RAG or fine-tuning: which do we need?

Usually retrieval first. It grounds answers in your data and is faster and cheaper to maintain. Fine-tuning helps with tone, format and narrow tasks. We help you choose, and we 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, a clear ‘I do not know’ path, and an evaluation suite that measures the hallucination rate 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. We build cost monitoring in, so you see it per feature rather than as a surprise invoice.

LLM & Generative AI App Development

LLM & GenerativeAI Apps.

Assistants, copilots, search and agents built carefully on today's best models, with evaluation and guardrails.

Retrieval-augmented generation over your data

Fine-tuning and model selection for your domain

Agents that take real actions across your tools

Guardrails, evaluation and cost monitoring

Large language models are powerful, and a demo is not a product. The hard part is making them accurate, safe and affordable at scale. That is where we spend our time.

We build LLM applications people trust: copilots, search, chat and agents, grounded in your data and wrapped in proper evaluation.

What we build

Knowledge assistants and search. Ask a question over your documents, policies, tickets or product data and get a grounded answer with its sources. Retrieval quality is what decides this. Chunking, ranking and filtering tuned to your material, not a vector database bolted onto a prompt. If you are deciding between retrieval and training, our guide to RAG versus fine-tuning is the five-minute version of that conversation.

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

Agents that act. Beyond answering, agents take actions: look up the order, file the ticket, update the record, run the multi-step process. We build them with explicit permissions, a person in the loop where the stakes demand it, and a log of every step. See AI agent development for the detail.

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

Built to last

Every build ships with a test harness, so quality is a number rather than an impression. Guardrails against harmful or off-policy output. Monitoring for cost and latency. And a clean layer over the model itself, so when a better or cheaper model ships next quarter you change a config value rather than rewrite the product.

The process

Discovery, to pick the use case and the success metric. A prototype in weeks, on your real data. Then production hardening: integration, evaluation, guardrails, monitoring and rollout. You own everything: the prompts, the pipelines, the tests and the infrastructure.

Want a copilot, an assistant or an AI feature in your product? Talk to us. We reply within one working day.

LLM & GenerativeAI Apps.

Start with your best examples.

What we build

Built to last

The process

Before you ask.

Tell us aboutthe work.