Generative AI Development Services

What is generative AI development?

Generative AI development is the engineering discipline of building products and systems powered by large language models and other generative AI technologies. This includes LLM integration, custom AI agent development, retrieval-augmented generation (RAG) systems, fine-tuning proprietary models, and deploying generative AI features into production applications. It is distinct from general AI automation (using AI to speed up internal SDLC workflows) and from standard software development where no generative model is involved.

What is the difference between your AI and Automation service and your Generative AI Development service?

Our AI and Automation Services deploy AI agents inside the software development lifecycle to automate QA testing, code review, deployment orchestration, and DevOps operations. The goal is faster, more reliable software delivery. Generative AI Development is different: it means building LLM-powered features and products into your own applications so your customers or employees can interact with AI. One improves how software is built; the other determines what the software does.

Which LLM models do you work with?

We are model-agnostic. Our engineers have production experience with OpenAI (GPT-4o, o1, o3), Google Gemini, Meta Llama (open-source, self-hosted), Mistral, Anthropic Claude, and Grok. Model selection is driven by your use case, latency requirements, cost targets, and data residency constraints, not by vendor agreements. Where appropriate we use multiple models in combination, routing tasks to the model best suited to each function.

What is RAG and when do you recommend it?

Retrieval-augmented generation (RAG) is an architecture that connects an LLM to a searchable knowledge base of your own content, allowing the model to ground its responses in your proprietary data without exposing that data to third-party training. We recommend RAG when: you have a large corpus of internal documents, policies, or product content; you need responses that are specific and verifiable against a known source; or you cannot afford hallucinations in regulated or high-stakes outputs. RAG is often the right choice before fine-tuning, because it is faster to update and easier to audit.

How do you prevent GenAI hallucinations in production?

Hallucination prevention is an engineering problem, not just a prompt engineering problem. Our approach combines: retrieval-augmented generation to ground responses in verified source documents; output validation pipelines that check responses against known facts or structured constraints; confidence scoring and fallback handling when the model is uncertain; human-in-the-loop review gates for high-stakes outputs; and continuous monitoring through SEOP that tracks output quality as a first-class delivery metric. No LLM system eliminates hallucinations entirely, but well-engineered systems contain them to acceptable rates for their use case.

How quickly can a generative AI development team be deployed?

Under 21 days from signed contract. We start from a permanent bench of senior AI engineers, not from a post-contract recruitment race. The 21-day window covers discovery, architecture design, environment setup, model selection, and first sprint kickoff. Focused integrations with clear scope can move faster. Complex enterprise systems with multiple data sources and compliance requirements take longer to architect but not longer to staff.

Can you fine-tune models on our proprietary data?

Yes. We have experience fine-tuning open-source models (Llama, Mistral) on client-specific datasets for domain adaptation, tone consistency, and task-specific accuracy. Fine-tuning requires sufficient labeled training data, a well-defined evaluation framework, and an MLOps pipeline for model versioning, deployment, and performance monitoring. We advise on whether fine-tuning or RAG is the better approach for your use case before committing to either.

How do you handle data privacy and compliance in GenAI systems?

Data handling architecture is designed before any code is written. For regulated clients, this typically means: keeping sensitive PII out of third-party model API calls by pre-processing or redacting inputs; deploying open-source models in self-hosted infrastructure where data sovereignty requires it; implementing role-based access controls on AI outputs; maintaining full audit logs of model inputs, outputs, and decisions; and aligning with SOC 2, GDPR, POPIA, and ISO 27001 where applicable. We do not treat compliance as a post-launch checklist item.

What does ongoing LLM maintenance involve?

Production LLM systems require ongoing attention that traditional software does not. Model providers release new versions (sometimes with breaking changes), prompt performance drifts as user behavior evolves, RAG retrieval quality degrades as content grows stale, and token costs change as usage scales. Our ongoing maintenance includes: model version evaluation and migration planning, prompt optimization based on production usage patterns, RAG corpus refresh and retrieval quality monitoring, cost optimization as scale increases, and performance benchmarking against your quality thresholds. This is what keeps a GenAI feature performing at launch quality twelve months later.