AddonAI / Foundation models at work

Generative AI Development

Custom LLM applications and content generation powered by foundation models.

Capabilities

What production generative ai development requires.

/ 01

LLM fine-tuning.

Fine-tune GPT, Claude, Llama, and Mistral models on your domain data for specialised, high-accuracy applications.

/ 02

Content generation.

Automated content pipelines producing marketing copy, reports, product descriptions, and documentation at scale.

/ 03

Multi-modal AI.

Applications that understand and generate text, images, audio, and video using cutting-edge diffusion and transformer models.

/ 04

Prompt engineering.

Optimised prompt architectures with chain-of-thought reasoning, few-shot learning, and automated prompt testing frameworks.

/ 05

Safety & guardrails.

Content filtering, output validation, and toxicity detection to ensure generated content meets compliance standards.

/ 06

Code generation.

AI-powered code assistants and automated code review tools that accelerate development velocity.

How we deliver
01.Requirements analysis
02.Model selection
03.Prompt architecture
04.Fine-tuning & testing
05.Safety validation
06.Production deployment
Tools we reach for
OpenAIClaudeLlama 3Stable DiffusionLangChainPineconeWeights & BiasesRLHF
FAQ

What teams ask about Generative AI Development.

  • Prompt architecture usually goes first — chain-of-thought reasoning, few-shot learning and automated prompt testing solve more cases than teams expect, at a fraction of the maintenance burden. Fine-tuning on your domain data earns its place when the task is narrow, repeated at volume, and prompting has genuinely plateaued.

  • We work with GPT, Claude, Llama and Mistral, and select per workload rather than standardising on one. Treating the model as a parameter of each step, rather than a fixed decision, keeps the next model release a configuration change and a regression run instead of a migration project.

  • Through evaluation, guardrails and cost control built in from the start, plus automated prompt testing frameworks that catch regressions before they reach users. The test set is part of the deliverable, because a system that worked once is not the same as one that keeps working.

  • Yes. We build multi-modal applications that understand and generate text, images, audio and video using diffusion and transformer models, and content pipelines producing marketing copy, reports, product descriptions and documentation at scale.

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