Generative AI Development Services

What generative AI can do for your business today

Large language models (LLMs) can read, summarise, classify, extract and write text at a level that was impossible a few years ago. The value comes when they are connected to your documents, systems and workflows — not when staff copy and paste into a chat window.

Generative AI solutions we build

How we build reliable LLM applications

1. Use case & data1–2 weeks

   Pick a measurable use case, gather real examples and agree what “good” looks like.

2. Prototype2–4 weeks

   Retrieval, prompts and model choice tested against an evaluation set.

3. Harden3–6 weeks

   Guardrails, permissions, logging, cost controls and human-review steps.

4. Integrate & launch2–6 weeks

   Connected to your tools (CRM, help desk, intranet) and rolled out with training.

Choosing models and hosting

We design so you can switch models as better or cheaper ones appear, without rewriting the application.

Retrieval-augmented generation (RAG) explained

Most business AI assistants use a pattern called retrieval-augmented generation. Instead of relying only on what a language model learned during training, the system first searches your own documents for the passages most relevant to the question, then gives those passages to the model with instructions to answer only from them and cite its sources. This keeps answers current — update a policy document and the assistant knows immediately — and makes them verifiable.

The quality of a RAG system depends far more on the retrieval step than on the model. We pay close attention to how documents are split into chunks, how tables and PDFs are parsed, how search combines keyword and semantic matching, and how results are filtered by user permissions. We then test the whole pipeline against real questions and measure answer accuracy before anyone relies on it.

Controlling cost and latency

LLM usage is billed per token, so costs grow with volume and prompt length. We design for efficiency from the start: routing simple tasks to smaller, cheaper models and reserving the most capable models for complex reasoning; caching repeated answers; trimming prompts to what the model actually needs; and streaming responses so users see results immediately. Per-feature and per-user budgets with alerts prevent surprises on the monthly bill.

Matching the use case to the right approach

Not every generative AI idea needs the same technique. Picking the right approach keeps costs down and makes quality easier to control.

Most products combine two or three of these. Fine-tuning is rarely the first step; it becomes worthwhile once a prompted solution is proven and volume is high.

Evaluation: how we know the output is good

Generative AI quality cannot be judged by trying a few examples. We assemble a test set of real inputs with expected outputs or grading criteria, then score every version of the system against it — using automated checks, model-based grading for subjective qualities and human review for the most important cases. This turns “it seems better” into measurable progress and protects against regressions when prompts or models change.

• Factual accuracy against source documents
• Format and schema compliance
• Tone and policy adherence
• Refusal of out-of-scope or unsafe requests
• Cost and response time per request

From prototype to production

1. Use-case workshop1 week

   Identify tasks, users, data and risks; choose one high-value starting point.

2. Prototype2–4 weeks

   Build a working version on real data and measure it against the evaluation set.

3. Production build4–10 weeks

   Add authentication, integrations, guardrails, monitoring and an admin interface.

4. Launch and tuneOngoing

   Review real conversations, improve prompts and retrieval, and manage model updates.

Timelines are typical for a single focused application.

Designing the user experience around AI

Good generative AI products make it easy to trust and correct the output. Users should see where an answer came from, edit drafts before sending, give quick feedback and know when they are talking to AI. Streaming responses improve perceived speed; clear limits on what the assistant can do reduce frustration. These details often matter more to adoption than the choice of model.

• Citations or links to sources
• Editable drafts rather than automatic sending
• Thumbs up/down feedback captured for improvement
• Clear handover to a human where needed

Explore AI agents, RAG development and OpenAI API development.

Guardrails we build in

Guardrails keep a generative AI product inside its intended purpose. They combine instructions to the model with independent checks in code, so safety does not depend on the model always following its prompt.

• Input filtering for prompt-injection attempts and off-topic requests
• Output checks for personal data, prohibited content and policy breaches
• Grounding checks that answers are supported by retrieved sources
• Rate limits and per-user quotas to control abuse and cost
• Logging and review tools so issues can be investigated quickly

Signs a use case is ready for generative AI

• The task is mostly reading, writing or summarising text
• Good and bad outputs can be recognised by your team
• Mistakes can be caught before they cause harm
• The volume justifies building and maintaining the system

If a use case meets all four, a prototype on real data is usually the fastest way to confirm the value.

Security and data privacy

• Business API tiers that do not train on your data
• Access control so users only retrieve documents they are allowed to see
• Personal data masking and audit logs for every request
• Prompt-injection defences and output filtering
• Cost limits and monitoring per user and feature

Need the AI to take actions, not just answer? See AI agent development.

This page is part of AI Development & Data Engineering Services — see the full overview for related services, approach and pricing.