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Intempt

Event streaming for data teams

Stream every event to your own Kafka and S3.

Give every team real-time customer data by streaming scored events to your own Kafka and S3, with no warehouse in between.

Start for free
  • Unlimited destinations
  • Consent checked
  • Declared topics
track('Plan Upgraded')Data Engineer agent· adding scores
Maya Chenrec_9f21 · usr_48213
Scores inside the message
RFM
Champion
Upgrade likelihood
82%
Next best product
Journeys

Consent checkedmarketing: granted

Apache Kafka

billing.upgrades.v1

Delivered · partition 7

Amazon S3

s3://meridian-lake

Written · part-0418

7+ years profitable, and counting

on G2
4.5on G2
events
10B+events
profiles
500M+profiles
enterprises
50+enterprises

It takes four tools to get one scored event to Kafka.

  1. Segment
  2. Warehouse
  3. Reverse ETL
  4. Kafka Connect

One record in your topic

You shouldn’t be the glue between four tools.

Today one scored event goes from Segment to a warehouse, through a reverse ETL tool, and into a consumer you keep alive.

Start streaming free

The Data Engineer agent adds scores before the send

Data Engineer agent

Adding RFM, likelihood and next best product to each record.

  • Scores attached1,284
  • Consent checked1,284
  • Held, no consent38

Topic ruleBlocked

marketing.test.v2

Not a declared topic. The send failed and nothing was created on your cluster.

Declare topic4 declared

Outgoing recordsLive

  • Maya ChenPlan Upgraded · Champion, 82% likelySent
  • Arjun PatelViewed Pricing · 71% upgrade intentSent
  • Lina OrtizSession Started · At risk, 64% churnWritten
  • Tom BeckerSigned Up · New customerSent
  • Sofia RossiOpened Email · no marketing consentHeld
  • Northwind teamSeats Added · Expansion, +4 seatsSent
  • Priya NairScoring

Three jobs

One event streaming platform does three jobs.

Send live events to Kafka, keep a full copy in your own S3 bucket, and check every record before it leaves Intempt.

01 · Kafka

Stream events to the Kafka you already run

Each record goes to the Kafka topic you choose, in JSON or Avro, with the customer’s scores already inside the message.

  • It works with the brokers you already run.
  • It sends JSON or Avro with the schema inline.
  • Ordered rules send each record to a topic.
  • The partition key defaults to the record id.
intempt.com/workflows/stream-events
Publish to KafkaWorkflow node · cluster prod-mskConnected
Brokers
b-1.prod-msk:9098+2
FormatJSONAvroSchema inline

Routing rules · first match wins4 rules

  1. 1event = Plan Upgradedbilling.upgrades.v1
  2. 2rfm = Championcrm.champions.v1
  3. 3likelihood > 0.7sales.intent.v1
  4. ·Anything unmatchedevents.scored.v1default
Partition keyrecord_iddefaultChange

What changes

Six things you stop building yourself.

  • billing.upgrades.v1Avro

    The score rides on the record

    Your consumers get each event and the customer’s scores together, so no one has to join the two back up later.

  • 2Hops in between

    Kafka+S3

    No warehouse in the middle

    Events go straight to Kafka and S3, so you skip the warehouse round-trip and the reverse ETL bill that comes with it.

  • JSONAvroSchema inline

    { "schema": { "name": "PlanUpgraded" },
  "payload": { "event": "Plan Upgraded",
    "user_id": "usr_48213" } }
    billing-svcReads Avro

    The format your apps already read

    Pick JSON or Avro with the schema inline, so your consumers can read every message without writing any new code.

  • Cluster · 2 topicsAuto-create off

    events.scored.v1declared

    crm.champions.v1declared

    Nothing new on your cluster

    You declare your topics up front, and a send to any other topic fails instead of quietly creating a brand new one.

  • Kafka node · ordered rules

    1Plan Upgradedbilling.upgrades

    2RFM = Championcrm.champions

    ·Unmatchedevents.default

    Routing in a single step

    Ordered rules on one workflow node send each record to the right topic, and anything unmatched goes to a default.

  • Consent gate

    • Maya ChenConsent ✓Sent
    • Sofia RossiSaid noHeld
    • Arjun PatelConsent ✓Sent

    Privacy built into the pipe

    Consent is checked before every send, so people who said no never reach your Kafka topics or your S3 bucket at all.

The difference

Here is how event streaming should work.

Before and After
BeforeAfter
You send raw events to a warehouse and wait for someone to add the scores.Each event leaves Intempt with the customer’s scores already attached to it.
Your streaming tool only sends JSON, so your Avro consumers need extra code.You pick JSON or Avro, and the schema travels inline with every message.
You pay for a reverse ETL tool just to send warehouse data back out again.Records go straight to Kafka and S3, so there is no reverse ETL step at all.
You write and babysit a consumer or a Kafka Connect sink to move the data.One workflow node with ordered rules sends every record to the right topic.
A tool can write to topics you never approved, and you find out too late.Only declared topics get data, and a send anywhere else fails on purpose.

The offer

What streaming costs the old way.

These are the bills a small team pays when the CDP, the reverse ETL tool and the engineer who joins them are bought apart.

The old way

$91,600/yr

Year one, before warehouse compute.

Intempt

$0

free plan

  • Identity build3$3,000
  • Data engineer time3$1,500/mo
  • Reverse ETL licence2$15,000/yr
  • CDP licence1$55,600/yr

The old way

$0 · free plan

Intempt

Free

No destination caps

The pipeline you were about to build yourself

  • Stream to your own Kafka and S3.
  • Send JSON or Avro with the schema inline.
  • Connect unlimited sources and destinations.
  • Get 10,000 streaming deliveries a month.

AI credits included

The data engineer you were about to hire

  • Data Engineer agent

    Enforces your topic rules on each send.

  • AI Attributes

    Adds scores to every customer profile.

  • Workflows

    Routes each record with simple rules.

  • Consent

    Checks privacy before each record goes.

  1. 1Vendr marketplace, retrieved 2026-08-15. Twilio Segment median annual contract $55,600 across 613 purchases.
  2. 2Vendr marketplace, retrieved 2026-09-29. Hightouch median annual contract $15,000 across 149 purchases.
  3. 3Fiverr, Upwork, Clutch. Freelance data-engineering retainers $1,000-2,500/mo; identity-resolution builds 20-40 hrs at $100-250/hr.

Control

Your cluster stays under your rules.

Anyone on your team can set up a stream, but nobody can invent a topic or send data somewhere you never approved.

topics
4 declared
auto_create_topics
false
partition_key
record_id
secrets
write-only

  • Declare it or it fails

    You list your topics up front, and a send to any other topic fails on purpose instead of creating something new.

  • The partition key is yours

    The key defaults to the record id, and you can change it so each customer’s records land in the order you expect.

  • Secrets stay secret

    Passwords and keys become write-only after you save them, so no one can ever read them back from the screen again.

  • Consent before every send

    Each record is checked against the customer’s consent before it leaves Intempt for your Kafka topic or S3 bucket.

Integrations

Wired into the tools your team already uses.

Collect events from your site, app and server, then send them to Kafka, S3, webhooks and your CRM with no caps.

Collect · from

  • Your siteJavaScript SDK
  • Your appiOS, Android and React Native SDKs
  • Your serverNode.js, Python and PHP SDKs

Intempt

  • Profile
  • Scores
  • Consent

Send · to

  • Apache KafkaYour topics · JSON or AvroLive
  • Amazon S3Your bucket · your regionCopy
  • WebhooksAny HTTPS endpointSame canvas
  • Your CRMContact and deal writesSame canvas
  • No caps on sources
  • No caps on destinations
  • Consent checked on every send
See all integrations

Go deeper

Two more reasons data teams pick Intempt.

One page shows the scores that ride on every record, and the other compares event streaming on Segment and Intempt.

  • 9:41

    Audience AI

    Who to contact next

    • Maya ChenTeam planChampion
    • Jonah ReyesOnboarding callAt risk
    • Priya LalAnalytics add-on74%

    Job 01

    Audience AI

    RFM, likelihood and next best product scores are computed on every profile, so each streamed event knows its value.

    See how Audience AI scores

  • Segmentraw event

    { "event": "Plan Upgraded", "user_id": "usr_48213" }

    JSONNo scoresWarehouse next

    IntemptKafka · S3

    { "event": "Plan Upgraded", "scores": { "rfm": "champion" } }

    AvroDeclared topicScored record

    Job 02

    Segment alternative

    See why teams leaving Segment get Avro, declared topics and scored records on Kafka and S3 with no warehouse in between.

    Compare Intempt and Segment

The products underneath

Teams love it

They stopped stitching tools and hired one system.

StockInvest

Acquisition and retention are one connected motion. Personalizing the path before signup only pays off when the same system carries the user through onboarding afterward.

Anonymous traffic was not turning into registered users, and acquisition and retention were run as two separate problems. We ran Experiences on the anonymous visitor flow to put the right content and CTAs in front of each visitor, then handed new signups to automated Journeys for onboarding and deeper engagement.

455,396

users instrumented

Read the full research
FieldsUSA

Judge experiments on revenue per visitor instead of conversion rate. A variant can win on conversions and still lose on revenue, and repeat buying only shows up when the whole journey is in one view.

High-intent shoppers browsed a large, specialized outdoor catalog, and browsing, product interest and purchase history sat in different places. We unified them into one customer view, then ran real-time personalization, intent-based segmentation and timely follow-ups on top of it, judging experiments on revenue per visitor.

Read the full research
Hoperfy

Build the pipeline on observed signals. When follow-ups trigger on intent, the team spends its time on the users who are genuinely engaging.

Static lead lists and timeline-based follow-ups were hiding who was actually engaging. We ran a signal-led outbound pipeline where follow-ups fire on real behavior: outbound replies, onboarding progress and product usage.

Read the full research
rating on G2
4.5
events processed
10B+
user profiles
500M+
profitable
7+ yrs
enterprise customers
50+

Frequently asked questions

Straight answers about event streaming, Kafka, S3 and how Intempt fits into the data stack you already run.

Event streaming is sending each event, like a signup or a purchase, to other systems the moment it happens instead of in a nightly batch. For customer data, it means your apps and your data lake see what customers do right away.

Ready when you are

Scored events, straight intoyour own Kafka.

Connect your sources, declare your topics, and every record reaches Kafka and S3 with its scores attached and consent checked.

Connect your AI to your customer data

$ claude mcp add intempt -- npx -y @intempt-technologies/mcp