Generative AI consulting that ships to production

Generative AI consulting is the process of helping businesses identify, design, and implement generative AI systems such as LLM-powered chatbots, RAG knowledge bases, AI agents, and document automation tools in a way that delivers measurable business value.

A focused generative AI project typically takes 3 to 8 weeks with Agility. A RAG system or chatbot can go live in 3 to 4 weeks. A multi-agent workflow or fine-tuned model deployment typically takes 6 to 10 weeks. We scope tightly and ship fast.

Yes. We have extensive experience building systems that keep sensitive data inside your own cloud environment, never sending it to third-party model APIs. We support Azure OpenAI, AWS Bedrock, and self-hosted open-source models for full data sovereignty.

Healthcare, insurance, manufacturing, finance, retail, and logistics are our primary verticals. Each has specific compliance and data requirements that we handle as part of the implementation.

General AI consulting covers the full spectrum: predictive models, data engineering, automation, and analytics. Generative AI consulting focuses specifically on systems built on large language models: LLM integration, RAG knowledge bases, AI agents, and document and content automation. Agility does both, but a generative AI engagement is scoped around LLMs, prompt and retrieval design, hallucination control, and token-cost optimisation rather than classical machine-learning pipelines.

Single-use-case generative AI deployments (a RAG chatbot, a document-automation pipeline, or one AI agent) typically start at $30,000 to $60,000 with fixed-scope pricing. Larger multi-agent or fine-tuned-model programmes run higher. We scope every project to a fixed price with milestones before build. See our generative AI consulting cost breakdown or book a free scoping call for a concrete number.

It depends on the workflow, but the pattern is consistent: generative AI removes manual, language-heavy work. Our deployments have cut document processing from days to minutes, deflected large shares of support tickets, and saved analyst hours daily. We define the target metric, handling time, deflection rate, hours saved, in the first workshop and measure against it, so ROI is tracked rather than assumed.

There is no single best model; it depends on your task, data-residency needs, and budget. We are model-agnostic and often route between them: GPT-5 or Claude Opus 4 for the hardest reasoning, smaller or open-source models (Llama 4, Mistral, Qwen) for high-volume or on-premise work where cost and data control matter. We benchmark candidates on your actual data before committing.

Yes. RAG consulting is one of our most-requested services. We act as your RAG consultant end to end: connecting your documents, wikis and databases to an LLM, building the retrieval and grounding layer, and shipping a production system that returns cited, auditable answers, typically in 3 to 4 weeks. We work with Pinecone, Weaviate, Chroma and pgvector and can deploy on-premises for full data control.

A RAG consultant designs and builds retrieval-augmented generation systems: they scope the knowledge sources, engineer the ingestion and chunking pipeline, select the embedding model and vector database, build hybrid semantic and keyword retrieval with reranking, and tune the system to minimise hallucinations while grounding every answer in a cited source. At Agility this is delivered by senior engineers and evaluated on accuracy, latency and cost before go-live.