AI & Generative AI Development

We build with OpenAI GPT models, Anthropic Claude, Google Gemini, and open-weight models such as Meta Llama and Mistral, including fine-tuned variants. Model choice is made per feature: we run the same evaluation set against candidate models and pick on quality, latency, cost and data-handling requirements, so a model can be swapped later without rewriting the product.

A focused AI feature integration (e.g., adding a chatbot or RAG-powered search to an existing product) typically takes 2–6 weeks. A full AI-native SaaS product from scratch typically takes 2–4 months because it also needs product design, authentication, billing, permissions, and release preparation. A narrower MVP has a separate estimate. We agree milestones after reviewing the data, integrations, and acceptance criteria.

Yes — RAG (Retrieval-Augmented Generation) is one of our specialties. We build secure pipelines that ingest your private documents (PDFs, databases, web content), create vector embeddings, and apply access controls before retrieval. We agree which data may be sent to a model provider, its retention requirements, and the hosting approach before implementation; private documents do not automatically mean private model processing.

Yes. We add AI capabilities to existing SaaS products, web and mobile apps, and APIs — LLM pipelines, RAG search, agents, and model fine-tuning where evaluation shows it is needed. We deliver the AI backend, prompts, evaluation set, UI components and cost monitoring as a self-contained feature, so your team does not need to re-architect the product.

We implement caching (including semantic caching with Redis), model routing so simpler requests go to cheaper models, prompt and context trimming, streaming to reduce time-to-first-token, and per-feature usage monitoring with budget alerts. We measure the baseline cost per request first, so savings are reported against real numbers rather than estimated.

The main factors are the workflow scope, data preparation, permission rules, external integrations, evaluation requirements, and expected usage. We separate implementation from recurring model, retrieval, hosting, and support costs. Share a representative workflow and your existing stack so we can discuss the assumptions behind an estimate.

Usually RAG first. Retrieval-augmented generation is the right fit when answers depend on your own documents or data that changes, and it lets responses cite sources and respect access permissions. Fine-tuning changes how a model behaves — a consistent format, tone or domain vocabulary — rather than what it knows, and is slower and more expensive to iterate on. Most products start with prompting plus RAG, then fine-tune only if evaluation shows a gap that retrieval cannot close; some use both.