Custom large language model solutions
LLM development services for the enterprise — fine-tuning and adapters, LLM application development, private and self-hosted deployment, evaluation harnesses and LLMOps, on any model.
Quick answerLLM development services cover building with and on large language models — fine-tuning and adapter training, evaluation harness design, LLM application development, retrieval pipelines, guardrails, and private or self-hosted deployment. It spans both the model work and the application layer, with an emphasis on data control, cost and specialisation.
The LLM development practice
Each links to a dedicated page.
LLM Application Development Services
Assistants, copilots, search and workflows on any LLM — RAG, tools, evaluation and integration.
ExplorePrivate LLM Development Services
On-premise, VPC and air-gapped LLM deployment and fine-tuning, inside your boundary.
ExploreFine-tuning & adapters
When style, format, latency or accuracy justifies it — measured against RAG-only baselines.
ExploreEvaluation harnesses
Curated, versioned test sets with automatic and human scoring and regression runs.
ExploreLLMOps
Prompt and model versioning, monitoring of quality, latency, drift and cost.
ExploreEnterprise AI Services
LLMs at scale — a governed gateway, shared retrieval and one operating model.
ExploreHow we approach an LLM build
Retrieval before training
RAG grounds answers in current data without training, and stays easy to update.
Fine-tune where it pays
Consistent style, lower latency and token cost at scale, or task accuracy a prompt cannot reach.
Portable architecture
Provider-abstracted so you can benchmark and switch between GPT, Claude, Gemini, Llama or a fine-tune.
Evaluation harness
Representative test set, automatic and LLM-as-judge scoring, human review for critical cases.
Guardrails
Jailbreak and prompt-injection defence, content filtering, and refusal on weak evidence.
Cost & latency
Model routing, caching and context management, tracked as cost-per-request and cost-per-user.
Choosing the right approach
| Need | Fine-tuning | RAG |
|---|---|---|
| Current, changing knowledge | Retrain to update | Update content, not the model |
| Consistent tone / format | Strong | Prompt-dependent |
| Lower cost & latency at scale | Shorter prompts | Context sent each call |
| Per-user permissions | Not possible | Enforced at query time |
| Most production systems | A light fine-tune | + a retrieval layer |
An LLM development company for the enterprise
AWS-native delivery
Bedrock, SageMaker and GPU infrastructure at enterprise scale.
Private deployment ready
Self-hosted, VPC and air-gapped options for regulated workloads.
Evaluation-first
Every build ships with a versioned evaluation harness.
No lock-in
Provider-abstracted so model choice stays yours.
Platforms behind the team
RLCatalyst and Spectra accelerate build and run.
Enterprise scale
350+ Data & AI specialists across regulated industries.
LLM solutions, tuned by industry
Data-control, cost and specialisation requirements vary sharply by sector.
Financial services
Document AI, risk and fraud models, and governed copilots that meet audit and compliance needs.
Healthcare & life sciences
Clinical and research assistants, knowledge retrieval and automation with data governance built in.
Retail & consumer
Personalization, demand forecasting, and customer and associate assistants across channels.
Manufacturing & supply chain
Predictive maintenance, quality vision, and agentic workflows for planning and operations.
Public sector
Secure, private-LLM deployments for citizen services, case work and knowledge access.
Technology & ISVs
Embed generative AI features, agents and RAG into your product with a partner who ships.
- AWS
- DataStax
- Salesforce
- Snowflake
Scaling this across the enterprise?
Our Enterprise AI Services practice brings generative AI, agents and LLMs to production at scale — with the governance, platform and operating model to keep them reliable, compliant and cost-controlled.
LLM engineering insights from Relevance Lab
LLM in a Box: generative AI inside the Trusted Research Environment
A pre-configured private AI appliance that brings LLMs to sensitive data without it leaving the environment.
Read More BlogRethinking enterprise analytics with GraphRAG and context-aware AI
Why data engineering stalls on missing context — and how GraphRAG makes data relationships explicit for AI.
Read More Case studyGoverned AI coding assistants with Research Gateway & Amazon Bedrock
Secure, self-service access to AI coding assistants with institutional control over data and usage cost.
Read MoreLLM Development Services: frequently asked questions
Content last reviewed: September 2026
Have an LLM build in mind?
Tell us the use case and we will come back with a retrieve-versus-fine-tune recommendation and a plan.
Talk to an AI specialist
Tell us where you are on your AI journey — a use case, a proof of concept, or a platform decision — and an AI consultant will come back with next steps and a rough shape for the engagement.