AI Development / Generative AI & LLM development
Generative AI and LLM development for real products.
A generative AI development company for real products: features built on large language models, grounded in your data and evaluated before they reach users.
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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.
Research & review assistant
Citation-grounded answers, each one traced back to its source document.
~50% faster review time AI · TravelCustomer support assistant
A support assistant grounded in live inventory data, available around the clock.
~35% fewer repeat contacts AI · Agent platformsAI agent platform
Agents retrieve, reason and act across systems, with human approval before high-stakes actions.
~40% less manual effortHave an AI idea you want to test against real data?
From idea to production. Four stages, with a working version reviewed at each one.
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Discovery
Map the use case, the systems and data involved, and what good looks like.
Discovery notes
- Goals and constraints
- Data available
- How success is measured
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Scope & architecture
Agree the architecture, success measures and delivery milestones.
Solution design
- Approach and architecture
- Evaluation plan
- Delivery milestones
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Build
Build against real data, with a working version reviewed at each milestone.
Working build
- Built against real data
- Results measured
- Review at each milestone
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Ship & support
Roll out to production with monitoring and a defined support window.
Production rollout
- Production rollout
- Monitoring
- Defined support window
Why Avinya for generative AI. Answers you can trace back to a source.
Answers with sources
Output is grounded in your own documents, with the source shown so it can be checked.
People stay in control
Approval checkpoints, escalation paths and exception review are designed in from day one.
Traceable by default
Citations, audit trails and logs, so outputs can be verified rather than trusted blindly.
Production, not demos
Built to run against real systems and real data, with monitoring once live.
Full-stack delivery
Data pipelines, models, product and integrations from one team.
AI and Web3 under one team
One studio for AI systems and, where it helps, on-chain infrastructure.
Engagement models. Pick the shape that fits where you are.
Dedicated team
A dedicated team that works as an extension of yours, from first release to scale.
Project-based
A defined scope, milestones and delivery date for a specific product or workflow.
Consulting & advisory
Architecture and readiness advice before you commit to a build.
What our clients say.
“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.”
“Avinya Labs built our AI travel chatbot … A highly capable team that delivers ahead of the curve.”
Guides on this topic. Plain answers to the questions buyers ask first.
RAG vs Fine Tuning: Which Does Your Business AI Need?
When to use each, when to combine them, and how cost and upkeep compare.
Read the guide → GuideHow to Build a RAG System for Your Company Knowledge Base
Architecture, components and stack choices, and when RAG is the wrong tool.
Read the guide → GuideEnterprise AI Deployment: Why Projects Stall in Pilot
The eight causes we see most, and a roadmap that gets AI live.
Read the guide →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.
How do you evaluate a generative AI development company?
Look for test sets built from your own content, guardrails against made-up answers and a way to measure quality over time. As a generative AI development company we build those in from day one, using the NIST AI Risk Management Framework.
Free tool
How ready is your business for AI?
Twenty questions, about four minutes. You get a score across five areas and what to fix first.