Nylivo gives every enterprise a brain that knows where everything is — it learns what your systems can answer, fetches only what a question needs, and can prove how it knows. 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.
A production run, replayed — the platform's own brain, asked two questions. One had no source, so it declined. One it fetched live and cited. Nothing below is mocked; only the typing is animated.
What's our customer churn forecast for next quarter?
I don't have a source for that. No connected system claims to answer churn — and I don't guess. declined · no resolvable citation
What did we spend on LLM calls yesterday?
not in memory → checking the map → the usage ledger claims this answer → fetching…
$2.4403 across yesterday's model calls. Cited: the usage ledger, fetched live for this question. citation resolves ✓
Unedited outcome, condensed timing. The refusal is the feature: an answer must point at real retrieved data, or it declines.
New products are provisioned as workspaces on shared infrastructure — not rebuilt from scratch. Every layer is domain-agnostic; the industry is configuration.
A knowledge layer that works the way expertise does: it learns what each of your systems can answer without copying their contents, keeps only what's worth remembering — every fact with its source — and when a question's answer isn't in memory, it retrieves it live from the system that has it, cited like everything else. Connectors teach it your enterprise in minutes, not crawl-weeks.
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.
Enterprise search connects your knowledge so AI can answer. Nylivo was built for the harder jobs: a brain that knows what every system can answer without copying it, agents that act — never unattended — and answers that prove themselves, verified in production every day.
The doctrine below is enforced in code — the same discipline a regulator would ask for, built into the substrate rather than bolted on.
Nylivo is built for decisions you have to defend. The qualifying question is never “is this industry regulated?” — it's does being wrong cost you? Wherever the answer is yes, the same rails apply.
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.
Taking our first design partners now. If your domain is one where being wrong costs you — bring a system, and watch a brain learn what it can answer in minutes, on your own physically separate tenant.