AI & Generative AI Development — Custom Agents, LLM Integration & RAG Systems

Fine-tuning modifies the model's weights by training it on your domain-specific data — the model 'learns' your terminology, style, and patterns. This is effective when you need the model to consistently follow a specific format, adopt domain vocabulary, or replicate a particular tone. RAG (Retrieval-Augmented Generation) keeps the base model unchanged but enhances it with a retrieval step: relevant documents from your knowledge base are fetched at query time and injected into the prompt as context. RAG is better when your data changes frequently, when accuracy on specific factual queries is critical, or when you need the model to cite sources. Most production AI systems use RAG rather than fine-tuning because it's cheaper, faster to iterate, and produces more auditable outputs.

Timeline depends on the type of AI system and data availability. A focused AI feature (classification, summarisation, or extraction) integrated into an existing product typically takes 6–10 weeks from scoping to production. A full RAG system with document ingestion pipeline, vector database, custom chunking strategy, and production-hardened retrieval runs 10–16 weeks. A custom AI agent with multi-step reasoning, tool use, and human-in-the-loop escalation typically takes 4–6 months. The longest part is usually data preparation — if your data isn't clean, labelled, and accessible, that adds weeks regardless of model complexity.

Not necessarily — the data requirements depend heavily on the AI approach. For RAG-based systems, you need the documents, knowledge bases, or structured data you want the model to reference — often a few hundred to a few thousand records is enough to demonstrate value. For fine-tuning, you typically need several hundred to several thousand labelled examples. For training a custom model from scratch, you need far more — which is why we almost never recommend it. Modern foundation models (GPT-4, Claude, Llama) already encode enormous general knowledge, so the question is usually how to constrain and specialise their outputs with your specific data, not how to train a model from zero.

Hallucination prevention is an architecture challenge, not a prompting challenge. The most effective technique is RAG: every LLM response is grounded in specific retrieved passages from a verified knowledge base, and the model is instructed to answer only from those passages. Beyond RAG, we implement output validation layers that check responses against structured schemas, confidence scoring that flags low-certainty answers for human review, citation requirements that force the model to reference source documents, and automated evaluation pipelines that test accuracy regressions when the system is updated. For high-stakes applications (medical, legal, financial), we also add human-in-the-loop review steps for any output above a defined risk threshold.

Yes — and this is one of our most common engagements. Most AI features are added to existing products via API integration rather than rebuilding from scratch. We identify the specific user workflows where AI creates value (document drafting, semantic search, classification, summarisation, anomaly detection), design the integration architecture to minimise changes to your existing codebase, and ship the AI feature as a modular addition. If your existing software has clean API boundaries — which we design for in custom development engagements — adding AI capabilities in a future phase typically takes weeks, not months.

Responsible AI is a design requirement, not an audit checkbox. We implement output guardrails that filter for harmful, biased, or non-compliant outputs before they reach users. We use evaluation datasets that test for demographic bias and document the results. We build explainability logging so every AI decision can be traced back to the inputs and retrieved context that produced it. For regulated industries (healthcare, finance, insurance), we assess applicable AI regulations, design for explainability requirements, and document the AI system's decision-making process for regulatory review. We also stay current with emerging AI regulation — the EU AI Act, NIST AI Risk Management Framework, and sector-specific guidance — and design systems that are compliant today and adaptable as regulation evolves.