GTM experimentation engine
Stop guessing. Start experimenting. Scale what works.
We build AI-powered go-to-market engines that discover your best buyers, test messaging at scale, learn from every conversation, and turn market intelligence into qualified pipeline.
- Market
- Segment
- Messaging
- Experiment
- Decision
- Pipeline
Manufacturing
Priority 74 · 0/12
Healthcare
Priority 55 · 0/12
Financial Services
Priority 78 · 0/12
SaaS
Priority 90 · 0/12
Choosing the next segment in SaaS.
Every number in this engine is simulated from a seeded model to show how decisions get made. It is not client data and not a prediction of your results. Pause it, or pick a vertical, ICP, seniority or angle to explore.
The problem
Most GTM teams run one test a month and call it strategy
The bottleneck is rarely effort. It is that the system cannot learn fast enough to tell good markets from bad ones.
Research is manual
Reps build lists by hand from a database filter, so the market a team works is whatever one person had time to pull.
Systems do not talk
The data tool, the sequencer and the CRM each hold part of the answer. Nobody can say which segment produced last quarter's pipeline.
One message, judged on feel
Most teams run one angle at a time and call it after a week. A 2% reply rate on 150 sends is noise, not a result.
Coverage is slow
A team that tests one segment a month needs four years to learn which of its 48 buyer segments is worth the budget.
Learning stops at the reply
Replies get counted, but meetings, opportunities and objections never flow back into who gets targeted next.
How the engine works
From market map to qualified pipeline, on a loop
The six stages in the engine above, in plain terms. Each cycle ends by choosing the next test from what the last one proved.
01
Market discovery
Score every vertical you could sell into on reachable accounts, contract value and fit. Decide where to test first.
02
ICP segmentation
Split each vertical into ICPs and seniority levels. Four verticals, four ICPs and three levels is 48 segments, each tracked on its own.
03
Message generation
Write one variant per messaging angle for the chosen segment and split the audience at random so no variant gets the easier half.
04
Controlled experiment
Send, track delivery, replies, positive replies, meetings and opportunities per variant. Nothing is judged before 400 delivered.
05
Decision
Promote a winner only at 95% confidence. Extend tests that are close. Check deliverability before blaming copy. Write a new hypothesis for anything that loses.
06
Pipeline and repeat
Record the result against the segment, re-score the market with real conversion data, and queue the next experiment.
GTM experimentation
Decision rules, written down before the first send
Testing only works if the rules for calling a winner are fixed in advance. These are the ones the engine runs on.
- 400 delivered
- Minimum per variant before any variant is judged.
- z ≥ 1.96
- A winner is promoted only at 95% confidence. Anything less is reported as not yet significant.
- One extension
- Close tests get 50% more volume on the top two variants. Still close after that, both stay in rotation.
- List before copy
- A variant with bounce above 4% is a list problem. The same copy reruns on a re-verified list.
- Held-out follow-ups
- A losing angle gets one new hypothesis, sent to contacts the first test never touched.
Revenue intelligence
The same model, pointed at the accounts you already have
Experiments find new segments. Revenue intelligence works the CRM: who to call, who to expand, who is about to leave.
- CRM enrichment
- Fill missing firmographics, titles and buying-committee contacts on the accounts already in your CRM.
- Account scoring
- Rank every account on fit, timing signals and potential contract value, rebuilt as conversion data comes in.
- Whitespace analysis
- Find the segments and accounts that match your best customers and that no rep is working.
- Expansion signals
- Watch existing customers for hiring, funding, new locations and leadership changes that open a larger deal.
- Customer health
- Combine usage, support and engagement data into an early read on churn risk, routed to the owner.
- Conversation intelligence
- Classify replies and call notes by objection and intent so messaging changes follow what buyers actually say.
A visualization of how D3 scores a market. Companies are invented; the scoring structure is real.
Meridian Freight Systems
Tier 1Logistics SaaS · 620 employees · Chicago, IL
- ICP fit
- 28/30
- Opportunity signals
- 26/30
- Timing
- 17/20
- Potential ACV
- 13/15
- Data quality
- 5/5
- Hiring 6 SDRs
- New VP Sales in March
- Salesforce + Outreach
Cobalt Benefits Cloud
Tier 1HR tech · 240 employees · Austin, TX
- ICP fit
- 25/30
- Opportunity signals
- 22/30
- Timing
- 16/20
- Potential ACV
- 13/15
- Data quality
- 5/5
- Series B in Q2
- Paid ads ramping
- Expanding mid-market team
Harborlight Analytics
Tier 2BI consultancy · 85 employees · Boston, MA
- ICP fit
- 21/30
- Opportunity signals
- 15/30
- Timing
- 11/20
- Potential ACV
- 12/15
- Data quality
- 4/5
- Steady headcount
- No timing trigger yet
Stateline Industrial Supply
Tier 3Distribution · 1,100 employees · Toledo, OH
- ICP fit
- 12/30
- Opportunity signals
- 8/30
- Timing
- 6/20
- Potential ACV
- 9/15
- Data quality
- 3/5
- Weak fit
- Low signal activity
Custom AI engineering
Built in your stack, owned by you
Deterministic code first, AI where judgment is needed. Everything runs in accounts you own, so it keeps working if we leave.
Integrations
Sequencer, CRM, data vendors and calendars connected so events flow without exports.
Automation workflows
List builds, enrichment, reply routing and CRM updates that run on a schedule, not on someone's memory.
AI agents
Used where judgment is needed: classifying replies, reading websites, drafting variants. Code does everything a rule can do.
Data pipelines
One account and contact model across sources, deduplicated, with every record traceable to where it came from.
Internal tools
Small apps for the work your team repeats: territory views, research briefs, approval queues.
Reporting
Pipeline attribution by segment, angle and source, in your BI tool or a dashboard we build.
Case studies
Results, with the method attached
No case studies published yet.
We publish results only with client permission and with the real numbers attached: segment, volume, reply rates, meetings and opportunities. Ask on a call and we will walk through current engagements directly.
Your 2027 GTM plan
What a year of testing looks like at your volume
Pick a starting vertical and a sending volume. The roadmap shows how fast the engine can give every segment a tested answer.
- Experiments per quarter
- 11
- Quarters to test every segment
- 3
Assumes four message angles at 650 contacts each per experiment. Your real plan depends on your list size, sending capacity and sales cycle, which the strategy call works out.
Q1 2027
Map and first tests
Build the SaaS account map (24,200 accounts in this demo), score it, and run 11 segment experiments. Deliverability infrastructure warms in month one.
Q2 2027
Scale what cleared the bar
Winning angle and segment pairs move to full volume. 11 more segments get tested, starting Manufacturing. Underperformers get one follow-up hypothesis, then retire.
Q3 2027
Close the loop with CRM data
Meetings and opportunities from the CRM re-weight segment scores, so the next tests chase pipeline, not reply rate alone.
Q4 2027
Plan 2028 from evidence
By now 24 of 24 segments have a tested answer. Budget for 2028 follows the segments that produced qualified opportunities.
See what an experimentation engine would look like for your company.
Bring your market and your current numbers. We will map your segments, show where we would test first, and tell you whether the volume is there to learn anything.