AI & data
Applied AI with the discipline of production engineering.
Most AI projects fail in the gap between demo and operations. We close that gap: grounded retrieval, evaluated outputs, controlled cost and a security boundary your compliance team can accept.
Retrieval-augmented generation
Your documents, tickets, contracts and operational history become a queryable knowledge system. Content is indexed, ranked and cited so every answer remains traceable to its source.
Private inference boundaries
Self-hosted or region-pinned models where regulation or client policy requires it, with prompt and response logging under your governance rules.
Agents with real guardrails
Task automation with explicit tool permissions, deterministic fallbacks and human approval gates on anything that writes to a system of record.
Evaluation and cost control
Golden datasets, regression suites and token budgets per workflow. You get measurable answer quality and a predictable monthly bill.
Reference pipeline
How a StackZeta RAG system is assembled.
Every stage is observable and independently testable. That is what keeps the system maintainable once it is live.
01
Ingest & normalise sources
02
Chunk, embed & index
03
Retrieve & re-rank
04
Generate with citations
05
Evaluate & monitor
Bring us your data problem.
Tell us what the system has to achieve. We will tell you honestly what it takes, what it involves and how we would build it.
NDA-ready before discovery · Contracting through StackZeta, LLC, New Mexico, USA
- 01
Send the brief
What the system has to do, what it connects to and what is at stake.
- 02
Technical discovery
A senior engineer reviews the request and walks through constraints and integration surface with you.
- 03
Scoped engagement
A written architecture direction, delivery phases and support path before any contract is signed.