AI & Automation

We work with OpenAI (GPT-4o, GPT-4), Anthropic (Claude), Google (Gemini), and open-source models via Ollama or Hugging Face. Most projects use OpenAI or Anthropic — both are mature, reliable, and well-documented. We'll recommend the right model for your use case based on capability, cost, and latency requirements.

Every AI feature we build includes cost controls: per-user token limits, budget caps, usage monitoring, and alerting when spend approaches thresholds. We also optimise prompts and choose the smallest model that meets your quality requirements — which can reduce per-request costs by 70–90% compared to always using the largest model.

RAG stands for Retrieval-Augmented Generation. Instead of relying solely on what the AI model was trained on, a RAG pipeline retrieves relevant content from your own documents or data in real time and feeds it to the model as context — so it can answer accurately based on your specific knowledge.

You need RAG if you want an AI feature that answers questions about your internal docs, product catalogue, support articles, or any data that isn't in the public training set. If you're doing general-purpose generation (summaries, rewriting, classification), RAG is usually not needed.

We build guardrails into every AI feature: system prompt constraints that define what the AI should and shouldn't do, fallback responses when the model doesn't have a good answer, content filtering for inappropriate outputs, and an admin dashboard where you can monitor and review responses. For RAG-powered chatbots, grounding the model on your documents significantly reduces hallucination.

Integrating a basic AI feature like a Q&A chatbot typically runs $8,000–$20,000. A full GPT-powered SaaS with usage billing, prompt management, and abuse prevention is $25,000–$60,000+. Ongoing API costs depend on usage volume and model selection — a chatbot handling 500 queries per day costs roughly $8–$108/month depending on whether you use a mini or flagship model.

For most task types they are closely matched. GPT-4.1 has an edge on structured tool use and following complex multi-step instructions. Claude Sonnet 4.6 tends to perform better on long documents, nuanced writing, and tasks requiring careful reasoning. The practical answer is to run both on your actual prompts and choose based on measured output quality for your specific use case.

Yes — if the app is built with an abstraction layer (a common interface your code calls rather than calling the SDK directly). We build this by default. Switching models becomes a configuration change, not a rewrite. This also lets you route different features to different models — using a cheap model for classification and a flagship model for high-stakes generation.

Not always. For small datasets (under ~10,000 chunks), a similarity search over embeddings stored in PostgreSQL with pgvector works well — no additional managed service, no extra cost. Dedicated vector databases like Pinecone or Weaviate make sense above a certain scale or when you need real-time updates at high volume. Most business chatbots are well within the pgvector range.

Accuracy depends on the quality of your source data, the chunking strategy, and the retrieval configuration. A well-built RAG system on clean, well-structured documentation typically achieves 85–95% answer accuracy. Hallucinations are reduced significantly because the model is answering from retrieved context rather than memory. The most important factor is the quality of your source documents — garbage in, garbage out.