AI Development / 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.
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 notesDone
- Goals and constraints
- Data available
- How success is measured
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Scope & architecture
Agree the architecture, success measures and delivery milestones.
Solution designDone
- 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 buildDone
- 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 rolloutDone
- Production rollout
- Monitoring
- Defined support window
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.”
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.
Or email contact@avinyalabs.co