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Revenue Intelligence Workspace

Turn revenue data into decisions your team acts on

Flolyt finds the revenue threats hiding in your business before they become lost revenue. Your team and AI agents then fix them together, in one shared workspace.

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SSO, RBAC, and audit logs160+ connected data sourcesNo per-seat pricing
ROOM 2471·opened by Repeat & Decay Agent4:12 ago

Second-order rate fell from 38% to 27% for everyone who signed up after 4 March.

Customers affected
0
At risk · 90 days
$0
Confidence
0.91
−11 pts
EvidenceWeight
The delivery fee appears after the cart, not before it. Introduced in the 4 March checkout release.
Measured · product analytics + release log
Customers whose first order arrived late reorder 31% less often.
Corroborated · support tickets + order data
The first-order discount is not reapplied, so order two reads as a price rise.
Indicative · pricing table
Two competitors dropped their free-delivery threshold in March.
Unverified · third-party report
Show delivery fees before the cart, and hold the first-order discount through order two.
Approved · Dana O.Objection open · Finance
In the roomDOAMRVTKRunningRDDQFlolyt workspace

Connects to the systems you already run

PostgresStripeShopifySnowflakeBigQuerySegmentHubSpotZendeskSlackLinearJiraPaystackBrazeKlaviyoPostgresStripeShopifySnowflakeBigQuerySegmentHubSpotZendeskSlackLinearJiraPaystackBrazeKlaviyo

Flolyt doesn't just show you revenue problems. It saves you from them

Connect your data. Flolyt agents watch continuously for churn risk, payment failures, repeat purchase decay, and expansion signals. When they find a threat, they open a Room with the cause, the dollar impact, and the fix. Your team and AI act together—fast.

Figures from the example workspace used throughout this site.

11 days0

From revenue break to a named owner

$0

Preserved by a single room, measured against a holdout

0

Specialist AI agents included in every workspace

0

Per-seat charges, on any plan

The platform

Powered by your own live data.
Built for every stage of the revenue lifecycle.

One workspace for finding revenue leaks, deciding what to do about them, and proving what worked.

See all capabilities
Revenue leakage90 days · USD
Checkout friction
$412,000
Failed payments
$286,400
Discount leakage
$183,900
Gross margin
Unavailable
Recovered · Q3
$312,000

Revenue leakage map

Revenue at risk mapped by segment, lifecycle stage, market, and currency, with every figure linked to the rooms and sources behind it.

Explore leakage
Room 2471Live
Second-order rate fell 38% → 27%
$412,000 at riskCause found
DOAMRVRD1 approval waiting

Rooms

One shared workspace where marketing, product, support, finance and multiple AI Agent actually solve the problem together. No more meetings. No more email chains. Just decisions that get made.

Inside a room
Data healthHealthy
Postgres · orders2m ago
Stripe · payments1m ago
Zendesk · ticketsStale 6h
Warehouse · COGSNot connected

Live data fabric

Continuous sync with Postgres, Stripe, Shopify, and 160+ more, with fields mapped, duplicate customers merged, and stale sources flagged automatically.

Business memory3 matches
Room 1180 · retry window+$88.4K
Room 1642 · win-back SMS+2.1%
Repeat & Decay cites Room 1642: messaging alone did not hold this cohort. Fix the checkout first.

Business memory

Every room, approval, and outcome retained, so agents cite what already worked and new teammates can read the reasoning behind past decisions.

Agent permissionsWorkspace
Open a room, draft a campaignAutomatic
Message a customerProposed
Change a priceProposed
Write to the CRMBlocked

Governance

Automatic, proposed, or blocked, set per agent and per action, with a named approver required on anything that reaches a customer or a price.

How it works

Find revenue threats before they become lost revenue
Turn cross-functional teams from silos to a single revenue room

Continuous monitoring across every connected source

Thirteen (13) specialist agents run against Postgres, Stripe, Shopify, and 160+ more, with no event instrumentation and no weekly exports to wait for.

Machine · autonomous
Agent activity · last nightRunning
Repeat & Decay scanned 12 cohorts1 anomaly
Data Quality checked 6 sourcesConfirmed real
Involuntary Churn reviewed 4,180 failuresNormal
Acquisition Quality re-scored 5 channelsNormal

Rooms that open with the cause attached

Each room arrives with a readable problem statement, the population affected, the exposure in dollars, and findings filed at four visible weights.

Machine · drafts, never sends
EvidenceWeight
Delivery fee moved below the cart in the 4 March release.
Measured
Late first delivery cuts reorder rate by 31%.
Corroborated
Discount not reapplied, so order two reads as a price rise.
Indicative
Competitor changed free-delivery threshold.
Unverified · excluded from the estimate

Human approval on every outward action

Agents propose and people release. Approvals record who and when, work syncs to Jira or Linear, and recorded objections stay visible after the decision.

Human · accountable
Decision11:04
Show delivery fees before the cart, and hold the first-order discount through order two.
Approved · Dana O. · ProductObjection open · Revan S. · marginFLO-284 synced to Linear

Outcomes that make the next decision faster

What was tried, what it returned against the holdout, and what failed—cited by your agents the next time the same pattern appears.

Both · compounding
Room 2471 · closedDay 9
Measured against 10% holdout$312,000
Time to resolution9 days
Finance objectionPartly upheld
Filed to memoryCited 3 times since
Included agents

Thirteen specialist AI agents.
Included in every workspace.

Each agent owns one revenue question and opens a room when the answer changes. Import agents from Claude or OpenAI, or build your own.

RD

Repeat & Decay

Tracks second and third order rates by cohort and flags the week they bend.

IC

Involuntary Churn

Separates customers who left from customers whose card failed. They need opposite things.

AQ

Acquisition Quality

Measures each channel against the LTV it returns, not the CAC it promised.

LP

LTV Predictor

Projects lifetime value per segment and revises it as behaviour changes.

CS

Churn Specialist

Ranks who is leaving next by value at stake, with the reason attached.

DO

Discount Optimizer

Finds discounts applied to customers who would have bought anyway.

RF

Revenue Forecaster

Projects 30, 60 and 90 days by segment and market, and flags a miss early.

DQ

Data Quality

Confirms an anomaly is real before anyone acts on it.

C3

Customer 360

Resolves identities across systems and keeps one profile per person.

ED

Experiment Designer

Turns a proposal into a test with a holdout and monitors significance.

+

Your own agents

Import from Claude or OpenAI, or build natively. Scoped to the data you allow.

@

Role assistants

CMO, Head of CS, Product and Finance personas you can mention in any room.

Hit your revenue quota this quarter—don't discover the gap in week 10

You make the call. Flolyt brings the evidence, the exposure, and the owner.

POST /v1/rooms200 OK
// open a room from your own signal
room = flolyt.rooms.create(
  metric   = "second_order_rate",
  segment  = "acquired_after_2026-03-04",
  agents   = ["repeat_decay", "data_quality"]
)
room.exposure   → 412000
room.confidence → 0.91
Accuracy and control

Insights you can audit.
Actions you control.

Every claim carries a weight and a source you can open, and anything that reaches a customer needs a named approver.

A machine did thisA person must actMoney at riskMoney preserved
  • 01

    No estimates in place of missing data

    If COGS is not connected, margin reads Unavailable on the goal tracker, the scenario planner and every segment that would use it.

  • 02

    Four evidence weights on every finding

    Measured, corroborated, indicative or unverified. The weight sits in the room, next to the source it came from.

  • 03

    Named human approval on outward actions

    Anything that reaches a customer or changes a price is proposed, not sent. You decide what is automatic, proposed or blocked.

  • 04

    Objections recorded alongside decisions

    A recorded objection stays visible after approval. When the result lands, the record shows whose call it was and who dissented.

  • 05

    SSO, RBAC, audit logs, and data residency

    SSO via SAML or OIDC, role-based access down to the data source, full audit logs, and GDPR and CCPA tooling.

Built for revenue teams

One workspace for every team that touches revenue

Revenue & growth

Cause, exposure, and owner in hand before the weekly review.

Product & engineering

The release that bent the curve, with the revenue consequence on the ticket.

Customer success

Daily outreach ranked by value at stake, not by inbox order.

Finance

Every recovered dollar traced to a room, an approver, and a holdout.

Support

Frontline patterns turned into weighted evidence, in front of decision makers.

Pricing

Pricing that scales with the revenue you protect

Choose the plan that matches your annual revenue. Every teammate is included at no additional cost, on every plan.

Billed monthly. Switch to annual at any time.
Free
Under $500K revenue
$0
No card, no expiry
  • 1 workspace, unlimited teammates
  • 10 data sources
  • 2 agents · Repeat & Decay, Churn Specialist
  • 3 active rooms
  • Lifecycle view and basic segments
  • 1 campaign a month · 500 credits
  • Community support
Start for free
Growth
$500K – $5M revenue
$2,500/mo
 
  • Everything in Free, plus
  • 5 workspaces · 50 data sources
  • 5 agents · adds LTV Predictor, Involuntary Churn, Discount Optimizer
  • Unlimited rooms and segments
  • Journey canvas and leakage map
  • Business memory · 6 months
  • 5 campaigns a month · 2,000 credits
  • A/B testing · Slack and email alerts
Start free trial
Scale
$5M – $50M revenue
$6,000/mo
 
  • Everything in Growth, plus
  • 20 workspaces · 100 data sources
  • All 13 agents and the agent builder
  • Unlimited business memory
  • Forecasting, scenarios, health scoring
  • Multi-touch attribution and playbooks
  • Unlimited campaigns · 10,000 credits
  • Jira, Linear, Asana and Trello sync
  • SSO, audit logs, API, priority support
Talk with our team
Enterprise
$50M+ revenue
From $15,000/mo
Scoped to your volume
  • Everything in Scale, plus
  • Unlimited workspaces and sources
  • Custom agents and marketplace access
  • White-labelling and embedded views
  • Data residency and advanced governance
  • Custom integrations and webhooks
  • 99.9% uptime SLA
  • Named CSM, onboarding and training
Talk to sales

Revenue bands are self-declared and reviewed annually. Crossing a band mid-contract takes effect at renewal, never retroactively.

Questions people actually ask

Why price on revenue instead of seats or contacts?
Flolyt works best when marketing, product, support, and finance are all in the same room, and per-seat pricing would discourage exactly that. Revenue bands also track the value at stake, so you pay in proportion to what is at risk rather than to the size of your team.
How long until the first useful room?
Agents begin scanning as soon as your first source is connected, and most workspaces see a substantiated room in the first session. If nothing surfaces in week one, Flolyt will name the additional source most likely to change that.
What happens if an agent is wrong?
Every finding carries a weight and a source you can open, so a weak claim is visible rather than persuasive. Nothing that touches a customer or a price goes out without a named person releasing it, and results that contradict a diagnosis are written to memory and change how that agent reasons next time.
Do you replace Braze, Klaviyo or Customer.io?
No. Those platforms send messages well, and Flolyt works alongside them. Flolyt decides what is worth sending and why, then either triggers the campaign or hands the segment to the platform you already run. A message is one possible action out of many, and a checkout fix or a pricing change is often the better one.
Where does our data live, and who can see it?
Flolyt reads from your sources on a schedule you control, with role-based access down to the individual source. Every human and agent action is written to an audit log. Enterprise adds data residency options and customer-managed keys.
Can we bring our own agents?
Yes. Import agents from Claude, OpenAI or your own stack, or build them in Flolyt. Imported agents inherit the same governance settings and evidence requirements as the built-in ones: they draft, they cite their sources, and a person releases the action.

Start for free. Upgrade when a room pays for it.

Every plan includes the full workspace. Upgrading adds agents, data sources, memory, and governance.

How it works

See how a room works, start to finish

A room is where one revenue problem is diagnosed, decided, and closed. Below is a complete example from the workspace used across this site: $412,000 exposed on a Tuesday, $312,000 preserved nine days later.

Opened by an agent$412,000 at risk1 objection, left openClosed in 9 days
1 · It opens itself

The room opens on its own

At 03:40 the agent finished its nightly pass over order data. Customers acquired after 4 March were reordering at 27%, against 38% for every prior cohort. It confirmed with the Data Quality agent that no pipeline had broken, put the ninety-day exposure at $412,000, and opened a room.

Every room arrives with a title a person can read, the population affected, the money at stake, and a confidence score.

2 · Evidence, weighed

Four findings, each with a visible weight

Flolyt files each finding at one of four weights, so you can separate a measured fact from an unconfirmed signal before committing engineering time to it.

  • 01

    Measured

    Observed directly in your own data. The 4 March release moved the delivery fee below the cart.

  • 02

    Corroborated

    Two independent sources agree. Support tickets and delivery logs both point at late first orders.

  • 03

    Indicative

    Consistent with the pattern, not proven by it. The lapsed first-order discount reads as a price rise.

  • 04

    Unverified

    External or unconfirmed. A competitor changed its delivery threshold in March. Noted, and excluded from the estimate.

3 · People and agents

People and agents, always shown separately

People appear as solid avatars and agents as dashed outlines, in two separate stacks, so you always know whether a statement came from a colleague or a model.

In the room

  • Dana O. · Product

    Owns the checkout fix, approved it.

  • Amara N. · Support

    Brought the complaint pattern that became evidence.

  • Revan S. · Finance

    Objected to the discount cost, on the record.

  • Tunde K. · Engineering

    Shipped it on day seven.

Running

  • Repeat & Decay

    Opened the room and holds the thesis.

  • Data Quality

    Confirmed the anomaly was real, not a broken pipe.

  • Experiment Designer

    Built the holdout and monitored significance.

4 · The decision

One decision, one approver, one recorded objection

Agents drafted two actions. Dana approved the checkout change. Revan objected to holding the discount on margin grounds. The objection stayed visible after approval and was attached to the outcome when it landed.

Show delivery fees before the cart, and hold the first-order discount through order two.
Approved · Dana O. · 11:04Objection open · Revan S.
5 · Tested, not assumed

48,000 customers, a 10% holdout, a measured result

Before the reactivation wave went out, the Experiment Designer split the audience between WhatsApp and push and held back 10%. Significance was monitored, not declared.

  • Day 0 · agent
    Room opened. $412,000 exposure over 90 days.
  • Day 1 · human
    Support confirms the complaint pattern. Finance objects to the discount cost.
  • Day 3 · human
    Checkout fix approved and assigned. Ticket syncs to Linear with the room attached.
  • Day 4 · agent
    A/B test live with a 10% holdout.
  • Day 7 · shipped
    Fee shown before the cart. Second-order rate begins to recover.
  • Day 9 · closed
    $312,000 preserved, measured against the holdout. Finance's objection recorded as partly upheld.
6 · Governance and security

Autonomy you granted, in writing

Every agent capability sits in one of three states, set per workspace and logged when changed. In this room, agents could open the room and draft the campaign unaided. Sending it and changing the discount required a person. Writing back to the CRM was blocked.

Automatic · analyse, open rooms, draftProposed · message customers, change pricesBlocked · write to CRM, delete a source

SSO via SAML or OIDC · role-based access at data source, room and feature level · full audit log of every human and agent action · data residency options · GDPR and CCPA tooling · 99.9% uptime SLA on Enterprise.

7 · Closed, and remembered

Closed rooms make the next one faster

When the leak stops, the room closes and its full record is written to Business Memory: evidence, disagreement, approval and measured outcome. The next time second-order rates bend, the agent opens with what worked and what did not, and a new hire can read why the checkout looks the way it does.

A dashboard is worth the same on day 700 as on day one. A record of every revenue decision your company has made is worth more with each one you add.

Start protecting the revenue you already earn

Connect one source, let the agents run overnight, and read your first room in the morning. The free workspace never expires and never asks for a card.

Let’s get started

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