Data
Highly curated, integrated signals across developer + enterprise activity
Usage, experiments, discussions
Products, Initiatives, Org Structure
How ATC works
From scattered signals to decision-grade GTM intelligence.
ATC connects developer activity, enterprise adoption, product ecosystems, people, and market movement into a single intelligence engine. The output is not another list of signals. It is a clear account thesis, the right use case, and the next move your team should make.
The engine
How data becomes conviction
The ATC system moves through three layers: curated data, a modeling framework, and decision-grade outputs. Each layer narrows noisy market activity into the accounts, people, problems, and timing that matter.
System map
ATC's Intelligence Engine
Modeling framework
Contextualized to client's ecosystem
Foundational categorization
- 10,000 products + 300+ initiatives indexed
- 50M+ product interrelationships mapped
- 10+ years historical adoption + sentiment
- 4M+ business units aggregated
Core analytics applications
Product Ecosystem
Product Momentum
Relevant People
Purchase Prediction
Win/Loss Analysis
Business Problem Identification
Use cases
Decision-grade outputs delivered through ATC apps today and API-ready outputs over time.
- Account Prioritization
- Campaign Development
- SDR Outreach
- Scaling ABM Efforts
- Market Analysis
- Ecosystem Analysis
The process
Conviction Intelligence Process
ATC follows the same research discipline across account prioritization, campaign planning, SDR workflows, ABM, and market analysis.
Curate the signal base
ATC starts with highly curated developer and enterprise activity: open-source participation, product adoption, hiring signals, employee expertise, and developer practices.
Map the ecosystem
The engine categorizes products, initiatives, relationships, sentiment, historical adoption, business units, and the people tied to each motion.
Model what matters now
Core applications analyze product ecosystems, momentum, relevant people, purchase likelihood, win/loss patterns, and business problem emergence.
Contextualize the output
ATC translates those signals into the client's ecosystem, so teams receive account-specific guidance instead of generic market observations.
Why it matters
The same intelligence layer powers every GTM motion.
Because ATC grounds outputs in the client's ecosystem, the same engine can tell a rep who to call, a marketer what campaign to build, an ABM team where to focus, and a strategy team how the market is shifting.
Know which accounts deserve attention first.
Build campaigns around real account problems.
Give SDRs precise outreach angles and buying-center context.
Scale ABM and market analysis on the same intelligence layer.
Put it to work
See what ATC knows about your next account.
Start with an Account Attack Plan, or add ATC to Claude and ChatGPT to bring the intelligence engine into the workflows your team already uses.