Every number, verifiable.
Each figure in an answer traces to a query against your governed data — checked by an independent verification pass before it reaches you. If a claim can't be supported, RQVST says so instead of improvising.
Private beta · Enterprise intelligence for finance & operations teams
Analysis into decisions.
Outcomes into intelligence.
Every business decision starts with a request. RQVST (pronounced: “request”) turns that request into governed analysis, an evidence-backed decision, and a measurable outcome. Then it learns what worked, so the next request starts smarter.
Evidence trail behind every number
Refuses claims it can’t verify
Your data stays in your tenant
Verified claim
Causal trace
Office-ready brief
A closed loop, not another dashboard.
Dashboards stop at the chart. RQVST carries the work through the decision — and brings what happened back into the system.
01
Connect
Source systems, warehouses, spreadsheets, and external market data.
02
Model
A governed semantic layer: catalogs, definitions, metrics, and quality checks.
03
Analyze
Root-cause, causal, and strategic analysis with the evidence attached.
04
Decide
Verified recommendations with confidence, rationale, and a decision record.
05
Act
Briefs, scheduled agents, and workflows that move the decision into operations.
06
Learn
Outcomes are measured against the recommendation and become memory.
Why teams switch
Numbers your CFO can defend. Decisions your board can trace.
General-purpose AI gives confident answers no one can audit. RQVST is built the other way around: governance first, verification always, and a memory of every decision it helped make.
A semantic layer it builds with you.
RQVST catalogs your sources, proposes the model, and keeps one set of blessed definitions with lifecycle governance — so "active customer" means the same thing in every answer, and deprecated metrics can't sneak back in.
Causal answers that refuse to guess.
"Did the promotion work?" runs through a causal engine that checks its identifying assumptions before estimating. When a check fails, you get an honest "can't attribute this cleanly" — not a plausible number that happens to be wrong.
Decisions become memory.
Facts you teach it, patterns it learns, decisions you log, and outcomes it measures all feed back into context. The system gets smarter about your business every cycle — and that memory compounds.
Audit ready
Deep analytics with the evidence trail attached.
Structured, verified work product for operating reviews, board prep, vendor conversations, category decisions, and executive briefs.
Connect
Bring together internal systems, external signals, catalogs, and semantic models.
Investigate
Run root-cause and strategic analysis across performance, market, and operating context.
Verify
Double-check findings with source evidence, calculations, and an audit-ready trail.
Learn
Log the decision, track the outcome, and feed the result back into memory.
Use cases
Built around the reviews operating teams already run.
The recurring decision cycles where context, speed, and evidence quality determine whether the team acts — or debates the spreadsheet.
PE Portfolio Ops
01
100-day diagnostics and value-creation reviews.
Decompose EBITDA variance, identify margin drivers, benchmark portfolio companies, and prepare evidence-backed operating plans.
“What is driving the margin gap versus our LBO model?”
Supply Chain
02
Supplier performance, fill rate, and inventory triage.
Break aggregate metrics down to SKU, supplier, location, and revenue impact so teams know which shortfalls deserve action.
“Which suppliers are responsible for our fill-rate gap?”
Category Management
03
Category reviews, price benchmarking, and assortment calls.
Analyze share, pricing, sell-through, margin, and item performance to separate real opportunities from noisy movement.
“Where are we priced above the category average?”
Why RQVST
Dashboards show. General AI guesses. RQVST proves.
BI tools stop at the chart, and general-purpose AI will happily invent a number. RQVST does the investigation, shows its work, records the decision, and learns from the outcome.
Dashboards + general AI
Charts that need an analyst to explain
Confident numbers nobody can trace
Definitions that drift by team
One-off answers, forgotten by next quarter
Insights that never become actions
With RQVST
Root-cause narratives with evidence attached
Every figure traced to a governed query
One set of blessed definitions
A decision ledger with measured outcomes
A loop that compounds into memory
Connect → Model → Analyze → Decide → Act → Learn
Questions
The questions operators ask first.
How do you keep AI from making up numbers?
Three ways. Every figure in an answer must trace to a query RQVST actually ran against your governed data model — a faithfulness check verifies this before the answer is shown. Metric definitions are governed with an approval lifecycle, so agents use your blessed definitions rather than guessing from column names. And causal questions run through an engine that tests its statistical assumptions first — if the checks fail, it declines to give an effect estimate rather than inventing one.
Where does our data live?
In your own isolated tenant, enforced with row-level security at the database layer. Connections to your source systems are read-only. For pilot engagements we deploy single-tenant, so your data shares infrastructure with no one.
What can we connect?
QuickBooks (native sync with incremental updates), Postgres and warehouse connections, and CSV / Excel / Parquet uploads. RQVST also pulls external context on demand — market data, economic series from FRED / EIA / BLS, and news sentiment — so internal numbers can be analyzed against the world they live in.
What does the private beta look like?
A guided pilot: we connect your data with you, build the governed model together, and run your first real decision cycles side by side. Single-tenant deployment, invoice-based — no credit card, no self-serve setup to fight through.
Private beta
Put a decision loop under your business.
Join the private beta and see how RQVST connects your data, verifies deep analysis, and turns every decision's outcome into operating memory.