LLM & Generative AI App Development

Large language models are powerful, but a demo is not a product. The hard part is making them accurate, safe and affordable at scale — and that is where we focus.

We build LLM and generative AI apps that people actually trust: copilots, smart search, chat experiences and autonomous agents, grounded in your data and wrapped in proper evaluation.

What we build

Knowledge assistants and RAG search. Ask questions over your documents, policies, tickets or product data and get grounded, cited answers. Retrieval quality is the whole game here — chunking, ranking and filtering tuned to your corpus, not a vector database bolted onto a prompt. (Deciding between retrieval and training? Our RAG vs fine-tuning guide is the five-minute version of the conversation.)

Copilots inside your product. Drafting, summarising, extracting and transforming — embedded in the workflow your users already have, with your data as context and your rules as guardrails.

Agents that do things. Beyond answering, agents take actions: looking up an order, filing the ticket, updating the record, running the multi-step process. We build them with explicit permissions, human-in-the-loop where stakes demand it, and logs that let you audit every step. If this is your use case, see our dedicated AI agent development page.

Customer-facing chat. Support and product assistants that resolve real queries — covered in depth under AI chatbot development.

Built to last, not just to demo

Every build ships with an evaluation harness (so quality is a number, not a feeling), guardrails against harmful or off-policy output, cost and latency monitoring, and a clean abstraction over the model itself — so when a better or cheaper model ships next quarter, you swap it in a config change, not a rewrite.

The process

Discovery to pick the use case and success metric; a prototype in weeks against your real data; then production hardening — integration, evaluation, guardrails, monitoring — and rollout. You own all of it: prompts, pipelines, evals, infrastructure.

Want a copilot, assistant or AI feature in your product? Talk to us — we reply within one business day.

Frequently asked questions

RAG or fine-tuning — which do we need?

Usually RAG first: it grounds answers in your data and is faster and cheaper to maintain. Fine-tuning helps for tone, format and narrow tasks. We help you choose — and wrote a full guide comparing the two.

Which models do you use?

We are model-agnostic and pick the best fit for accuracy, latency and cost — often a mix, with the ability to swap as the field moves.

How do you stop the model making things up?

Grounding through retrieval, guardrails that check outputs before users see them, honest 'I don't know' paths, and an evaluation suite that measures hallucination rates instead of hoping. No system is perfect; ours are measured.

Can it run on our private cloud or on-prem?

Yes. We deploy against private model endpoints or self-hosted open-weight models when data cannot leave your environment.

What does an LLM app cost to run?

Inference for most business apps runs tens to hundreds of dollars a month at moderate usage, and we build cost monitoring in so you see it per feature, not as a surprise invoice.

LLM & Generative AI Apps