Privorum designs and ships AI workflows for teams that need real product outcomes — not a demo that falls apart on the second week in production.
We help answer practical questions such as:
- Which parts of this workflow actually benefit from an LLM, and which should stay deterministic?
- How do we keep cost, latency, and failure modes predictable?
- Where does retrieval belong, and where is it overkill?
- How do we evaluate this beyond vibes, and catch regressions before users do?
- What does the human review path look like when the model is wrong?
Typical engagement areas
- LLM-backed workflow design and orchestration
- retrieval-augmented generation (RAG) architecture and indexing
- evaluation harnesses, guardrails, and regression suites
- cost, latency, and reliability tuning
- integration with existing backend services and data stores
Example
- Production AI workflows inside existing backend platforms, delivered with senior engineering judgment rather than prompt-only prototypes.