds custom training datasets from your proprietary documents, historical decisions, and expert annotations.

For regulated industries including legal, healthcare, finance, and defense, we implement differential privacy techniques that preserve data confidentiality during training. Models learn patterns without memorizing sensitive records. For technical domains, we incorporate domain-specific tokenizers and vocabulary extensions that capture specialized terminology more efficiently than general-purpose encoders.

Fine-tuning scope includes classification tasks (document routing, sentiment analysis, entity recognition), generation tasks (report writing, code synthesis, question answering), and reasoning tasks (decision support, diagnostic assistance, compliance checking). We optimize for your target metrics, whether that is F1 score, BLEU score, perplexity, or business KPIs like time-to-resolution.

Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA) enable fine-tuning billion-parameter models on modest GPU hardware. Instead of updating all model weights, LoRA freezes the base model and trains small adapter matrices that modify layer outputs. A 70B parameter model that normally requires 8x A100 GPUs for fine-tuning can be adapted using a single high-end consumer GPU with QLoRA.

Adapter weights are tiny (typically 10-100MB) compared to full model checkpoints (50-200GB), enabling version control, A/B testing, and rollback strategies that are not practical with traditional fine-tuning. This architecture supports multi-tenant deployments where one base model serves 100 different client adapters.

Our LoRA pipelines include automated hyperparameter search, adapter merging for multi-task models, and compatibility layers that let you hot-swap adapters at inference time for rapid experimentation and production deployment.

Retrieval-Augmented Generation (RAG) systems combine the reasoning capabilities of LLMs with dynamic knowledge retrieval from your document corpus. When a user submits a question, the system searches a vector database for relevant context, then constructs a prompt that includes both the query and the retrieved information. This delivers up-to-date answers without retraining, which is critical for knowledge bases that change daily.

We engineer RAG pipelines using ChromaDB, Weaviate, Pinecone, or pgvector, paired with embedding models fine-tuned on your domain. Our architectures incorporate hybrid search (combining dense vectors with BM25 keyword search), reranking layers using cross-encoders, and citation tracking that links generated text back to source documents for auditability.

RAG complements fine-tuning: use RAG when knowledge changes frequently or requires attribution, fine-tuning when you need to internalize reasoning patterns. Most production systems in 2026 combine both approaches for maximum performance. Learn more about how this integrates with our AI implementation services.

Generic embedding models trained on web data fail to capture semantic similarity in specialized domains. Two medical terms that are synonyms may be mapped to distant vectors. We train custom embedding models using contrastive learning on your corpus so that your retrieval systems understand your taxonomy, abbreviations, and conceptual relationships.

Our vector database implementations are optimized for your scale. Small knowledge bases under one million documents run efficiently on PostgreSQL with pgvector. Larger corpora require distributed architectures with HNSW or IVF indexing. We benchmark retrieval latency, recall metrics, and memory footprint to select the optimal stack.

Integration includes automated ETL pipelines that embed new documents as they arrive, reindexing strategies for schema evolution, and monitoring dashboards that track embedding drift to flag when retraining is needed.

Model quality is downstream of data quality. We start every engagement with a data audit, assessing volume, labeling consistency, class balance, and contamination risks. What looks like 50,000 training examples often reduces to 8,000 usable samples after deduplication and quality filtering.

Our curation process includes format standardization, deduplication using MinHash and semantic similarity, and stratified sampling that ensures the training set represents real-world distribution. Active learning workflows identify high-value examples worth human labeling, reducing annotation costs by up to 60% compared to random sampling.

For privacy-sensitive data, we implement PII detection and redaction, synthetic data generation for rare edge cases, and differential privacy noise injection. Data lineage tracking lets you trace any model output back to its training sources, satisfying compliance and debugging requirements.

We evaluate models across three dimensions: quantitative metrics (accuracy, F1, perplexity), qualitative assessment (human expert review), and business impact (measuring downstream KPIs like customer satisfaction or time savings). Benchmark suites include holdout test sets, adversarial test sets designed to exploit known failure modes, and production replay datasets from real user queries.

Quality assurance extends to per-example error analysis. We identify systematic failure patterns, such as difficulty with negation or demographic bias, and implement targeted improvements through additional training data or architectural modifications.

For production deployment, we package trained models using optimized inference runtimes (vLLM, TensorRT-LLM, ONNX Runtime) with REST APIs, streaming interfaces, and batch processing pipelines. Shadow deployment and A/B testing let you validate the model on live traffic before full cutover. See our Secure AI Inference page for deployment architecture details.