LLM development services cover the engineering work of putting large language models to productive use: selecting and integrating models, connecting them to your data with retrieval, tuning prompts and context, evaluating output quality, and operating the result under real cost and latency constraints. BigData Boutique delivers all of it — as a project team or alongside yours, in your cloud or on-premises.
We are not a model-training lab. Like most successful enterprise LLM work, our projects build on foundation models — Anthropic Claude, models on AWS Bedrock, OpenAI, and open-source models — and put the engineering effort where it pays off: retrieval quality, context engineering, evaluation, and guardrails.
What Our LLM Development Services Include
Custom LLM Development & Integration
We design and build LLM-powered features end to end: API and application integration, structured outputs, tool calling, and streaming interfaces. LLM integration into an existing product is usually a few weeks of work when the data foundations are right — and most of our work is making the data foundations right.
LLM Implementation on Your Data
Grounding models in your data is what makes them useful. We build the retrieval layer — hybrid vector and keyword search, chunking, reranking — that feeds your documents to the model, the core of every serious enterprise LLM implementation. For the full RAG picture, see our generative AI development services.
Fine-Tuning & Evaluation
When retrieval isn't enough, we fine-tune — see when fine-tuning beats RAG. Every engagement ships with an evaluation suite: assertions, curated eval sets, and LLM-as-a-judge scoring, following the practices in our LLM evaluation guide.
LLM Consulting & Cost Optimization
Already running LLMs in production? Our LLM consulting engagements review architecture, quality, and spend. Caching, model routing, and context compression routinely cut LLM costs by half or more — the playbook is in our LLM cost optimization guide.
Why Teams Choose Us as Their LLM Development Company
- Production first. Evals, guardrails, observability, and cost controls are part of the build, not an afterthought.
- Your data, your cloud. Everything runs in your environment — AWS, GCP, Azure, or on-premises. As an AWS Advanced Tier Services Partner with the AI Services Competency, we fast-track deployments on Bedrock.
- Search engineering roots. A decade of Elasticsearch, OpenSearch, and vector search work means the retrieval layer — where LLM quality is won or lost — is our home turf.
- Fixed-path delivery. Our AI Launchpad program takes an LLM use case from idea to production in weeks, with AI agent development and MLOps practices behind it.
Not sure whether you need LLM development, machine learning consulting, or AI strategy consulting? Start with an AI readiness assessment — one short engagement that tells you what to build and what it will take.