Built for High-ACV SalesBuilt for High-ACV, Relationship-Driven Sales
High-fit, high-intent accounts, at the right moment.With the right message - and a warm path in.
We encode your niche signals, company knowledge, and network map.
Then layer our agents on top.
Your team sells.
Most AI sales tools add noise — junk alerts, generic intent data, spray-and-pray sequences that burn a finite market. IntelligentNoise is the opposite: an intelligence layer that pulls the signal out of the noise — the account entering a buying window, the person who owns the decision, the warm path in — so your team spends its time selling, not sifting.
No new tab. No new habit. Reps can chat with IN, launch work, and approve actions from Slack, Teams, or Claude — with drafts sent from Gmail or Outlook and every update posted back to Salesforce or HubSpot. The agent comes to your reps, not the other way around.
Context is what makes an agent useful — and it compounds with every run. Every rep gets an IN agent that learns their voice and accounts. Every org's ICP, pricing rules, objection patterns, and playbooks live in a reviewable memory layer — proposed by the agents, approved by your team, applied by every agent on every run.
Approvals where they matter — shared config, sensitive sends, anything that touches another rep's pipeline. Every run is registered, reconcilable, and inspectable end-to-end. SSO and audit logs built in. We never train on your data.
Every revenue org runs differently. We turn your ICP, stakeholder maps, signal preferences, and playbooks into a versioned methodology your agents reason against, then pair bespoke deployment with white-glove partnership so the system gets sharper with real work.
Warm intro paths mapped into target accounts
7,000+
High-intent buying signals surfaced
1,500+
Days, on average, to a first meeting booked
<30
Hours saved per rep, every month
40+
The foundation behind every IN agent — context and memory that compound, and an architecture built for enterprise trust.
Every won deal, refined playbook, and corrected draft makes the next agent run smarter. Your team's ICP, objection answers, and won-deal patterns stack into a moat that grows with every run.
IN learns each rep's writing style, deal cadence, and account history — so every draft sounds like the rep who'll send it, not a template.
Memory and context are isolated per tenant — your accounts, playbooks, and conversations never pool with another org's data. We never train models on your tenant, and everything you put in is yours to export, anytime.
IN proposes the action — your team approves it. Every send, every record update, every memory change can be gated. No “oops, the AI sent that.”
Who ran it, what tools it touched, who approved, what changed. Every IN action is recorded end-to-end — ready whenever your security, IT, or compliance teams want a look.
Backed by industry-leading investors and advisors
