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Intempt

Your GTM.Hired.

Nine agents run the plays across data, design, marketing, sales, and analytics. You set the strategy. Nothing ships without your approval.

Free plan · No time limit

Data unify it

Every event, profile and deal lands on one customer record.

Data Engineer

Pick a lane. Blu runs it.

Five self-driving apps. The work of five hires.

One customer record. Each app runs itself and holds for your approval. Choose where to go deep.

G2
0.0on G2
10B+events
500M+users
7+years profitable
50+enterprises

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 every integration
An octopus holding a different cable in each arm.
JavaScript
Node JS
Apple
Stripe
Gmail
Google Calendar
Google Meet
Twilio
SendGrid
Slack
Amazon SES
Webhook
Apache Kafka
Amazon S3
Firebase Cloud Messaging

One record. Five teams' work.

The more truth-seeking a company is to its customers' real experience, the faster it grows. So Intempt is a context warehouse and five tools on one customer profile, run by Blu's agents: a self-driving GTM engine. You set the strategy, the agents run the plays, and nothing ships until you approve it.

The GTM context warehouse: every event, profile, message and deal on one customer record. Nothing to stitch, no sync to babysit.
See Data
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Prefer towire it yourself?

A brass diving helmet with a coiled air hose.
mcp
$ claude mcp add intempt -- npx -y @intempt-technologies/mcp
Explore MCP
cli
$ npm install -g @intempt-technologies/cli
Explore CLI

Teams love it

They stopped stitching tools andhired 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

From the lab

Our GTM research

Deep dives and playbooks from the team, written in the open.

All posts
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Founder-led GTM

How to run a one-person GTM team in 2026 with AI agents

The judgment half of five GTM jobs stays with you. Agents take the half with a checkable output.

Read the post
A message in a bottle bobbing on a curling wave.

Outbound

AI cold outreach: website data vs. scraped intent

Most marketers call intent data fundamental, few report exceptional ROI. The honest gap, and what actually moves reply rates.

Read the post
A deep-sea submarine with brass portholes and a spinning propeller.

Data

How to build a GTM brain (and why memory is not the hard part)

Five layers, in order: capture, retrieval, source truth, permissions, feedback. Most teams build the first and stop.

Read the post
A brass compass lying open on a sea chart beside a starfish.

Data engineering

AI for data engineering: the half of the job it can own today

AI works where the output checks itself: schema drift, identity, destinations. Sorted by the layer they touch.

Read the post

THE NEXT PLAY

One deep dive a week, in your inbox.

A new GTM playbook from the team. Nothing else.

Your advantage,anywhere you sell.

One subscription covers Mac, Windows, iOS, and Android. Notes, transcripts, journeys, and pipeline sync across every device the moment they land.

Desktop runs the live HUD over your video calls. Mobile records in-person meetings, triages the inbox between calls, and answers you on the road.

Get the desktop appGet the mobile app
A Golden Gate Bridge tower rising from curling waves.

Frequently askedquestions.

Three people, and you will know which one you are. The seller: "I'm tabbing between Fathom, Calendly, and HubSpot for every deal. My call recorder lives in one tool, my scheduling in another, my pipeline in a third. Nobody stitches it." The lifecycle marketer: "My emails live in Klaviyo. My product data lives in Mixpanel. I can't trace which email drove which product event." The founder still doing GTM: "I'm stitching HubSpot free plus Mailchimp plus Calendly plus GA4 with duct tape. No single view of ad click to signup to trial to paid." All three are the same problem at a different size, which is why one platform answers it.

You set the strategy. Agents run the plays.

Nine agents across data, design, marketing, sales and analytics, on one customer record. Free plan, no time limit, no credit card.

Nothing ships until you approve it.

Prefer a walkthrough?

A ship's steering wheel on its post, a coil of rope at its foot.

Intempt is an independent product. Not affiliated with Salesforce, HubSpot, Zoom, Google, or Microsoft. Product names are trademarks of their respective owners.