---
title: Generative AI and LLM Development Services | Avinya Labs
description: Generative AI and LLM development by Avinya Labs: products and features built on large language models, with grounding, guardrails and evaluation.
url: /service-pages/service-ai-generative-llm.html
---

[AI Development](/service-pages/service-ai-development.html) / Generative AI & LLM development

# Generative AI and LLM development for real products.

Products and features built on large language models, grounded in your data and evaluated before they reach users.

[Book a call](https://calendly.com/abbylester/30-mins-meeting) [See the work](/service-pages/service-ai-generative-llm.html#solutions)

## Generative AI services. From first prototype to a feature people rely on.

### Use case discovery

Find where generation adds value and where it adds risk.

### LLM application development

Assistants, drafting tools and summarisation built into your product.

### Prompt & workflow design

Structured prompts and multi-step flows for dependable output.

### RAG & grounded answers

Retrieval over your documents, with every answer traced back to its source.

### Guardrails

Limits on what the model may produce, with checks on output.

### Evaluation

Output tested against representative cases before launch.

### Model selection

Choose models per task on quality, speed and cost.

### Integration

Generative features delivered inside your existing tools.

### Monitoring

Quality and cost tracked once live.

## Related AI work. Delivered engagements in neighbouring areas. None of them is a Generative AI and LLM development project.

AI · Legal

### Research & review assistant

Citation-grounded answers, each one traced back to its source document.

~50% faster review time

Link: /case-studies/ai-legal-assistant.html

AI · Travel

### Customer support assistant

A support assistant grounded in live inventory data, available around the clock.

~35% fewer repeat contacts

Link: /case-studies/ai-travel-chatbot.html

AI · Agent platforms

### AI agent platform

Agents retrieve, reason and act across systems, with human approval before high-stakes actions.

~40% less manual effort

Link: /case-studies/enterprise-ai-agent-platform.html

## Have an AI idea you want to test against real data?

[Book a call](https://calendly.com/abbylester/30-mins-meeting)

## From idea to production. Four stages, with a working version reviewed at each one.

### Discovery

Map the use case, the systems and data involved, and what good looks like.

Discovery notesDone

  - Goals and constraints
  - Data available
  - How success is measured

### Scope & architecture

Agree the architecture, success measures and delivery milestones.

Solution designDone

  - Approach and architecture
  - Evaluation plan
  - Delivery milestones

### Build

Build against real data, with a working version reviewed at each milestone.

Working buildDone

  - Built against real data
  - Results measured
  - Review at each milestone

### Ship & support

Roll out to production with monitoring and a defined support window.

Production rolloutDone

  - Production rollout
  - Monitoring
  - Defined support window

## Engagement models. Pick the shape that fits where you are.

Ongoing

### Dedicated team

A dedicated team that works as an extension of yours, from first release to scale.

Fixed scope

### Project-based

A defined scope, milestones and delivery date for a specific product or workflow.

Advisory

### Consulting & advisory

Architecture and readiness advice before you commit to a build.

## What our clients say.

AI platform for an insurance broker

> “Avinya Labs built our full-stack AI solution, delivering scalable infrastructure, optimized AI workflows, and a seamless user experience. Their expertise accelerated our launch and significantly reduced execution risk.”

SandraProject lead, Insurance broker

Result~2.5x Faster processing speed [Read case study →](/case-studies/ai-platform-insurance-broker.html)

AI chatbot for a travel platform

> “Avinya Labs built our AI travel chatbot … A highly capable team that delivers ahead of the curve.”

LauraCo-founder, Travel platform

[Read case study →](/case-studies/ai-travel-chatbot.html)

## Generative AI & LLM development FAQs.

### What can generative AI do for our product?

Draft, summarise, answer questions and turn unstructured input into structured data. Discovery narrows this to the use cases worth building.

### How do you keep output reliable?

Grounding in your data, guardrails on output, and evaluation on representative cases before launch.

### Do you build RAG systems?

Yes. Retrieval-augmented generation finds the relevant passages in your documents first, then writes an answer from them with citations, so reviewers can check every claim.

### Which AI models do you use?

We evaluate models against your real workflow and choose per task. The architecture is designed so models can change later without rebuilding the product.

### How long does a project take?

It depends on scope and integrations. We agree milestones up front and review a working version at each one.

## Ready to turn your vision into reality?

Tell us the problem, the data, and where manual effort sits today. We'll map the path from MVP to scale.

[Book a call](https://calendly.com/abbylester/30-mins-meeting) [See the work](/work/index.html)

Or email [contact@avinyalabs.co](mailto:contact@avinyalabs.co)