AI agents configured to understand your value and your playbooks — running every night across every deal and every customer, updating your CRM and keeping stakeholders informed. The data never leaves your AWS account.
AskNicely doubled win rates in one quarter →"We deployed two inbound agents in just three weeks, and the impact on our inbound workflows was immediate. If you want high ROI and rapid time-to-value across your revenue org, I highly recommend their agile, hands-on approach."
"The impact on win rates and time to close was immediate. I can't imagine leading a revenue team without this framework. It's not a tool — it's infrastructure."
"Most teams simply do not have the time to perform deep, objective account reviews at scale. By implementing an agentic strategy, we removed repetitive, unscalable work from our most skilled team members. We now have an always-on, autonomous layer that tells us exactly where to focus to deliver maximum value. AI should not replace judgment — it should sharpen it."
"Fast, no-hassle setup — and immediate impact on forecasting and deal pursuit."
"The agents deployed with zero effort and ran autonomously from day one. The ROI was undeniable — quota attainment up 45% while absorbing the capacity of three open headcount roles."
"We were drowning in human subjectivity. Our forecast and pipeline health were basically built on rep happy ears and gut feelings." — Alex Burkholder, VP Revenue, AskNicely
Deal inspection eats leadership bandwidth every week, and the number you call is still a guess dressed up as a methodology.
The CS team scores every account, but the score has zero correlation with who actually leaves at renewal.
Upsell opportunity is sitting inside your existing base right now — there's just no systematic way to find it before a competitor does.
Each node reads every transcript, email, note, and CRM field — every night — and writes an objective readout back to your CRM.
Reads every transcript, email, and note across every active deal. Scores deals against your methodology, surfaces risk without bias, and drives rigorous deal execution to prevent slipped quarters.
Replaces subjective health scores with an AI maturity model. Automatically flags churn risk before renewal and surfaces hidden expansion opportunities directly from actual customer interactions.
Instantly evaluates inbound intent, researches the account, and routes to the right rep with a synthesized brief. Turns hours of manual pre-call research into seconds of automated prep.
Most events are unambiguous and get routed instantly by static rules. Edge cases go to Claude Sonnet for judgment. Nothing ever leaves your AWS account.
Salesforce or HubSpot event lands in your VPC
LangGraph on ECS Fargate reads the event
Static for volume, Claude Sonnet for edge cases
ARR, GRR, or NRR agent scores the record
Field writes back — your team sees it, not us
One Terraform apply, scoped to your AWS account and your permissions.
ECS Fargate, RDS, ALB, Secrets Manager, and IAM roles — all inside your VPC.
ARR, GRR, and NRR nodes come online and register with the orchestrator.
Salesforce or HubSpot webhook connects, and the first nightly pass runs automatically.
Starting at $20K CapEx per node, deployed in 14 days. Infrastructure runs at pass-through AWS + Anthropic token cost — typically ~$300/month at Series B volume.
Tell us where it hurts most — pipeline, churn, or expansion — and we'll show you what a nightly pass across your own book of business looks like.
"It is much cheaper to buy than to build and maintain."
No. Every deployment runs inside your own AWS account — ECS Fargate, RDS, and Secrets Manager in your VPC. The only external call is to the Anthropic API. No Revenue-Growth.AI servers are in your data path, ever.
The infrastructure deploys in 29 minutes via one Terraform command. Full production rollout — including CRM connection and your custom scoring frameworks — typically completes in 14 days.
One-time CapEx starting at $20K per agent node. Ongoing infrastructure is pass-through AWS compute and Anthropic token cost only — typically ~$300/month at Series B volume. No per-task billing. No recurring license. No SaaS markup.
Salesforce and HubSpot, connected via webhooks through an Application Load Balancer inside your VPC with IP allowlisting enforced.
This isn't a replacement — it removes the 30–50% of tedious, manual work your team performs today: deal inspection, health scoring, account reviews, forecast reconciliation. Your most skilled people stop doing repetitive analysis and start acting on objective, always-on readouts.
You have three options, and all of them work: your engineering team can own it like any other internal service (full infrastructure-as-code via Terraform, logs and alarms in your CloudWatch), Revenue-Growth.AI can manage it for you, or we can train your RevOps team to run it themselves. Want it gone? One command tears it down in ~30 minutes.
Those are workflow runners — static rules with no judgment, per-task billing that explodes at scale, and your data on their shared infrastructure. Revenue-Growth.AI applies actual AI reasoning to every event, bills nothing per task, and runs entirely inside your own AWS account.
Static routing rules take over automatically. The hybrid engine is designed so high-volume, predictable events never touch the LLM at all — and edge cases degrade gracefully to deterministic behavior.
A practical guide for revenue leaders who want AI working across their pipeline without handing their customer data to another SaaS vendor.
Helping companies go from AI experimentation to rapid, measurable revenue impact — in days, not quarters.
Built Revenue-Growth.AI after watching a Series B company miss its board target — a critical, committed deal completely mismanaged in plain sight. The realization: if AI can be trained to pass the bar exam and medical boards, it can certainly be trained to automate the 30–50% of tedious, manual work every B2B revenue team performs.
We're early and intentional — working closely with Series A–C SaaS revenue leaders who are ready to stop guessing and start running their pipeline on objective data.
