LLM Integration & Fine-Tuning

Most enterprise teams achieve strong results from prompt engineering and RAG before fine-tuning becomes necessary. Our AI integration services begin with an evaluation phase — we test the base model against your use case, identify where prompt engineering falls short, and recommend fine-tuning only when domain-specific accuracy consistently requires it. Fine-tuning is most valuable for specialised style, tone, or vocabulary that cannot be injected at query time.

We integrate OpenAI (GPT-4o, o1, o3), Anthropic (Claude 3.5 and 4), Google (Gemini 1.5, 2.0), Mistral, Meta Llama, and Cohere, as well as open-source models self-hosted on AWS, Azure, or GCP. Model selection is driven by your latency requirements, cost envelope, data-residency rules, and output quality benchmarks.

We implement PII scrubbing before data reaches any model API, role-based prompt access controls, audit logging of every inference call, and output filtering for harmful or off-topic responses. For regulated industries we scope an on-premises or VPC-isolated deployment where no data leaves your environment.

We instrument every integration with token-level cost tracking, latency percentiles, prompt and completion logging, hallucination rate baselines, and drift alerts when model outputs degrade. Dashboards surface these in real time so your team can catch regressions before they reach end users.

Our AI integration services follow a structured delivery: a two-week discovery and model evaluation sprint, a four-to-six-week build phase covering API integration, prompt engineering, guardrails, and observability, then a two-week hardening and handover. Most production-ready integrations ship in eight to ten weeks. Fine-tuning projects add two to four weeks depending on dataset size and evaluation cycles.