Strategy, Security & Scale — a practical guide from someone who built it, broke things, and figured out what actually works.
Who we are, how we sell, and why this matters
We use the MEDDPICC framework across three deal tiers
ACV > $1.2M · 12–18 month sales cycle
$400K – $1.2M ACV · ~12 month sales cycle
ACV < $400K · Under 6 month sales cycle
Our tech stack before the AI ecosystem
Good tools, wrong workflow — here's what was breaking
Every revenue team hits the same walls — here's what was slowing us down
The root cause: The CRM forces users to: find the record → find the field → edit it → save. That's four friction points per update. It's not how humans naturally work. Voice is. That's why we moved to it — and everything else followed.
Four core problems, one AI-powered platform — Indi, built in-house at FrankieOne
The pattern: Remove friction, centralise context, let AI do the heavy lifting — humans focus on selling and coaching, not admin.
Three purpose-built apps — each solving a different department's problems
AI-powered deal intelligence, MEDDPICC coaching, deal scoring, and voice-to-CRM updates
Deal management, quoting, pipeline analytics, and revenue operations tooling
Financial modelling, forecasting, scenario planning, and executive reporting
Solving these problems taught us how to build AI the right way
From wild west to intentional ecosystem
Individual teams experimenting with AI independently. No central oversight, no data governance, no shared infrastructure. Marketing using one tool, Sales another, Finance building their own. Every tool had its own API keys to every system.
A single person in RevOps started building AI-powered tools — a deal calculator that replaced $70K/year CPQ software, commission reconciliation, marketing attribution that went from 0.1% to 33.3% accuracy. Delivered results that normally require dedicated teams. Leadership noticed.
These wins exposed a deeper truth: the potential is massive, but 15 different API connections to 5 different systems with different credentials scattered across tools is a ticking time bomb. One wrong data connection and sensitive customer data is in an AI prompt.
Started with just me and the CTO having honest conversations about what was being built and what data it touched. No formal charter, no monthly meetings with agendas — just regular, honest conversations. Over time it naturally grew to include people from security, product, and finance.
Build it right. Centralise the data. Apply governance at the data layer. Let every team focus on their AI interface and features while security controls what they can see. Make AI a capability of the organisation, not a collection of experiments.
Why every company needs an AI ecosystem — not just an AI tool
A sequential approach — each pillar builds on the last
Give SecOps line of sight from day one — not after an incident. Start an AI committee organically: CTO + a key operator, then grow it cross-functionally.
Identify 1–2 key users who are already enthusiastically using AI. Open the doors — give them system access and data access. Their early wins become the proof that convinces everyone else.
Don't wait for the perfect infrastructure. Connect directly to individual systems — HubSpot, Xero, your data warehouse. Ship PoCs. Generate value immediately while learning what scale actually requires.
As more people want to build, you realise: scale requires fewer API connections, fewer keys, more centralisation. Build a governed data lake where the end user just worries about the AI interface and features — not plumbing.
Apply traditional security and governance at the data layer — with all the gates, locks, and field-level security access controls that SecOps needs. The data is centralised and controlled; the users are free.
Making security an enabler, not a blocker
Security teams should see everything AI-related happening across the business in real-time — not discover it in a post-mortem.
This isn't about blocking innovation. It's about making security an enabler.
Would an AI Committee help? Yes — but don't overthink it. We did it organically. It started with just me and the CTO having regular conversations about what was being built and what data it touched. Over time, it grew naturally to include people from different departments — security, product, finance. The key is starting small and being consistent, not forming a committee and waiting for a charter.
The single most important step most companies skip
Find 1–2 people in your org who are already enthusiastically using AI on their own. They're out there — probably quietly solving problems nobody asked them to solve.
Open the doors for them. Give them system access. Give them data access. Give them air cover. Their early wins become the case studies that convince the sceptics and the executives.
These champions become your proof of concept for the entire AI ecosystem. When the CEO asks "does this actually work?" — you point at the deal calculator that replaced $70K in software, or the attribution dashboard that went from 0.1% to 33.3% accuracy. Real results from real people, not a vendor slide deck.
You don't start with a data lake — you grow into needing one
Connect AI tools directly to individual systems — HubSpot API, Xero API, Redshift queries. One connection per tool. Works brilliantly for early PoCs. Fast to set up, immediate value.
✅ Quick wins ⚠️ Doesn't scale
As more people want to build, you realise: 10 tools each with their own API keys to 5 different systems is a security and maintenance nightmare. Centralise the data. Apply governance once.
✅ Scales ✅ Secure ✅ Governed
Campaign data, attribution, web analytics
Revenue figures, customer PII, contract terms
Revenue, billing, forecasts, commission data
Product telemetry, engineering logs, HR data
Usage analytics, feature adoption, NPS
Individual salaries, legal contracts, raw financials
The permission vs. forgiveness spectrum — and the right answer
Slow. Safe. Frustrating. Teams wait weeks for approvals. Innovation dies in committee.
Fast. Risky. One breach and trust is gone. Shadow AI spreads unchecked.
Give teams personal API keys, sandboxed environments, and pre-approved toolsets. Let them experiment freely within boundaries that security has defined. The data layer handles the governance — so the users don't have to think about it. Fast and safe.
Ship fast, learn fast — but never skip the security check
Build PoC with direct API connections. Isolated environment, real business problem.
Security review + stakeholder sign-off on what data it touches and how.
Run with real data, limited users, full monitoring. Catch issues early.
Migrate to centralised data layer. Roll out with documented guardrails.
Key insight: If your PoC fails, the blast radius should be near zero. That's by design, not by luck. And when it succeeds, the path to production should be clear — not a second build from scratch.
What we got wrong and what we learned about people
The hardest parts weren't technical — so you don't have to learn them the hard way
Every tool was a standalone island — its own API connections, keys, and data access. Scaling meant re-creating the plumbing from scratch each time.
Security reviewed tools after they were built, not during. This created friction, rework, and delayed deployments. Bringing security in from the start would have been faster.
15 connections to 5 systems with scattered credentials. More keys = more risk surface. Centralisation isn't optional at scale — it's the only way.
Teams feeling threatened, unclear ownership, resistance to new workflows. The tech was the easy part.
The critical insight: Both sides are right. The solution isn't to pick a side — it's to build infrastructure that makes both right at the same time. Redefine tech as platform builders, create clear swim lanes, start with champions not mandates, and celebrate wins from both sides.
Everything you need before scaling AI across the organisation
If you can't check most of these, you're not ready to scale AI — you're ready to start building. And that's exactly the right place to be. Find your champions, start your committee, ship your first PoC. The ecosystem grows from there.