Generative AI Development Company

Generative AI development is building production software on large language models and other generative models, so the system produces text, code, images or structured decisions grounded in your own data. It covers use-case scoping, retrieval engineering, prompt and model design, evaluation, guardrails, integration, deployment and monitoring.

Nine GenAI development services, from strategy to running systems. They are GenAI strategy, RAG pipelines, agents and copilots, LLM applications, model integration and fine-tuning, document intelligence, prototype modernisation, private LLM hosting and LLMOps.

Teams with knowledge that is hard to search, a lot of repetitive language work, or a product an assistant would set apart. Support, claims, contracts and onboarding are common starting points. If your data is thin or the task is fully rule-based, GenAI is usually the wrong tool, and we will say so.

Generative AI development generally starts from $17,000, and agentic automation for one workflow from $8,000. Most of the difference above that comes from how ready your content is, how many systems it connects to and how strict your compliance needs are. The exact price is fixed when discovery ends, within four weeks.

Most builds reach production in 4 to 8 weeks, and a focused RAG pilot can be live in about 4. Multi-agent systems, private hosting and compliance reviews add time, and you get a dated plan before the build starts.

RAG fetches passages from your knowledge base at the moment a question is asked, so answers stay current and can cite a source. Fine-tuning retrains the model to learn a format, tone or narrow task, and its knowledge freezes at training time. Most business cases are solved by RAG plus good prompts, which is cheaper and easier to update.

It is the chain that turns your documents into knowledge a model can use: ingest, chunk, embed, store, retrieve, re-rank, then generate with citations. Answer quality is usually decided by chunking and retrieval, not by which model you pick. The pipeline section above shows each stage.

We build the retrieval layer, and it is usually the bigger part of the work. That covers parsing, semantic chunking, embedding and indexing, hybrid search with re-ranking, and permission-aware filtering. Prompting an API with no retrieval is exactly the chatbot wrapper we do not build.

With four layers working together. Retrieval grounds each answer in your content. Structured output schemas make responses machine-checkable. Confidence thresholds send uncertain cases to a person. And an automated evaluation suite runs on every change, so accuracy drops never reach users.

It depends on the task, and mixing is normal. Long documents go to frontier models with large context. High-volume classification goes to smaller or open models, code to code-specialised models, and regulated work to privately hosted open models. One abstraction layer makes each choice a setting, not a rewrite.

Yes. We work across six modalities: text, images, speech and audio, code, video, and structured or synthetic data. Multimodal builds are increasingly normal, such as an assistant that takes a voice note and replies with a cited answer. See our AI voice agents for the speech side.

It can, but only with grounding and measurement. Deflection is real when answers come from your own help content, uncertain cases go to people, and you track resolution rate rather than message count. Ungrounded chatbots usually increase escalations instead. Our AI chatbot development page covers support use cases.

Traditional AI classifies, scores or predicts from structured data. Generative AI creates new content such as text, code or images, usually from a foundation model. Many production systems use both: a classifier routes the request and a generative model drafts the reply. Our machine learning team covers the traditional side.

Yes, in the practical sense. We deploy open models such as Llama or Mistral inside your own environment and compress them to cut compute cost. They connect to private retrieval that respects each user’s permissions. Nothing calls an external API, so you keep full control of the data and avoid a per-token vendor bill.

Yes, where relationships matter more than passages. GraphRAG combines a knowledge graph with vector search. It answers multi-step questions, such as which suppliers a policy change affects, far better than passage retrieval alone.

Yes, and it is one of the most common requests we get. We audit the prototype, then add what production needs: proper retrieval, an evaluation harness, guardrails, tracing, cost controls and integration. You keep what already works instead of starting again.

Brand and safety rules are built into the pipeline. Style and negative prompts and reference images keep output on brand. Filters catch unsafe results, and a person approves anything before it is published.

Measure four things against a baseline taken before the build. They are time saved per user each week, cost per resolved task, automation or deflection rate, and accuracy or error reduction. Subtract build cost plus yearly running cost, including inference. We set the baseline in discovery, so the comparison is real.

If you serve users in the EU, yes. From 2 August 2026, people must be told when they are talking to an AI, and AI-generated content must carry a machine-readable mark. Systems already on the market before that date have until 2 December 2026 for marking. We build both into the pipeline. See security and compliance.

Ask how many GenAI systems they run in production with real users, then ask to see a sample evaluation report. Check their retrieval method, named hallucination controls, model neutrality and cost per query estimates. Get their data policy in writing and confirm you will own the prompts and evals. Finally, ask when that gen AI development company last told a client GenAI was the wrong tool.