Prepared for Sun Global Investments
Sun Global's AI transformation starts where the revenue is made.
IA generates the cash flow, and IA is where Sun Global's growth is bottlenecked. You scaled the team down because some advisors couldn't bring in enough flow to justify their cost. We believe the real constraint isn't the advisors; it's the infrastructure around them.
- Prepared for
- Dhairya Gupta
- Company
- Sun Global Investments
- Prepared by
- Alex Girardet & Alex Garcia Chicote
- Date
- 17th April 2026
What We Heard
Your best advisors are spending their time on admin instead of on clients. Client context is scattered across Outlook, Teams, WhatsApp, phone, Excel sheets, email threads, and people's heads. The previous CRM was abandoned because logging every interaction by hand cost more time than it saved.
"My biggest issue was the number of follow-ups that I have to do. I need those note-takers to then put it into me and tell me: these are the things, to-dos that I need to do for that particular person. And these are the deadlines. Fix up my diary. Fix up my follow-ups."
— Amit
"We actually send those ideas to everybody. But what would help is that if five out of those 50 would have told that if this kind of idea is there, then let us know. Rather than calling all 50, you can call up only those five."
— Hitesh
The previous CRM didn't fail because people were lazy. It failed because it asked advisors to do the work twice: have the conversation, then log it. Any new system that asks for that will fail the same way.
The fix comes in two parts. First, the CRM becomes Sun Global's shared intelligence layer: the living, structured record of every relationship the business runs on. Second, AI assistants around it do the work of keeping that intelligence alive. Absorbing admin, surfacing context, freeing advisors to focus on what actually generates revenue. The team stops feeding the system. The system starts feeding the team.
Phase 1 addresses both: a clean, consolidated CRM in Attio, and a personal AI assistant for Amit that manages it on his behalf.
Phase 1: What You Get
The dirty but essential work. Before the assistant can do anything useful, it needs something to work from. We consolidate all of Sun Global's scattered relationship data into one place: Attio, the most AI-native CRM on the market.
Excel files, email threads, the old CRM, per-deal project files, prospect lists. All migrated, cleaned, and structured around how your IA team actually works: clients, prospects, interactions, preferences, deal interest.
Attio is ~£29/user/month. It's UK-registered, ICO-registered, SOC 2 Type II and ISO 27011 certified.
Outcome: This is the first step in regaining control of Sun Global's relationship data. Cleaning it, organising it, and storing it properly. The foundation everything else builds on. Amit's assistant has something to work from on day one rather than starting cold.
Amit gets a personal AI assistant with its own WhatsApp number, connected to his Outlook email and calendar, and to the CRM. He can talk to it, ask it questions about his clients, his calendar, his emails, and instruct it to update the CRM on his behalf. One place to go instead of juggling five systems.
WhatsApp
Outlook (email + calendar)
Attio CRM
Outcome: Amit has a personal assistant that knows his calendar, his emails, and his client data. If he wants something in the CRM, he tells the assistant.
The assistant becomes proactive. Instead of waiting for Amit to ask, it keeps the CRM alive on its own. Amit just approves.
- Recorded call logging. Amit's client calls are recorded. The assistant ingests the transcript, extracts relevant notes, and drafts a CRM entry for Amit to approve. When the call touches a trade or investment advice, the assistant understands that this must be evidenced through a written record with the client. It proactively drafts the confirmation email for Amit to review and send via recorded channel, maintaining the FCA audit trail automatically.
- Email & calendar logging. The assistant silently reads Amit's Outlook and logs client interactions against the right contact in the CRM. Fireflies meeting summaries get pulled in too, instead of sitting unread in his inbox.
- Voice note debrief. For conversations the assistant can't observe (in-person meetings, unrecorded calls, WhatsApp chats), Amit sends a voice note afterwards. The assistant transcribes, structures, and sends back a summary for approval. The same compliance audit trail applies: if the voice note mentions a trade or advice, the confirmation email gets drafted too.
- Client preference memory. As the assistant logs interactions, it builds a structured profile for each client: risk appetite, return expectations, asset class interests, historical investment decisions. No more fragmented Excel sheets or relying on memory. When a client says no to an idea, the assistant captures why (price, sector, timing, risk) so that refusal sharpens every future recommendation.
- Client context on demand. Amit asks "what's the latest with Steve?" and gets the full picture: recent conversations, pending actions, preferences, history. One question, complete context.
Built-in protections
Sensitivity filter. The assistant is built to exclude confidential and personal information (health, family matters, anything not related to investments) from CRM entries.
Approval gate. Before anything is stored in the CRM, the assistant sends Amit a summary for approval. AI is powerful but not infallible. Amit is the final gate, so nothing slips through.
Outcome: The CRM stays current without anyone carrying the burden, which is what killed the last one. Amit walks into every conversation knowing exactly where things stand. That context lives in his head today, which means it doesn't scale. With the assistant, it does. When Hitesh joins in Phase 2, he sees Amit's relationship context before making a recommendation; Amit sees how clients responded to Hitesh's ideas before picking up the phone. What starts as Amit's personal assistant becomes Sun Global's shared intelligence layer.
Once the CRM has context, the assistant doesn't just sit on it. It acts.
- Follow-up engine. After every interaction, the assistant extracts action items, deadlines, and reminders. Surfaces them to Amit based on his schedule and upcoming meetings. Nothing falls through the cracks.
- Idea distribution & client matching. When an investment idea is ready, the assistant matches it to clients whose preferences and history align. "Who would be interested in this?" becomes a question the assistant can answer. The spray-and-pray stops.
- Prospect reminders. The assistant knows Amit's active prospects and nudges him when someone hasn't been touched in a while, or when timing is right based on previous conversations.
- Daily market snippets. Amit currently spends ~30 minutes every morning preparing a market summary for IA clients. The assistant drafts this proactively, tailoring where possible based on client interests, and sends it to Amit for a quick review before distribution.
Outcome: These capabilities get smarter over time as the CRM fills with context. The more Amit uses it, the better the matching and suggestions become. It's a flywheel. The value compounds.