Most companies bolt AI onto their GTM stack. We embed it into the operating system to orchestrate the data.
AI doesn't suggest competitive intelligence; it generates it, structures it, and automatically distributes it to your Competitive Intelligence object.
AI doesn't recommend personalisation; it analyses company data, synthesises GTM context, calculates persona-specific messaging, and queues personalised outreach sequences ready to send.
AI doesn't propose strategy; it builds GTM Strategy records with positioning, value props, and persona-mapped messaging based on your product and sales data.
Intelligence embedded at the object level means your GTM system thinks, then acts. Not the other way around.
Standard approach: Sales rep manually researches competitor, pastes notes into a text field.
ARISE OS approach:
When a competitor domain is entered, AI workflows automatically:
Result: Your team gets competitive intelligence that updates itself, not static notes that go stale.
Standard approach: Marketing creates a static PDF battlecard that lives in a Google Drive folder no one can find.
ARISE OS approach:
Digital Battlecards pull data FROM Competitive Intelligence objects and combine it with your GTM Strategy:
Result: Sales reps get contextual battlecards inside active deals, not outdated PDFs.
Standard approach: Strategy lives in PowerPoint decks and Notion docs that no one references.
ARISE OS approach:
Your product and sales data builds executable GTM Strategy records:
Result: Strategy isn't a document. It's an object that informs every AI workflow across your GTM motion.
Step 1: Intelligence Gathering
AI workflows pull company data (firmographics, technographics, hiring signals) and synthesise it with your GTM Strategy object to understand:
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Step 2: Persona-Specific Communication
The layer applies MEDDIC + your value proposition to determine:
Step 3: Scoring & Prioritisation
Every prospect gets scored: Hot, Warm, or Nurture based on:
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Step 4: Content Strategy Alignment
AI analyses your existing content library and:
Result: Sales and marketing operate from the same intelligence. ABM strategy writes itself.
The Methodology:
This isn't a feature. It's a framework. The AI Prospecting Layer uses native HubSpot properties, custom properties, and related objects to build executable outbound strategy that scales without losing personalisation.
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Traditional integrations are point-to-point. Want to pull LinkedIn data into HubSpot, then use it to personalise Customer.io campaigns, then feed it back to competitive analysis? You're building three separate integrations, managing three sets of credentials, and hoping nothing breaks.
MCP is a standardised protocol that lets AI workflows query data across systems through a single, secure interface.
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We connect to HubSpot's official MCP server, which means:
We build custom prompts and queries that:
Why This Matters:
Better customer understanding → higher win rates. Faster data synthesis → shorter deal cycles. Cross-system intelligence → expansion opportunities no one else sees.
MCP isn't just a technical implementation. It's a competitive moat.
Token Usage Calculation
Every AI-powered workflow calculates token consumption before execution:
We show you exactly what each workflow costs and let you set budgets by workflow type.
Workflow-Level Controls:
Optimisation Built In:
Transparency: Monthly reporting shows:
The Result:
You know exactly what AI costs, what it produces, and whether it's worth it. No surprise bills. No runaway spend. Just intelligent resource allocation.
Map your data and define orchestration opportunities.
Objects + AI foundation live within days.
Pre-built workflows activate fast.
We push automation, prediction, and generation forward every sprint.
Iteration driven by real pipeline and buyer behaviour.
Whether it's building campaigns, defining ICPs, or mapping your product-led motion, ARISE OS® makes sense of the noise fast. And with agentic capabilities on the roadmap, we’re building towards fully adaptive GTM models.
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