We integrate GPT-4, Claude, Gemini, or open-source models into your product stack — with fine-tuning, retrieval-augmented generation (RAG), and private API deployment so your AI knows your business context and serves users reliably.
GPT-4 · Claude · Gemini · Llama 3 · Fine-tuning · RAG · Private Deployment
Not all tasks need GPT-4. We assess your use case, latency requirements, data sensitivity, and budget to recommend the best-fit model — then build a clean, secure API layer so switching or upgrading models never requires rewriting your application.
We run your specific tasks against multiple models — frontier (GPT-4o, Claude 3.5, Gemini 1.5 Pro) and open-source (Llama 3, Mistral, Phi-3) — measuring accuracy, latency, and cost per token. The result is a clear recommendation with data, not guesswork, so you can make an informed build decision.
We build an abstraction layer between your application and the LLM provider — handling authentication, rate limits, fallback routing, request caching, and cost quota enforcement. The gateway also redacts sensitive PII before it leaves your infrastructure, meeting data handling obligations under GDPR and similar regulations.
Prompt quality determines output quality. We design and test structured system prompts, few-shot examples, and output format specifications for your use case — iterating against a curated evaluation set until the model behaves consistently and within acceptable bounds. Prompts are version-controlled and tested like code.
General models are trained on general data. For tasks that require deep domain knowledge — legal, medical, financial, technical — fine-tuning on your own data produces dramatically better results, lower hallucination rates, and more consistent formatting than prompt engineering alone.
The quality of fine-tuning data directly determines the quality of the resulting model. We help you identify, extract, clean, and structure the best training examples from your own historical outputs, documents, and expert knowledge — including setting up human labelling workflows for edge cases and quality validation checkpoints.
We run supervised fine-tuning using OpenAI's fine-tuning API for GPT-series models, or LoRA/QLoRA for efficient parameter-efficient fine-tuning of open-source models. Each training run is evaluated against held-out benchmarks and compared to the base model to quantify exactly how much the adapted model improves on your specific tasks.
Retrieval-Augmented Generation lets you attach your entire knowledge base to any LLM without retraining. We design RAG pipelines that retrieve the right documents at the right time — making responses accurate, grounded, and citable even as your data evolves.
We set up the right vector database for your scale and access patterns — Pinecone, Weaviate, pgvector, or Qdrant — and design embedding pipelines that keep your index current as documents are added, updated, or deleted. Chunking strategy and embedding model selection are tuned to your document types and query patterns.
Every model response is grounded in retrieved source documents, and citations are surfaced to end-users so they can verify answers. We design faithfulness evaluators that automatically flag responses where the model has drifted from the retrieved context — preventing hallucination at the application layer before it reaches users.
Book a free integration scoping session. We'll review your use case, recommend the right model, and produce a technical architecture plan within 5 business days.