Generative AI Development Services

Generative AI development is the engineering discipline of building products and systems powered by large language models and other generative AI technologies. It covers LLM integration, custom AI agent development, retrieval-augmented generation (RAG) systems, fine-tuning proprietary models and deploying generative AI features into production applications. It differs from AI automation of the software lifecycle, and from software development where no generative model is involved.

Scrums.com is model-agnostic. 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 follows your use case, latency requirements, cost targets and data residency constraints, not vendor agreements. Where appropriate, multiple models work in combination, each routed to the task it suits best.

Retrieval-augmented generation (RAG) connects an LLM to a searchable knowledge base of your own content, so the model grounds its responses in your proprietary data without exposing that data to third-party training. RAG is the recommendation when you have a large corpus of internal documents, policies or product content; when responses must be specific and verifiable against a known source; or when hallucinations are unacceptable in regulated or high-stakes outputs. RAG often comes before fine-tuning, because it is faster to update and easier to audit.

Yes. Open-source models such as Llama and Mistral are fine-tuned on client-specific datasets for domain adaptation, tone consistency and task-specific accuracy. Fine-tuning needs sufficient labelled training data, a well-defined evaluation framework and an MLOps pipeline for model versioning, deployment and performance monitoring. The workstream advises on whether fine-tuning or RAG suits your use case before committing to either.

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

Data handling architecture is designed before any code is written. For regulated clients that means keeping sensitive PII out of third-party model API calls by pre-processing or redacting inputs, deploying open-source models on self-hosted infrastructure where data sovereignty requires it, role-based access controls on AI outputs, full audit logs of model inputs, outputs and decisions, and alignment with SOC 2, GDPR, POPIA and ISO 27001 where applicable.

Production LLM systems need attention that traditional software does not. Providers release new model versions, sometimes with breaking changes; prompt performance drifts as user behaviour evolves; retrieval quality degrades as content grows stale; and token costs change as usage scales. Maintenance covers model version evaluation and migration, prompt optimisation from production usage, corpus refresh and retrieval monitoring, cost optimisation and benchmarking against your quality thresholds.

The first sprint starts in under 21 days: discovery, architecture, environment setup, model selection and kickoff. Workstreams run inside a delivery plan; the Sprint plan is $4,699 / month for one work stream, and larger plans run more work streams in parallel. As planning ranges, not quotes: a focused LLM feature is $50K to $150K; an AI product build is $300K to $800K a year; an enterprise GenAI platform is $800K+ a year.