Generative AI Development Services

Generative AI is easy to demo and hard to ship. Krazimo’s generative AI development services close that gap: we design, build, and deploy custom generative AI — copilots, chatbots, content engines, and RAG systems — that hold up in production. We’re a boutique generative AI development company of ex-Google engineers, and we evaluate every system against a real success metric before it goes live.

What we build with generative AI

Custom generative AI applied to your actual workflows, not generic templates:

• Copilots & assistants — domain-specific helpers embedded in your product or internal tools.
• Content & document generation — drafting, summarising, and transforming text at scale with your tone and guardrails.
• RAG systems — generative answers grounded in your approved data, with citations and access control.
• Generative agents — systems that don’t just generate, but act on what they produce.

Generative AI development company — how we work

As a generative AI development company, we start from the problem and the data, choose the right model (proprietary or open, fine-tuned or prompted), then build the full application around it. Senior engineers do the work — we cap active projects at ten so quality doesn’t get diluted.

GenAI use cases

Customer support copilots, internal knowledge assistants, marketing and sales content engines, code and data tooling, and document-heavy operations are where generative AI pays off fastest. If you’re unsure where it fits, that’s the first thing we help you figure out.

Evaluation & safety

Generative systems fail in ways traditional software doesn’t — hallucination, prompt injection, data leakage. We build evaluation harnesses and guardrails so output is measured and safe, not hoped-for. Many engagements pair with broader AI software development services or LLM and RAG development. Ready to build? Book a demo.

Generative AI we’ve shipped

• Professional AI translations — cut the average hours spent translating technical documents by ~80% with no appreciable quality loss, preserving file format end to end and flagging the passages that still need a human translator.
• PitchMee — gamified sales training that lets reps simulate dozens of realistic calls before they ever speak to a customer, ramping them faster through repeatable practice and feedback.
• Decarbonization strategy prototype — generative AI pointed at a hard, open-ended research problem, taken from idea to a working proof.

How a generative AI engagement works

Discovery and a defined success metric → a scoped pilot with a risk-free trial → build with evaluation and safety guardrails → deploy with monitoring. Senior, ex-Google engineers do the work; we cap active projects at ten.

Where generative AI pays off

Content and document generation at scale, copilots embedded in your product, customer and internal assistants, and turning unstructured material into something usable. We start where the output is measurable and the risk is controllable.

Industries we build generative AI for

Generative AI pays off wherever there’s high-volume, language- or content-heavy work. We build for financial services (report and memo generation, research synthesis), healthcare (clinical and administrative documentation, HIPAA-aware), retail and e-commerce (product descriptions and merchandising content at scale), marketing and media (on-brand content engines), legal and professional services (drafting and document review), and software teams (code generation and internal tooling). Each system is grounded in your data and tuned to your domain.

Our generative AI tech stack

We work across the leading foundation models — OpenAI GPT, Anthropic Claude, and Google Gemini, plus open models like Llama and Mistral where data residency or cost matters — and multimodal models for image, audio, and speech (e.g. DALL·E, Stable Diffusion, Whisper) when the use case calls for it. We ground them with retrieval and vector databases, orchestrate with frameworks like LangChain and LangGraph, and fine-tune when a custom model genuinely beats prompting. Model choice is per project — for accuracy, cost, and privacy — never one-size-fits-all.

Safety, guardrails & compliance

Generative systems fail in ways traditional software doesn’t, so safety is built in, not bolted on: evaluation harnesses that measure output quality, guardrails against hallucination and prompt injection, controls for data-leakage and IP/copyright exposure, and data handling aligned with GDPR and HIPAA-aware workflows for regulated industries. High-stakes output stays human-in-the-loop until it’s proven.