Time to 1,000 large enterprises
AI-native GTM compressed the time to 1,000 large enterprises
Large enterprise adoption
Claude-connected tools are entering GTM faster than any previous wave of sales tooling
Agentic workflows and Claude-connected tools are entering GTM faster than any previous wave of sales tooling. The question isn’t whether AI becomes central to how revenue teams operate — it already has. The question is:
Will Claude actually solve technology companies’ declining sales and marketing efficiency — or accelerate the decline?
“Claude is helping my sales team accelerate their outreach to prospects, but we’ve only seen a modest lift in initial calls and no real change in pipeline. I’m starting to wonder if we’re actually hurting our credibility given how often we’re reaching out.”
CRO, Leading CI/CD Company
This CRO isn’t describing a Claude problem. He’s describing an intelligence problem. Claude is doing exactly what it was asked to do — faster outreach, at scale. The issue is what it’s running on.
Bottom line: Increased activity without greatly improved intelligence doesn’t produce better results. It produces an exponential increase in noise.
We came at this from the outside.
For two decades our team had one job: research technology companies, deeply and rigorously, for the world’s largest tech investors — Fidelity, JPMorgan, T. Rowe Price. Our work didn’t get graded on activity. It got graded on whether the thesis held up. In that world, a single signal almost never drives a decision. You triangulate across sources, then you call the play.
We assumed the GTM intelligence market would work the same way. It doesn’t — and we were surprised by how thin the intelligence actually is.
The prevailing model is signal-based, and the prevailing instruction is spray. Vendors detect a behavioral signal — a website visit, a keyword search, a job posting — and hand the rep a list. That isn’t intelligence. It’s spray and pray with a slightly smarter trigger. One pixel handed to a rep as if it’s the whole picture.
Worse, existing tools are backward-looking. They tell you what’s already happening inside an enterprise after the window has started to open. What matters is what’s likely next.
What we built instead: Conviction Intelligence.
ATC — Ahead of The Curve — takes in signals from many carefully curated sources, triangulates them the way a research desk triangulates a thesis, and narrows it down to: this account, this problem, this person who owns it, this conversation worth having today. Broad in, narrow out.
We also built in anticipation. ATC projects the problems an account is beginning to have — the ones they haven’t broadcast yet — so reps arrive before the window opens wide, not after it closes.
Conviction Intelligence is accessible through the ATC Analyst Platform and directly via MCP, integrating natively with Claude and the agentic GTM workflows your team is already building.
Claude helps you go 10x faster. ATC lets you 10x your results.
Same prompt. Same model. ATC adds the buying center, active initiative, stack, and motion that make outreach worth sending.
Claude without ATC
Researching public sources...
- Researching Wells Fargo on the webLooking for broad data, cloud, analytics, and financial-services signals.
- Reading Wells Fargo press releasesFinding modernization themes.
- Inferring fitLarge bank plus governance needs suggests a possible Snowflake fit.
- Drafting outreachFocusing on data modernization
Wells Fargo recently highlighted major digital banking milestones, including growth in mobile usage and AI-powered customer interactions. That creates a plausible need for governed analytics, customer-data infrastructure, and scalable AI/reporting workflows. However, public sources alone do not identify a specific buying center, active data-platform project, or technical pain.
Hi {first_name},
Congrats on Wells Fargo’s recent digital banking milestones, especially the scale of Fargo and mobile engagement.
As digital and AI usage grows, many financial services teams look for more governed ways to unify customer, product, and interaction data across systems.
Snowflake helps banking teams consolidate data, improve governance, and support analytics and AI workloads at scale.
Worth a brief conversation next week?
Claude with ATC
Building ATC account plan for Snowflake...
- Scoring account fitStack, hiring, and relationship data point to a real Snowflake wedge.
- Mapping the buying centerTransaction Risk Strategy prioritized over generic enterprise data.
- Resolving the right contactNamed owner found for model governance and false-positive reduction. Name redacted.
- Finding active painGovernance modernization and model-refresh pressure surfaced as the trigger.
- Writing the motionGoverned risk telemetry layer without a full SAS or BI rip-and-replace.
ATC found a specific buying path: Transaction Risk Management is scaling risk decisioning across ATM, mobile, and branch channels while legacy analytics infrastructure creates concurrency, refresh-cycle, and false-positive pressure. Start with the Head of Risk Technology / CRO org, where Snowflake’s elastic compute can separate ingestion, model refreshes, and downstream analytics.
Hi [name redacted],
As Wells Fargo’s Transaction Risk Management org scales risk decisioning across ATM, mobile, and branch channels, it may face challenges managing risk without adding latency or review-queue noise.
The common bottleneck we see is concurrency. Risk teams need fresher model outputs, but ingestion, scoring, BI, and downstream analytics often compete for the same warehouse capacity. Governance issues can also arise while parts of the workflow still depend on SAS, Tableau extracts, and batch feature refreshes.
Snowflake can give risk-data teams isolated, elastic compute for high-volume scoring and analytics, so model refreshes do not slow ingestion or downstream reporting. Worth comparing notes on how Tier-1 banks are reducing false positives while keeping transaction-risk governance intact?
Claude is only as good as what you’re feeding it.
Without ATC, Claude runs on thin signals — producing generic but confident, articulate, fast outreach aimed at the wrong account, the wrong problem, or the wrong person. Volume goes up. Results don’t.
With ATC, Claude runs on Conviction Intelligence. It reasons about timing and positioning, drafts outreach a senior rep would actually send, and scales without the quality collapse that kills most AI-driven GTM programs. The intelligence layer is the constraint. It always has been.
What customers are telling us.
“We’re identifying accounts with real-time problems we can actually solve — not just behavioral signals that are a month old.”
“We can target the specific individuals who own the problem and create messaging that actually resonates.”
“We’re running Claude on a fraction of the accounts we used to chase — and getting better results from every prompt.”
Early clients have shown us numbers we wouldn’t have predicted: 10x reply rates against an industry that has come to accept low response as the norm. C-level engagement at major financial services and consumer brands. Real meetings, real pipeline — from the same reps, in the same hours, with better intelligence underneath. Not from sending more. From knowing more, and knowing it earlier.