AI systems that help your customers, and turn every customer conversation into insights you can actually use.
Hey there, I'm Front Desk, an AI voice agent. How can I help?
An AI voice agent that can talk through the labs, Jason's background, and what CX Matters does, or take a message for him, all grounded in what's written here.
One stream of customer feedback, running through every system here.
Working demos showcasing how AI can turn customer-experience data into decisions. Every one is live: open it and it runs against a real database and a real model, reading from one synthetic dataset of 549 pieces of customer feedback.
Humans. Data. Revenue.
Marketing makes a promise. Product sets an expectation. Every customer conversation is where a real person, with a real problem, finds out if it was true. Each of those conversations is three things at once. Most companies capture none of them.
Someone stuck and about to walk, forming the memory they will repeat to other people.
The export flow is failing. How many others hit this exact step and said nothing?
"Find another tool" is churn forming in real time, and it is still reversible.
Humans.
It's someone at a real moment — frustrated, doubtful, or just deciding if they're making the right call — forming the memory they'll repeat to others. That memory becomes word of mouth — and word of mouth is the cheapest marketing you'll ever get, or the most expensive you'll never see.
Data.
Your teams sit on the richest unstructured dataset in the company and let it evaporate one closed conversation at a time. Mined properly, it tells you exactly what to fix, what to change, and what each one is costing you.
Revenue.
It's the hinge between the revenue you keep and the revenue you win — the deal that stalls, the renewal decided in week one, the account that churns or expands. Customer experience isn't a cost centre. It's the cheapest growth you have.
Ask a question and get an answer grounded in real customer feedback, not a hunch.
A working system, not a mock-up. It searches 549 pieces of customer feedback from a fictional B2B SaaS company and cites every claim back to the verbatim it came from.
Diagnose. Build. Lead.
AI systems for customer intelligence, at three depths, without the full-time hire. Every engagement is build-led: I go in through the problem, prove the value with a working system, and leave the function sharper than I found it. Everything in the labs above can be built on your data.
Voice of Customer Diagnostic
A fixed-fee sprint. I take your real conversations (tickets, calls, churn notes, sales objections) and turn them into a prioritised read: what is breaking, what it is costing, and what to fix first. You get a decision document your product and exec teams can act on, and a recommendation for the system that would close the gap. This is the front door to everything below.
AI Systems for Customer Intelligence
Knowing when to build custom and when to configure what you already run, like Intercom Fin, so it resolves instead of deflects, is most of the job.
Either way you get the systems in the labs, on your data. Retrieval over your feedback, cited to source. Agents that investigate your at-risk accounts before anyone asks. Evals that catch what your support AI gets wrong before your customers do. MCP servers that put customer data inside the tools your team already has open. Voice agents that answer, escalate, and keep the conversation as data instead of losing it.
No two come out the same, because no two businesses have the same data, the same stack, or the same priorities. I build working software, shaped to how your business actually runs.
Fractional Customer Intelligence Lead
The senior seat, without the full-time seat. I shape strategy with your leadership, stand up the measurement, sharpen the AI, and run the loop that turns daily conversations into decisions. Part operator, part coach. Any system I build, I can also run: hosted, monitored, and improving as your customers change. The function holds its edge after I have gone.
Examples of my work
Rovo, Atlassian's flagship AI product, was launching to ~200 enterprise accounts, representing $61M in annual spend, with no fast way to know what they actually thought. I built a custom SQL and R reporting pipeline, with an AI agent synthesising raw feedback into structured, bi-weekly reports for product and go-to-market. Signal reached the teams who needed it, shaping real product decisions.
Heidi's AI front line sits in a regulated clinical environment, where a wrong answer isn't an annoyance. It's a safety issue. I designed the Conversation AI flows and agentic escalation paths running across it, on a Databricks VoC pipeline with Intercom's Fin as the resolution engine. CSAT rose 75%→87% while support volume grew 70% — accuracy holding as scale increased.
Immutable needed a 24/7 global support function in a nascent, fast-moving web3 space — built from nothing, without a large team to run it. I built Intercom from the ground up, with chatbot-first conversational design engineered to resolve rather than deflect. Self-serve resolution went from 0%→90% at scale — proving the model: volume to the machine, moments to people.
Most people who can lead the function cannot build it.
Fifteen years ago I started on a support desk at Apple. Since then: a developer, Intercom's first Sydney hire, CX leadership at Immutable, Shippit and Heidi, and Voice of Customer programs inside Atlassian. I hold a postgraduate qualification in data science, and SQL, Python, R & Ruby are how I let the data set the priorities. The idea that has run through all of it is simple: customer conversations, not tickets. The best products are built by teams who genuinely listen.
Used to deflect rather than resolve, AI walls customers off from the help they need and throws away the signal in the same move. Used right, it is what makes staying human affordable at scale. The AI era needs a hybrid skillset, the player-coach: someone who can read what an agent is getting wrong, redesign the handoff to fix it, coach the team through the change, then sit with the C-suite and explain what it means for the business.
I am forward-deployed by instinct. I build working systems to prove value, then partner with engineers to harden them. I have done both, leading the function and building what it runs on, with numbers behind it.