Generative AI services, concept to production
Generative AI consulting, custom generative AI development and implementation — built on GPT, Claude, Gemini, Llama and Mistral, with retrieval, guardrails and evaluation for the enterprise.
Quick answerGenerative AI services are the consulting, development and implementation capabilities used to build production applications on large language models and other foundation models. Relevance Lab covers GenAI consulting (use cases, feasibility, ROI), custom generative AI development (prompting, RAG, fine-tuning, agents, guardrails, evaluation) and implementation (deployment, integration and adoption) — model-agnostic across GPT, Claude, Gemini, Llama and Mistral.
Five ways to engage a generative AI partner
Start with consulting, a proof of concept, or a specific build — each links to a dedicated page.
Generative AI Development Services
Custom GenAI application development — prompting, RAG, fine-tuning, agents, guardrails and evaluation, model-agnostic.
ExploreGenerative AI Consulting Services
GenAI strategy, use case discovery, feasibility and ROI, model selection and a Responsible AI operating model.
ExploreChatGPT & OpenAI Development
Custom GPTs, Assistants API, function calling, RAG and fine-tuning on OpenAI.
ExploreClaude & Anthropic Development
Claude application development, tool use, long-context and RAG on Anthropic's models.
ExploreGenerative AI Implementation
Deployment, enterprise integration, guardrails in production, LLMOps and adoption.
ExploreEnterprise AI Services
Generative AI, agents and LLMs at scale — governance, platform and operating model.
ExploreWhat a generative AI build actually involves
Retrieval-augmented generation
Grounding answers in your documents, data and systems of record, with citations and permission-aware retrieval.
Fine-tuning & adapters
Where style, format, latency or task accuracy justifies it — usually alongside retrieval, not instead of it.
Agents & tool use
Multi-step workflows that call your systems, with typed tools, approvals and step-level tracing.
Guardrails & evaluation
Content filtering, hallucination and bias testing, and a versioned evaluation set run on every change.
Assistant & copilot UX
Streaming, sources, confidence, correction and safe defaults for anything the model can do.
LLMOps
Prompt and model versioning, monitoring of quality, latency and spend, and alerting wired to ownership.
Model-agnostic by design
We help you choose per use case — and keep the architecture portable so you are never locked in.
Custom GPTs, Assistants API, function calling and fine-tuning.
Long-context reasoning, careful instruction-following and tool use.
Multimodal builds and Vertex AI integration.
Open-weight deployments where cost, control or residency matter.
Efficient open models for latency- and cost-sensitive workloads.
Self-hosted models for security- and compliance-driven enterprises.
From first use case to GenAI in production
Frame
Pick the use case, define success, and confirm data and feasibility.
Prototype
A working PoC on real data in a few weeks — accuracy, cost and integration view.
Build
Production engineering — retrieval, prompts, agents, guardrails, evaluation, UX.
Integrate
Wire into enterprise systems, identity and workflows within your security boundary.
Operate
Monitor quality, safety and spend; feed real usage back into evaluation.
A generative AI partner built for the enterprise
AWS-native delivery
Generative AI on Amazon Bedrock, model work on SageMaker, GPU infrastructure at scale.
Platforms, not just people
RLCatalyst for AI operations and Spectra for data analytics accelerate build and run.
Strategy through to run
One partner from GenAI consulting and roadmap to build, integration and managed operations.
Responsible AI by default
Privacy controls, evaluation, bias and hallucination testing, audit logging and human oversight.
Evaluation-first
Every solution ships with a versioned evaluation harness, not a demo and a hope.
Enterprise scale
350+ Data & AI specialists and 150+ delivered projects across regulated industries.
Generative AI, tuned by industry
The same core services, adapted to the data, risk and compliance constraints of your 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.
Generative AI insights from Relevance Lab
Agentic AI vs Generative AI: key differences and enterprise use cases
Where generative AI ends and agentic AI begins — and which enterprise problems each is built for.
Read More BlogLLM 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 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 MoreGenerative AI Services: frequently asked questions
Content last reviewed: September 2026
Ready to build with generative AI?
Book a working session and get a shortlist of high-value GenAI use cases with a feasibility and ROI view.
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.