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For growth marketers

Growth runs on experiments. Experiments run on data. We're the data primitive.

Growth marketers ship five experiments a week — new audiences, new lookalikes, new ICPs, new outbound angles. Each one needs enriched data, segmented lists, and personalization at row level. TexAu is the engine those experiments run on: API-first when you want to script it, AI-column-rich when you want to draft it, fast enough to keep up with your roadmap.

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API + MCP-native · AI columns for segmentation · 32-integration enrichment · Schedulable experiments · BYOK for AI cost control

The problem

Your experiment velocity is capped by your data velocity.

You wanted to test outbound to mid-market healthcare CFOs this week. You wanted to test a new lookalike audience for your paid ad spend. You wanted to test a personalized re-engagement sequence for cold leads. None of those experiments runs without enriched, segmented, scored data — and most growth marketers spend more time pulling data than running tests. The fix is making the data layer programmable, AI-column-driven, and fast enough that data work isn't the experiment's bottleneck.

The fit

What TexAu does for this team

  • Audience and segment construction

    Build new ICPs in minutes: filter by industry, headcount, technographic, hiring velocity, recent funding, executive moves. Iterate the filter as the experiment evolves.

  • Personalization at row level

    AI columns transform any data into per-row personalization: a one-line opener referencing their last funding round, a custom subject line based on their tech stack. BYOK for cost control.

  • Experiments that ship as code

    Every TexAu action is a REST endpoint. Wire experiments into your growth-engineering stack: trigger an enrichment job from your A/B testing framework, push winning audiences to your ad-platform API.

In motion

A typical growth experiment

  1. 01

    1. Hypothesis

    "Mid-market healthcare CFOs who recently joined will respond to a peer-benchmarking message about budget cycles."

  2. 02

    2. List construction (5 minutes)

    Co-Pilot proposes the table: Apollo search filters, enrichment chain, AI column for "tenure in current role." Edit, run.

  3. 03

    3. Personalization (10 minutes)

    AI columns draft openers and subject lines per row. BYOK on Anthropic or OpenAI keeps costs predictable.

  4. 04

    4. Push to sender (1 minute)

    Push to Smartlead with the experiment-tagged campaign. Per-row openers and subject lines as merge fields. Send rate respects warmup curve.

  5. 05

    5. Measure and learn

    Replies sync back to the table with reply class (positive / interested / out-of-office / nothing). Tag the experiment, run the next one.

Five minutes, no card

Run it on your motion.

Free tier ships every feature.

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