Large language model engineering.
From integration to fine-tuning to orchestration, RapidData engineers large language models into dependable enterprise applications — model-agnostic, grounded in your data, and governed for production.
LLMs, engineered for the enterprise.
LLM development is more than calling an API. RapidData engineers large language models into reliable applications — choosing between RAG, fine-tuning and prompt engineering, orchestrating multiple models, and evaluating output rigorously.
Our LLM Mesh lets you route across commercial, open and private models, switch as the landscape evolves, and control cost and latency. We ground models in your governed knowledge and wrap them in guardrails and evaluation.
We deliver from selection and prototyping through to production deployment and managed run, with full governance and observability.
LLM Strategy & Grounding
We choose the right approach for each use case — RAG, fine-tuning or prompt engineering — and ground models in your data.
RAG vs fine-tuning
Pick the right grounding strategy per use case.
Prompt engineering
Robust, evaluated prompt and chain design.
Fine-tuning
Adapt open models to your domain where it pays off.
Knowledge integration
Connect models to your governed knowledge base.
LLM Mesh & Orchestration
Run multiple models behind one governed layer — route by task, cost and latency, and avoid lock-in.
LLM Mesh
Model-agnostic routing across providers.
Chaining & tools
Compose multi-step LLM workflows with tool use.
Caching & cost
Manage token spend with caching and routing.
Fallback & resilience
Graceful degradation across models.
Evaluate, Deploy & Operate
Evaluate rigorously, deploy on your infrastructure, and operate with observability.
Evaluation harness
Measure accuracy, safety and cost continuously.
Deployment
Cloud, on-premise or sovereign.
Observability
Monitor drift, latency and quality in production.
Governance
Guardrails, RBAC and audit logging.
Related capabilities & platforms.
Frequently asked questions
What does LLM development involve? +
Selecting and grounding large language models (via RAG, fine-tuning or prompting), orchestrating them, evaluating output, and deploying and operating them reliably in production.
Should we fine-tune or use RAG? +
It depends on the use case. RAG is best for grounding in changing knowledge; fine-tuning suits stable, domain-specific behaviour. We help you choose and often combine them.
What is an LLM Mesh? +
A model-agnostic orchestration layer that routes requests across multiple LLMs — commercial, open and private — by task, cost and latency, avoiding lock-in.
Can you use open-source or private models? +
Yes. We work with commercial, open-source and privately hosted models, including on-premise and sovereign deployments.
How do you evaluate LLM quality? +
With an evaluation harness measuring accuracy, safety, latency and cost, plus human review on high-risk output.
Where can models be deployed? +
On AWS, Azure, GCP, on-premise or a sovereign-cloud region with data-residency controls.
Engineer LLMs that hold up in production.
Talk to our LLM team about grounding strategy, orchestration and a governed deployment plan.