Trusted across 20+ countries by Fortune 500 companies and growth-stage brands
We build production-grade generative AI, from custom LLM applications and RAG systems to copilots and content engines, engineered around your data, your workflows and measurable business outcomes. Over a decade of experience, 250+ digital solutions delivered.
Get a 30-Minute AI Strategy Session, FreeGenerative AI development is the design and engineering of AI systems that create new content, such as text, code, images or structured output, using large language models and related techniques. Noseberry builds custom generative AI applications grounded in your own data through methods like retrieval-augmented generation and fine-tuning, with governance and guardrails built in for safe, reliable production use.
Key takeaways
We engineer generative AI across the full lifecycle, from use case to production, with security and governance built in.
We build bespoke applications on top of large language models, tuned to your domain and connected to your systems.
We ground models in your own knowledge base so answers are accurate and current, not hallucinated.
We build conversational assistants and in-product copilots for support, sales and internal operations.
We build engines that generate text, marketing copy, summaries and images at scale, with governance.
We build generative tools that accelerate engineering, from code assistants to test generation.
We fine-tune open-source and commercial models on your data for better accuracy and lower cost.
We embed generative AI into your existing products, apps and operations.
We combine generative AI with agents that reason and act across your tools to complete multi-step tasks.
instant, grounded answers that cut resolution time.
content, personalisation and proposal generation.
document processing, summarisation and drafting.
in-app copilots and generative features.
code assistants and faster delivery.
Timelines depend on scope. A proof of concept ships in 2 to 4 weeks, a production-ready single assistant or copilot in 4 to 6 weeks, and a full enterprise generative AI system in 12 to 20 weeks. Cost depends on model choice, data volume and integration complexity, and we share a clear budget range on your first call before any commitment.
Cost efficiency
Faster go-to-market
Shorter project timelines
Book the free 30-minute strategy session and we will scope a generative AI use case with you.
Book nowYou see a working proof of concept early, before committing to a full build.
We map your data, systems and goals to understand where you stand today.
We prioritise use cases and hand you a costed, phased plan.
We validate the highest-value use case fast, with clear success metrics.
We engineer and connect the solution into your production stack.
We ship, monitor and tune, so value compounds after launch.
Models & LLMs
Frameworks & agents
Data & vector
Cloud AI
Security and responsible AI are built in from day one, including custom guardrails on model outputs, private LLM hosting, encrypted data pipelines and role-based access controls. We build to industry standards including GDPR, HIPAA and SOC 2, deployed on AWS, Google Cloud and Azure with monitoring and audit trails.
Challenge
Support and underwriting teams were slow to surface answers buried in policy documents.
Solution
A RAG copilot grounded in policy and claims data, with source citations and guardrails.
Impact
93% of fraud caught pre-payout, an estimated $4.2M saved annually.
Challenge
Manual appraisal intake was slow and inconsistent across markets.
Solution
Document-AI and RAG intake to extract and draft valuations across three markets.
Impact
40% faster property valuations with higher consistency.
Challenge
Generic merchandising and copy were capping conversion.
Solution
Generative product content plus a personalization engine on a governed data lakehouse.
Impact
+28% lift in conversion rate.
Sector-anonymised outcomes shown until named clients are approved.
AI, Cloud and Data is our core, no generalist dilution.
We build generative AI that reaches production, not demos that stall.
We choose the right model, commercial or open source, on fit, cost and risk.
2M+ lives touched, 15+ Fortune 500 clients, 250+ solutions across 20+ countries.
Full lifecycle from discovery to deployment and optimisation.
Fractional access to senior generative AI engineers and data scientists.
Defined deliverables like a PoC or an integration, with fixed timelines.
Prototype and validate generative AI ideas fast in a sandbox.
Retraining, monitoring and cost optimisation after launch.
It is the design and engineering of AI systems that create content, answers, code or media, using large language models and techniques like retrieval-augmented generation and fine-tuning, grounded in your data.
RAG grounds a model in your knowledge base at query time for accurate, current answers. Fine-tuning adjusts the model itself on your data for domain accuracy and lower cost. We recommend the right mix per use case.
Both commercial and open source, including GPT, Claude, Llama, Mistral and Gemini. We are vendor-neutral and choose on fit, cost and risk.
A proof of concept ships in 2 to 4 weeks, a production assistant in 4 to 6 weeks, and a full enterprise system in 12 to 20 weeks.
Through retrieval-augmented generation, guardrails, evaluation, source citation and human-in-the-loop controls, plus monitoring in production.
Yes. We embed generative AI into your apps, products and operations through APIs and integrations with minimal disruption.
Book your free 30-minute AI strategy session and we will scope a use case and the fastest path to production.
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