Can marketing break my schema?

No. They build audiences from conditions that already exist. You decide which sources are connected, what the events are called, and where anything can publish. What they get is the answer, not the keys.

Isn't self-serve analytics a myth?

Mostly, when self-serve means handing someone a SQL editor and hoping. It works when the surface is narrow enough that there is nothing to get wrong. Picking conditions on events you already defined is that. Writing a query against your warehouse is not.

Do we have to move off our warehouse?

No. Keep it. We write Parquet into a bucket you own and your warehouse reads it exactly as it does today. The difference is that the profile is already built before anything lands there.

Can it publish somewhere we did not approve?

No. A workflow can only publish to a destination you declared. Point it at a topic that is not on the list and it fails rather than creating one.

What does it cost at our volume?

You pay for volume rather than per connection or per monthly tracked user. Pricing is published, so you can work it out without booking a call.

Data

Let teams use customer datawithout waiting on your queue

Marketing builds audiences from conditions instead of filing tickets, while you keep the schema and the sources.

Free to startUnlimited sources and destinations

0.0on G2

10B+events

500M+users

7+years profitable

50+enterprises

The category: Data

A pipeline vendor hands you clean events and stops. Then you build the CRM yourself, or buy a third one and wire it back to the events you already paid to collect. Segment forwards data. It does not give you the accounts, contacts, deals and lifecycle that the data is for.

Collect events from every source, resolve them into one profile per person, and act on them in a CRM that is already deployed. Accounts, contacts, deals, tasks, meetings, scheduling and catalogs, with every change available as an event. No handoff between a pipeline vendor and the CRM you'd still have to buy - the Data Engineer agent runs both ends of that pipe.

Let marketing build audiences on its own.

A condition builder instead of a query, so the request never reaches you.

Filter on any event, attribute or existing segment
Let the segment keep resolving as people change
Take the promotion list out of your ticket queue

Let marketing build audiences on its own

Move faster without losing control.

Self-serve fails when it means anyone can invent a field. It does not mean that here.

Own which sources exist and what the events are called
Decide where anything is allowed to publish
Let them build audiences without touching the schema

Give every team the same customer context.

One customer record for marketing, sales and analytics, so nobody keeps a private copy.

Serve three teams from one profile
End the argument about whose number is right
Remove the export and the drift between systems

Give every team the same customer context

Keep customer data where it belongs.

Your Kafka cluster, your bucket, your topics, declared up front.

Declare every topic, or the publish fails
Write Parquet into your own S3
Never have a topic created on your cluster

Use customer scores without the model work.

RFM, likelihood and next best product train on your own events and come back as ordinary attributes.

Set them up from the attributes page
Train on tracked events, score in the background
Let marketing filter on the score like any other field

Use customer scores without the model work

Use customer data before the warehouse is ready.

Events resolve to a person here, so no modelling project stands between collection and use.

Start without a warehouse or a dbt project
Send the raw data to your storage as well if you want it
Be useful in the first session rather than the second quarter

Use customer data before the warehouse is ready

The way it works today

Marketing files a ticket. You write the query. The backlog grows.

Pay for the pipeline, then pay again for anything that acts on it.

Warehouse round-trip before a marketer can use a segment.

Buy a pipeline, then buy a CRM, then wire them together.

Connector caps on the free tier throttle instrumentation.

The way it should work

They describe the audience. The segment resolves itself.

One profile store. Attributes, segments, CRM and activation on top of it.

Attributes computed and segments resolved in place.

One profile per person, and the CRM already runs on it.

Unlimited sources and destinations on every tier, including free.

The offer

Here's what this costs you the old way.

CDP licence at 100M events/mo¹$0/yr

Product analytics licence¹$0/yr

Data engineer time on audience tickets²$0/mo

Identity resolution build²$0

Warehouse egress for reverse sync$0/mo

Total value

→

↓

Your price

Unlimited sources, unlimited destinations, uncapped segments. Free to start.

Start collecting

No credit card required

Free forever

The pipeline your data team was going to build

Client, server and cross-platform SDKs

JavaScript, React Native, Swift, Android, Node, Python, PHP

Unlimited sources and destinations

No connector caps, on any tier

One schema for every event and object

Taxonomy and identity resolution included

30-day retention on Free

Unlimited on every paid tier

75 credits on us

The engineer you were about to hire

AI Segmentation

Describe the audience. Skip the query.

AI Attributes

Predictive traits computed for you

Event sync to your own Kafka and S3

Topic routing, partition keys, JSON or Avro

Real-time and batch delivery

Two meters. Pay for latency only where you need it.

Three jobs that used to land in your queue.

The Data Engineer runs them. You keep the schema.

Every source, one schema.

You define the event names once. Every SDK writes into them.

Client, server and cross-platform SDKs

JavaScript, React Native, Swift, Android, Node, Python, PHP

Identity resolution joins them to a person

As the events arrive, not in a nightly job

Unlimited sources and destinations

No connector caps, on any tier

Blu

Add our iOS app as a source, on the event names we already use on web.

Go deeper

The products underneath these jobs. Every one of them reads the same resolved profile, so nothing here needs a second integration to see what the first one collected.

Data Hub

The collection layer underneath both jobs. One event stream, one profile, every downstream product reading the same records.

Workflows

What Event Sync runs on. Topic routing, partition keys and your choice of JSON or Avro, from the same canvas as webhooks and CRM writes.

AI Attributes

What Audience AI runs on. RFM, Likelihood and Next Best Product computed on every profile, with no model to build or host.

Two reasons a founder buys this.

Each one compares what it takes on Segment with what it takes here, and who ends up doing it.

Events streamed to a Kafka topic, with delivery and replay on failure

Wired into the toolsyour team already opens.

Slack, Stripe, Twilio, SendGrid, Gmail, Google Calendar, Firebase, Apache Kafka, AWS. Blu Agent operates them for you without a browser tab.

See the full list

In the words of50+ live tenants.

“We were losing visitors before they signed up. Intempt's personalized experiences changed that - we started meeting people where they were instead of guessing. Once they're in, Intempt's automated email takes over and keeps the relationship moving. Acquisition and retention finally feel like one connected motion instead of two separate problems.”

Jim Stromberg

CEO, StockInvest

“Intempt helped us turn real browsing and purchase signals into personalized experiences that drive repeat buying. We finally have one system that sees the whole customer journey.”

Eric Gardner

COO, FieldsUSA

“With Intempt, we built a signal-led pipeline driven by real behaviors. Follow-ups are triggered by intent signals instead of timelines, so we only focus on users who are truly engaging.”

Tadas Kertenis

Co-founder, Hoperfy

Frequently askedquestions.

What a founder running the whole stack alone tends to ask before connecting a source.

01 / 06

We run HubSpot free, Mailchimp, Calendly and GA4. What actually changes?

Those four each hold a piece of the same person and none of them knows about the others, so any question crossing two of them becomes a manual join, usually yours. Here they write to one profile, so the question is a filter rather than a project.

Those four each hold a piece of the same person and none of them knows about the others, so any question crossing two of them becomes a manual join, usually yours. Here they write to one profile, so the question is a filter rather than a project.

Sources

1.Vendr marketplace, retrieved 2026-08-15. Twilio Segment median annual contract $55,600 across 613 purchases; Amplitude median $64,724 across 416.
2.Fiverr, Upwork, Clutch. Freelance data-engineering retainers $1,000-2,500/mo; identity-resolution builds 20-40 hrs at $100-250/hr.

Intempt Data

The pipeline and the CRM it feeds.

One profile per person, real-time or batch delivery, and accounts, contacts, deals and lifecycle already live. Free to start.

Start collecting