Generative AI Development
We design and ship Generative AI systems that hold up in production — LLM applications, RAG assistants, copilots, and autonomous agents grounded in your data.
Generative AI is easy to demo and hard to productionise. The gap between a promising prototype and a system your users trust is filled with retrieval quality, evaluation, latency, cost control, and guardrails. That gap is where we live.
Vector Pillar builds GenAI features end-to-end: we help you pick the right model, ground it in your proprietary data, evaluate it rigorously, and deploy it with monitoring and cost controls so it stays reliable as you scale.
What's included
- LLM application design — chat, copilots, summarisation, extraction, classification
- Retrieval-augmented generation (RAG) grounded in your documents and data
- Model selection and fine-tuning for domain accuracy and tone
- AI agents and tool-use workflows with human-in-the-loop controls
- Evaluation harnesses, guardrails, and hallucination mitigation
- Observability, cost monitoring, and prompt/version management
How we approach it
- Map the use case to the cheapest technique that works — prompting, RAG, or fine-tuning
- Stand up a measurable baseline with an evaluation set before scaling effort
- Iterate on retrieval, prompts, and models against that eval set
- Ship behind guardrails with monitoring, then expand coverage
What you get
- A production GenAI feature or service integrated with your product
- An evaluation suite you can re-run as models and data change
- Deployment, monitoring, and a cost/latency dashboard
- Documentation and knowledge transfer to your team
Technologies we use
Frequently asked questions
Should we fine-tune a model or use RAG?
Most teams need RAG first — it grounds a model in your data without training. Fine-tuning helps when you need a specific tone, format, or task behaviour that prompting can’t reliably produce. We help you decide based on your data and accuracy targets, and often combine both.
How do you stop the model from hallucinating?
We combine grounded retrieval, citations back to source documents, structured output validation, and evaluation sets that catch regressions. For high-stakes flows we add human-in-the-loop review and guardrails that refuse to answer out-of-scope questions.
Can you work with our existing product and stack?
Yes. We integrate with your codebase, data stores, and cloud rather than forcing a rebuild, and we hand over documented, maintainable systems.
Ready to talk generative ai development?
Tell us about your project and we'll respond within 24 hours with a clear next step.