Aurum Quanta GenAI.
Large language models tuned to your data and run from your cloud account.
We build LLM-powered features for drafting, summarising, translating, code assistance, and creative production. The model produces a draft and a person signs it off before anything ships to a user.
Retrieval-augmented generation, prompt engineering, evaluation frameworks, caching, guardrails. Runs against your data in your cloud account. The weights, the prompts, and the eval sets all stay with you when we hand over.
Free text in. Structured JSON out.
Pulls a meeting/event out of an email, message, or note.
{
title: string | null,
starts_at: ISO datetime | null,
ends_at: ISO datetime | null,
location: string | null,
attendees: string[],
notes: string | null,
confidence: number
}Click Extract to see Claude produce JSON conforming to the schema on the left.
Paste any free-text and pick a target schema. Claude is constrained to produce JSON that conforms to the schema - no extra fields, no missing fields, types enforced. This is the most leveraged GenAI pattern for production systems: unstructured input, structured output, predictable shape.
Note · this is a simplified demo
A real engagement would wire this into a typed client SDK so the output is parsed, validated, and routed without glue code. We'd add per-field confidence handling, fallback rules for ambiguous inputs, evaluation harnesses on labelled examples, and prompt-injection guards on user-controlled text. The five schemas above are illustrative; production schemas are bespoke to the use case and versioned alongside the application.
Tokens, attention, and everything in between.
An LLM is not magic - it's matrix multiplications at scale. Tokens flow through stacked attention blocks; each layer rewrites the representation a little. Understanding what's happening inside the box is what separates a working integration from a fragile one.
Concrete deliverables.
RAG pipelines
Grounded retrieval over your documents, with source citations and confidence scoring. When retrieval doesn't find anything strong enough, the system says so.
Fine-tuned assistants
Domain-specific voice and terminology, trained on the documents your business uses every day.
Evaluation & monitoring
Regression tests and eval harnesses that block prompt or model changes if they regress. Drift detection on production traffic that pages on-call before users notice.
Open-weight options
Open-weight models like Llama or Mistral where the use case allows it. Frontier APIs from OpenAI or Anthropic where it doesn't. Either way, the weights and the prompts live in your repository.
If the model isn't confident, it doesn't guess.
# rag/answer.py: answer with grounding, refuse without it
def answer(query: str, k: int = 5, min_grounding: float = 0.62) -> Response:
docs = retriever.search(query, k=k)
grounding = max((d.score for d in docs), default=0.0)
if grounding < min_grounding:
return Response(text="I don't have a confident source for this.", sources=[])
return llm.generate(query, context=docs, instructions=GROUNDED_PROMPT)Refusal beats hallucination. Confidence threshold tuned per use case.
How it would unfold.
Discovery
Data audit, use-case shortlist, eval metrics agreed in writing.
Pilot
Working RAG prototype or fine-tune on your real data, measured against the agreed metrics.
Production
Guardrails, caching, UX integration, observability, and a reviewer workflow for edge cases.
Ongoing
Monthly drift review, prompt tuning, and retraining as your data and product evolve.
Tools we reach for on this kind of work.
Common questions.
Does our data train your models?
No. We work in your cloud, your data stays in your environment, and we don't reuse client data to train other systems.
Can we run this fully on-prem?
Yes, using open-weight models like Llama or Mistral. You trade off frontier capability for full control. We help you make that call.
Will it hallucinate?
All LLMs can. RAG with citations and an eval set that explicitly tests for hallucinations dramatically reduces the rate. We measure it as a number and tune against that number on every prompt change.
Let's build it.
A 30-minute discovery call. We'll tell you whether we're the right shop for this.
Book a discovery call →