Generative AI Development

Go from idea to working product

Design and ship bespoke generative AI applications and product features, from early prototype to production.

Make outputs reliable, not just impressive

Tune prompts and models to your specific use case, tone and data for more consistent, dependable output.

Keep answers grounded in your knowledge

Use retrieval-augmented generation to ground generative systems in your own company knowledge so answers stay accurate and on-brand.

Built for teams that want to put generative AI into production

Generative AI systems create genuine value once they move past a demo and into a tool people rely on — grounded in the right data, tuned to a specific use case, and reliable enough for everyday use. At Yetiman, we design and ship generative AI systems end-to-end: from early prototypes to production-ready applications, tuning prompts and models to a specific use case, and grounding outputs in a company’s own knowledge so answers stay accurate and on-brand. This is especially useful for teams that want to move past experimentation and turn generative AI into a real product feature or internal tool.

we can help you with...

Capabilities

Product features powered by generative AI

Design and ship generative AI capabilities inside existing products, from early prototype to production.

Standalone GenAI tools

Build new applications where generative AI is the core of the experience, not an add-on.

Prototype to production

Move fast on early prototypes, then harden the parts that need to hold up in real use.

Prompt design for a specific use case

Shape prompts around your tone, task and data instead of relying on generic defaults.

Model fine-tuning

Tune models on your own examples where it improves reliability beyond prompting alone.

Evaluation and iteration

Test outputs against real examples and keep refining as usage patterns become clearer.

Grounding in company knowledge

Connect generative systems to your own documentation, data and internal content.

Accurate, on-brand answers

Reduce made-up or generic answers by retrieving the right context before generating a response.

Knowledge that stays current

Keep answers aligned with information as it changes, instead of relying on a fixed model snapshot.

Service Integrations / Tools

LLM Providers

Vector Databases

Custom APIs

FAQs

Straight answers to the questions we hear most about this service.

We design and ship generative AI systems end-to-end — from early prototypes to production-ready applications and product features, not just demos.

By tuning prompts and, where it helps, fine-tuning models to a specific use case, tone and dataset, then testing and iterating against real examples rather than relying on generic defaults.

RAG grounds a generative system in your own company knowledge before it answers, so responses stay accurate and on-brand instead of generic or made up — and can be kept current as your information changes.

Teams that want to move past experimentation and turn generative AI into a real product feature, internal tool or customer-facing application.