Enterprise LLM Development Company

Commercial APIs provide immediate capable models but create data privacy risk, ongoing per-query costs, and limited customisation. Custom LLMs offer domain accuracy, data sovereignty, cost control at scale, and output behaviour calibrated to your requirements.

LoRA fine-tuning can achieve meaningful improvements with 1,000–5,000 high-quality instruction-response pairs. Full fine-tuning for significant behavioural change typically requires 10,000–100,000+ examples. We assess your available data during scoping.

Yes. We specialise in private LLM deployment using open-source models on your on-premise GPU infrastructure or isolated private cloud. This is standard practice for regulated industry clients.

We apply model quantisation, continuous batching, KV cache optimisation, speculative decoding, and tiered model routing — reducing inference cost by 60–85% vs unoptimised baseline deployment.

We implement input validation, output content filtering, PII detection and redaction, confidence thresholds, and adversarial prompt injection defences — tested against your specific compliance requirements before deployment.

Requirements depend on model size and throughput. A 7B parameter model for moderate throughput can run on a single A10G. A 70B model serving many concurrent requests requires multi-GPU infrastructure.

Data curation through production deployment for a LoRA fine-tuned model typically requires 8–14 weeks. Full fine-tuning of larger models with extensive evaluation and integration takes 14–20 weeks.

Fine-tuned model weights, training datasets, prompt libraries, and all associated code are fully client-owned upon final payment.

We construct task-specific benchmark suites before fine-tuning begins, measuring baseline performance then evaluating the same benchmarks post-fine-tuning. Results are documented in a model card before production deployment.

Yes. We establish model versioning, retraining pipelines, and regression test suites that enable controlled model updates as your knowledge base or requirements evolve.

We fine-tune both open-source models and, where the licensing and deployment model fit, OpenAI and Anthropic Claude foundation models — the choice is driven by your data residency and latency requirements, not a single default vendor.

If fine-tuning is already the right call for your use case, we start directly. If you're unsure whether fine-tuning, RAG, or prompt engineering is the right approach, our AI Consulting engagement scopes that decision first — it's cheaper than fine-tuning the wrong architecture.

Fine-tuned weights and training data are scoped to your own cloud environment or a dedicated private deployment — never shared across clients — with access controls and audit logging matching your existing security requirements.

Production LLMs need monitoring for output drift, retraining triggers as your domain data evolves, and cost tracking as usage scales. We offer post-launch monitoring and scheduled retraining as a retainer scoped to your model's actual usage.

Yes — startup engagements are typically a single fine-tuned model for one domain; enterprise engagements add governance and multi-model version control. Both start under mutual NDA before any proprietary data is shared.