LLM Development Services

We select the customization method before training, because fine-tuning, continued pretraining, and distillation each solve a different problem. Supervised fine-tuning adapts behavior on labeled examples, continued pretraining adapts the model to your broader domain data, and distillation compresses a large model into a faster one. We design the training run, set the hyperparameters, and validate against held-out data at every checkpoint. Custom LLM training services run on documented pipelines so results are reproducible.

We prepare training and validation data to match the customization method, the model requirements, and the expected output format, because dataset quality directly affects whether the model learns the intended behavior. Data preparation covers preprocessing, cleaning, deduplication, and labeling, with preference datasets built where alignment methods require them. Where real data is scarce, we generate synthetic training data validated against your quality bar. Every dataset is versioned so training runs stay reproducible and auditable.

We use parameter-efficient fine-tuning to adapt a limited set of parameters rather than retraining the full model, which cuts compute and storage requirements substantially. LoRA introduces trainable low-rank adapters, and QLoRA combines those adapters with quantized model weights so large models fine-tune on smaller hardware. We select between full fine-tuning and parameter-efficient methods based on your accuracy target and budget, then validate that the efficient path holds accuracy against the full-tuning baseline before committing to it.

We evaluate every custom model against use-case-specific datasets before deployment, not against generic leaderboards that say nothing about your task. Custom LLM evaluation measures accuracy, factual consistency, instruction-following, and reasoning against golden datasets built for your use case. Safety, bias, and hallucination testing run alongside, with adversarial and red-team inputs where the risk profile requires them. Regression testing confirms that a new training run has not degraded behavior the previous version handled correctly.

We optimize inference after evaluation confirms accuracy, because a fast model that answers wrong is not a saving. Quantization represents weights at reduced precision to cut memory requirements, while pruning and compression reduce model size where accuracy allows. KV caching, prompt caching, and request batching raise throughput and lower per-query cost. We measure latency, token throughput, and GPU utilization against your traffic profile, then tune the serving configuration so the cost model after launch matches what you approved.

We deploy through LLMOps pipelines with a model registry, versioning, and safe rollback, so a new model or configuration releases and retracts without disrupting live inference. Deployment targets cloud, private cloud, or on-premises infrastructure depending on your data residency and security requirements. Production monitoring tracks inference latency, token usage, cost, and accuracy, with model drift detection that triggers retraining before performance degrades. Autoscaling and high-availability configuration hold service quality as request volume grows.

We design the security architecture before training begins, mapping how sensitive data and personally identifiable information are handled across preparation, training, inference, and storage. Private LLM development keeps your model and data inside your own environment, with encryption, role-based access control, and tenant isolation enforced at every layer. Audit logging records access and inference for compliance review, and model provenance tracks training-data lineage so the model’s supply chain is documented. Data residency requirements are met by deployment location.