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: search, enrich, verify, score and push in one table, AI columns for the copy, fast enough to keep up with your roadmap.
Pay on match: failed lookups cost zero · AI columns for segmentation · 32-integration enrichment · Schedulable experiments · BYOK for AI cost control
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 a data layer that runs the whole chain itself — search, enrich, verify, score, push — on a schedule, with AI columns doing the copy, so pulling data stops being the experiment's bottleneck.
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 run themselves
Put search, enrich, verify and score on a schedule so a new audience rebuilds itself overnight. Winners push straight into Smartlead, Instantly, Lemlist, HeyReach, or Apollo Sequences, and out to your ad-platform API from there.
A typical growth experiment
1. Hypothesis
"Mid-market healthcare CFOs who recently joined will respond to a peer-benchmarking message about budget cycles."
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. Personalization (10 minutes)
AI columns draft openers and subject lines per row. BYOK on Anthropic or OpenAI keeps costs predictable.
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. 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.
Run it on your motion.
The 14-day trial ships every feature.
Five minutes, no card