Skip to content

Service

LLM Services & RAG

Chat with your data. Get answers you can trust. We build RAG pipelines and LLM integrations for enterprises.

Discuss this service All services

LLM Services & RAG

What we can do

Custom LLM deployment, fine-tuning, and enterprise integration.

RAG pipelines connect models to your internal knowledge bases for accurate, context-aware answers. Databricks Genie Workspace setup lets your team query data in plain English.

“We treat LLM deployment as an engineering challenge: design robust RAG architectures, evaluate models against your data, and build governance guardrails from the start.”

System flow / LLM Services & RAG

How RAG works

Connect a user question to approved knowledge, evaluate retrieval, then return a response with security controls.

01 / ASK / User query

A question or natural language request

How We Deploy LLMs

  1. 01

    RAG Pipeline Delivery

    • RAG pipeline deployment
    • Advanced vector search
    • Contextual accuracy
    • Latency optimisation
  2. 02

    LLM Fine-Tuning

    • LLM fine-tuning: GPT, Claude, Llama, Mistral
    • Domain adaptation
    • RLHF
    • Efficient fine-tuning
  3. 03

    Genie Workspaces

    • Databricks Genie Workspace
    • Natural language data queries
    • Domain-specific environments
  4. 04

    LLM Security & Access

    • Secure LLM infrastructure
    • PII protection & prompt defence
    • Role-based access
    • Compliance-ready deployment

Scope, made tangible

Explore the work areas

Select a work area to see what it covers, including the technologies used for it. Scope and work products are tailored to your environment.

AREA 01

RAG Pipeline Design

Production RAG systems with vector search, contextual accuracy, and fast retrieval.

  • Vector search & embeddings
  • Knowledge base integration
  • Contextual accuracy
  • Databricks
  • Vector search
  • Embeddings
  • RAG

Delivery path / LLM Services & RAG

One delivery framework, tailored to this system.

Each phase is shaped around the systems, controls and operating needs of this service.

  1. 01 PHASE

    Knowledge & Access Scoping

    Review knowledge sources, use cases, model constraints and access needs.

    Check / output

    Knowledge and use-case scope

  2. 02 PHASE

    Retrieval & Context Design

    Design retrieval, vector search, embeddings and model integration.

    Check / output

    Retrieval and context checks

  3. 03 PHASE

    Accuracy & Latency Tuning

    Evaluate responses and tune for accuracy and latency.

    Check / output

    Model and retrieval evaluation

  4. 04 PHASE

    PII & Prompt Defences

    Apply PII protection, prompt defence and role-based access.

    Check / output

    Security and access review

  5. 05 PHASE

    Production Quality Watch

    Monitor and refine the deployed pipeline or Genie workspace.

    Check / output

    Production and quality checks

Engagement outputs

Typical work products

Final outputs are agreed during discovery, based on your environment and the work in scope.

  • 01

    RAG architecture

    Retrieval, vector search and knowledge-source connections.

  • 02

    Evaluation approach

    Checks for retrieval quality and accuracy.

  • 03

    Secure deployment

    PII protection, prompt defence and role-based access.

  • 04

    Genie workspace setup

    Natural language access to business data.