The operations lead at an education and training provider had a problem she’d lived with for years: her team spent more time chasing data than using it.
Sales results lived in HubSpot. Payments lived in Stripe. Enrolments lived in their LMS. To answer a simple question — “How did we perform last month?” — someone had to pull reports from three systems, manually reconcile the numbers, and build a summary. Every time.
That process took 20 hours a week. Four people touched it. Errors crept in. And by the time leadership saw the numbers, they were already stale.
We built Ion to fix it.
What Ion Does
Ion is an AI agent that connects to HubSpot, Stripe, and the client’s LMS via live API integration. It doesn’t just pull data — it interprets it.
Users ask questions in natural language:
• “What were our sales results last month compared to the month before?”
• “Which deals have been idle for more than 14 days?”
• “What’s our 30-day revenue forecast based on current pipeline?”
• “Show me conversion rates by lead source.”
Ion returns answers in seconds — with the underlying data visible and auditable.
How We Built It
Phase 1: Discovery (1 week)
We mapped the existing reporting workflow end-to-end:
• Where does data originate?
• Who touches it and why?
• What questions does leadership actually ask?
• Where do errors and delays occur?
This phase prevented us from automating the wrong thing.
Phase 2: Integration (2 weeks)
We built secure API connections to each system, with:
• Read-only access (no write operations)
• Credential encryption and rotation
• Logging for audit and compliance
Data stays in the source systems. Ion queries it live — nothing is stored.
Phase 3: Intelligence Layer (2 weeks)
We designed the natural language interface across six intelligence domains:
• Sales results and trends
• Invoicing and payment status
• Period-on-period comparisons
• Pipeline forecasting
• Enrolment analytics
• Lost lead intelligence
Each domain has its own query logic, validated against real business questions.
Phase 4: Testing & Handover (1 week)
We ran Ion against historical data to validate accuracy, trained the core users, and documented the system for ongoing maintenance.
The Results
• 3 systems connected via a single agent
• 6 intelligence domains covered
• 80+ hours saved per month across the team
• Real-time answers instead of week-old reports
The operations lead told us: “I used to dread Monday mornings. Now I just ask Ion.”
Why This Worked
Ion succeeded because we followed a few principles:
1. We solved a real pain. Not a theoretical efficiency gain — a task people genuinely hated doing.
2. We didn’t store data. Governance was built in from day one. No data residency concerns, no new security surface.
3. We trained the users. An AI agent only works if people trust it. We spent time building that trust through hands-on sessions.
4. We scoped tightly. Six domains, three systems, one clear use case. We can expand later — but we delivered value first.
What This Means for Your Organisation
If your team spends hours pulling data from multiple systems to answer recurring questions, that’s a problem AI agents can solve — today, not someday.
The build doesn’t take months. It doesn’t require a data science team. It requires clarity on the problem and a partner who can execute.
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