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I don't just talk about AI.
I build with it. Then I teach what works.

Most AI training ends with a deck and a shrug. Mine ends with your team holding something they built themselves - an assistant, an automation, a workflow that runs on Monday morning without me in the room.

2–3×
Team productivity, measured after implementation
100%
Of participants shipped a working workflow
4.5/5
Rating, most recent open-enrolment session
50+
Keynotes & workshops delivered
What I Believe

Five beliefs I bring into every room

These are not slogans. Each one came out of getting it wrong first - in my own business, or in someone else's.
1

AI is an enabler, not a KPI in itself.

"We rolled out AI to 200 people" is not a result. Hours returned, cost removed, revenue moved - those are results. If a board slide counts licences instead of outcomes, the programme is already drifting.

2

Start with the work, not the tools.

Bolting a chatbot onto a broken process gives you a faster broken process. I map the actual workflow first, find where the time leaks, then redesign it - and only then decide which tool earns a place in it.

3

Move people from AI literacy to AI fluency.

Literacy is knowing what AI is. Fluency is reaching for it without thinking, the way you reach for a spreadsheet. Literacy comes from a keynote. Fluency only comes from building something yourself, badly, and then fixing it.

4

The AI drafts. The human is still accountable.

This one came from my wife, CA Jagariti Mathur, describing AI in her accounting practice: the AI drafts, the CA signs. In regulated work that distinction is the whole ballgame - and it is what lets a cautious profession adopt AI at speed.

5

The leverage is not the platform. It's portable context.

Most people are still writing better prompts. The compounding move is building a context architecture you own - who you are, how you work, your frameworks, your voice - in plain files you can load into any model. I run my whole business on one. When a better model arrives, I move in a day instead of starting over.

The Stack

What I actually use, and what for

I am tool-agnostic in principle and opinionated in practice. This is the working stack behind my own business - not a vendor list. If your team already runs on something else, I teach on your stack, not mine.
Build & automate
Claude Code
My primary build environment. Internal apps, document generation, file operations, this website.
n8n
Workflow automation - the "chief of staff" layer that runs recurring jobs without me.
Codex
Second coding agent. Useful for cross-checking an approach before I commit to it.
Notion
CRM and project system. ~356 contacts, 18-field schema, built and populated via AI.
Think & write
Claude
Long-form writing, proposals, analysis. The model I trust most on nuance and voice.
ChatGPT
Fast drafting and second opinions. Being phased down as reusable skills replace it.
Gemini
Google Workspace integration - anything that lives in Docs, Sheets or Gmail.
Obsidian
The knowledge vault. Plain markdown, no lock-in, readable by any model.
Research & input
NotebookLM
Reading and synthesis. Books and reports in, structured notes out.
Perplexity
Real-time research and market intelligence with sources I can check.
Wispr Flow
Voice-to-text. Most of my first drafts are spoken, not typed.
Copilot
Microsoft 365 surface - relevant for corporate teams already standardised on it.

A caution I repeat in every session: the stack is the least durable part of this page. Tools will churn. The workflow design and the context architecture underneath them will not.

Built, Not Theorised

Where I have actually put AI to work

Everything below is running or has run in a real business - most of it in my own, or in my family's 10-year-old accounting firm, where the cost of getting it wrong lands on us.

The RAS AI Lab - an internal ops app for an accounting firm

In production

Built in Claude Code for R Accounting Solutions, an award-winning practice serving 200+ clients across APAC. Client journey tracking, document intake, a compliance calendar that auto-calculates statutory dates from financial year end, and a two-legged receivable/payable ledger the off-the-shelf accounting software could not hold.

Outcome: 2–3× team productivity · S$12–18k/year cost saved · ~24 hours cut from KYC response time · faster client turnaround.

This website - built end to end, by me

Live

Positioning, proposition, design, copy, the build itself, the domain on GoDaddy, hosting on Netlify, Google Search Console, Google Analytics and Microsoft Clarity. No agency, no developer, no team. Claude Code in the terminal, NotebookLM and n8n. Eighteen years running marketing departments, and I had never done a single one of those things with my own hands.

Outcome: you are reading it. The tools are available to everyone. Knowing what to build is the part I sell.

A networking CRM built and populated by AI

Running

An 18-field Notion CRM - schema, views, and data - designed and built through Claude Code rather than by hand, then populated with roughly 356 contacts across recruiters, former LEGO and P&G colleagues, and client relationships. It tells me who to reach out to this week and drafts a personalised hook for each one.

Outcome: a follow-up habit that survives a busy week, with PDPA handling designed in from the start.

A portable AI operating system for my own business

Daily use

Identity, voice, frameworks and live project state held in plain markdown files that load into any model - plus reusable skills for the writing I do most often. This is belief number five, made concrete. It is also the single thing I am most often asked to help leaders replicate.

Outcome: no platform lock-in, and a consistent voice across every draft regardless of which model wrote it.

Exam preparation with NotebookLM

One-off

A smaller case, but the one that convinced a sceptical professional. CA Jagariti Mathur used NotebookLM to prepare for her CSP examination - source material in, generated Q&A and audio summaries out.

Outcome: 97% on the exam.
Sessions Delivered

Recent AI sessions and where they landed

A selection from the last year. The pattern I care about is not attendance - it is how many people left with something running.

Prior speaking and workshop clients across all topics include Citibank, UBS, Grab, Deloitte, Sephora, PropertyGuru, Johnson & Johnson, Common Purpose, and the Ministry of Home Affairs, Singapore. See the full speaking page →

In Their Words

What participants said was most useful

These are verbatim answers to one question on the post-session feedback form - "what was the single most useful thing today?" - not curated testimonials. Notice how few of them mention a concept and how many name a thing they built.
How to create AI assistant
Learnt about n8n. Heard about it for the first time.
Walking through the specific steps of creating the workflow from newsletters to podcast
Difference in various AI tools and how the projects work
Knowledge on other AI apps like n8n, Claude including ChatGPT
Email automation

AI Spark Session, Singapore, May 2026 · verbatim answers from the written feedback form · session rated 4.5 / 5.

Executive Session
The most useful part was actually creating an AI assistant. Claude as an assistant for so many things I do every day.
AI Spark Session participant Executive workshop · Singapore, May 2026
Starting From Zero
I am not at a stage to suggest improvements. I am at the learning phase. Thank you for the guidance.
AI Spark Session participant Rated the session 5 / 5 · asked about the 5-Day AI Pathway next

The most common request in the "what could we improve?" column was more use cases and more case studies. Nobody asked for less hands-on time.

- My read of the same feedback form
Results

The numbers I am willing to be held to

Every figure below comes from a specific engagement, not an industry benchmark. Where a number is from my own family's business, I say so - because you should weight it accordingly.
2–3×
Team productivity after AI implementation
R Accounting Solutions, measured post-rollout
S$12–18k
Annual cost removed from the practice
R Accounting Solutions
~24 hrs
Cut from KYC response time per cycle
R Accounting Solutions
Faster client turnaround
R Accounting Solutions
100%
Participants shipping a working workflow
AI Spark Session, May 2026, 20 attendees
4.5/5
Session rating
AI Spark Session, May 2026
97%
CSP exam score, prepared using NotebookLM
CA Jagariti Mathur
~356
Contacts in a CRM designed and built with AI
Crossroads Navigator & RAS joint CRM
How We Work Together

Three ways in, depending on how far you want to go

Most organisations start at the keynote and stop there. The ones that get a return keep going to the third column.
1 hour · Inspire

AI Keynote

A no-fluff tour of what is already working, what is hype, and what your people can use on Monday morning. Designed to convert AI anxiety into AI energy.

  • What AI is genuinely good at - and where it still fails
  • Live demonstrations, not screenshots
  • The adoption curve, and why most teams stall at the same point
Inquire
3–6 months · Execute

Fractional AI Adoption Officer

I embed part-time and drive real adoption - mapping workflows, building the automations, and coaching your team until AI becomes an operating habit rather than a one-time event.

  • Workflow mapping and opportunity sizing
  • Automations built with your team, not for them
  • Governance: what AI drafts, what a human signs
  • Capability that stays after I leave
See fractional →
Let's Start

Where do you want your team to be in 90 days?

Tell me what your team does all day and I will tell you, honestly, whether AI moves the needle on it. If it does not, I will say so.

Book a 30-minute call

Straight into my calendar. No form, no back-and-forth on times. Bring one workflow that is eating your team's week.

Pick a time →

Or just stay in the loop

What I am building, what is working, and what turned out to be hype. Roughly monthly. No sequences, no pitch.