Custom generative AI development services
A generative AI development company for enterprises — solution design, RAG and prompt pipelines, fine-tuning, agents, guardrails and evaluation, built model-agnostic and shipped to production.
Quick answerGenerative AI development services are the engineering of a production application on top of foundation models — architecture design, retrieval-augmented generation (RAG), fine-tuning where it pays off, agent and tool integrations, guardrails, an evaluation harness, and integration with your data and workflows. Relevance Lab builds brand-agnostic solutions and plugs OpenAI, Anthropic, Google or open models in as interchangeable back-ends.
What custom generative AI development covers
Solution & architecture design
Use-case-fit model selection, reference architecture, and a build plan that ships value early.
RAG & data grounding
Ingestion, chunking, embeddings, hybrid and graph retrieval, re-ranking and citations.
Fine-tuning & adapters
When consistent style, format, latency or accuracy justifies it — measured against RAG-only baselines.
Agents & tool use
Reasoning loops, typed tools into your systems, approvals and step-level tracing.
Guardrails & evaluation
Content filtering, jailbreak and prompt-injection defence, and a versioned evaluation suite.
Application & UX engineering
Front and back end, session handling, and AI UX patterns that make the feature trusted.
When to retrieve, when to fine-tune
| Dimension | RAG | Fine-tuning |
|---|---|---|
| Keeps answers current | Yes — update content, not the model | No — retrain to update knowledge |
| Auditability | High — cites source passages | Low — knowledge is baked in |
| Per-user permissions | Enforced at query time | Not possible in the weights |
| Style / format consistency | Prompt-dependent | Strong — learned from examples |
| Token cost & latency at scale | Higher — context is sent each call | Lower — shorter prompts |
| Best used | Grounding in changing enterprise data | Fixed tasks, tone, structured output |
Built on the model that fits — swappable later
See our dedicated ChatGPT & OpenAI Development service.
See our dedicated Claude & Anthropic Development service.
Multimodal, open-weight and efficient options per use case.
For data-control and compliance-driven builds.
A path from PoC to production
Prototype
Working PoC on real data in a few weeks — accuracy, cost and integration view.
Harden
Evaluation harness, guardrails, error handling and security review.
Integrate
Enterprise systems, identity and data, inside your security boundary.
Release
Environments, CI/CD, observability and a supported go-live.
Improve
Real usage feeds the evaluation set; quality and cost tuned continuously.
A generative AI development company that ships
AWS-native delivery
Bedrock, SageMaker and GPU infrastructure at enterprise scale.
Platforms behind the team
RLCatalyst and Spectra accelerate build and run.
Evaluation-first
Every solution ships with a versioned evaluation harness.
Portable architecture
Provider-abstracted so you can benchmark and switch models.
Responsible AI by default
Privacy, evaluation, bias and hallucination testing, audit logging.
You own the output
Your code, your models, your infrastructure-as-code, with handover.
- AWS
- DataStax
- Salesforce
- Snowflake
Need it connected, not just built?
Our AI Integration Services wire OpenAI, Claude and open LLMs into your CRM, ERP, data warehouse and internal APIs with secure, governed pipelines.
Generative AI engineering insights
Rethinking 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 More BlogAgentic 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 MoreGenerative AI Development Services: frequently asked questions
Content last reviewed: September 2026
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.
Have a generative AI build in mind?
Tell us the use case and we will come back with a proof-of-concept plan, an architecture sketch and a rough timeline.
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.