Nylivo is a multi-tenant platform where a knowledge graph and a fleet of governed, metered AI agents turn intent into shipped product — with provenance on every number and a human on every consequential action. Live in fintech. Extending to retail, real estate, and enterprise technology.
New products are provisioned as workspaces on shared infrastructure — not rebuilt from scratch. Every layer is domain-agnostic; the industry is configuration.
A graph-based knowledge layer with structural provenance. Every fact carries its source; every answer cites it. Semantic memory and multi-hop reasoning run over a live federation of operational, transactional, and document data — fed by connectors that already speak Git, issue trackers, SQL, and object storage.
What makes agents safe to run at all: a single entry point with auth and rate limits, strict per-tenant isolation, a credentials authority that mints every service-to-service token, complexity-tiered model routing, per-workspace LLM metering, and backup/DR.
Metered agents that plan and act — never unattended. Intent → LLM-authored plan → human-approved execution → outcomes learned back into the Graph. Every model call is metered and feature-attributed, on shared SDKs so a new agent inherits the guardrails.
The other three are what you run on; Studio is how you compose them. A supervised workflow walks a workspace admin's intent through design → approval gate → provision → wire → verify, writing every step to a run ledger — so standing up a new product is a reviewable, replayable sequence instead of a migration project.
A single loop turns a template into a running, self-improving product. The human stays on the consequential steps; the Brain keeps the learning.
A template captures what the product should do — the domain, the signals, the guardrails.
An LLM authors a concrete, reviewable plan — provisioning, wiring, and the checks it must pass.
A human gates every consequential step. Nothing that moves money or state runs unattended.
Outcomes are graded on real, closed results and fed back to the Brain — so the next plan is better.
The doctrine below is enforced in code — the same discipline a regulator would ask for, built into the substrate rather than bolted on.
The qualifying question is never “is this industry regulated?” — it's does being wrong cost you? Wherever the answer is yes, the same substrate applies.
A deliberate ladder: read the market, then act on the book, then plan the life. Each step inherits the one before it — and a fabricated number at any step is a regulatory problem, not a bug.
The same guarantees, different stakes: a hallucinated price, spec, or stock level is a liability — so provenance is a feature, not overhead.
Where the source of truth is a document: a lease says what the income is. Get a term wrong and you've mispriced an asset, not filed a typo.
Where the source of truth is the deploy log and the incident timeline — not whoever remembers what shipped last Tuesday. A wrong root-cause guess burns error budget while on-call chases the wrong service; the same graph that ranks a trade or a lease ranks a suspect, cited and confirmed by a human before anything fires.
The same rails reach further — insurance and supply chain. Each new domain is a workspace, not a rebuild.
QuantVantage.today is live in production — every number cited, every consequential action confirmed. It's the proof that governed AI can ship where being wrong is expensive.