A human cortex drawn as points and lines, with 14 JAW AI services named on it
JAW AI Book a 20-minute scope call

AI systemsthat keep runningafter we leave.

Forecasts, reconciliations, and an assistant that answers from your own files. For Singapore companies whose numbers live in Excel and whose decisions live in an inbox.

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The offer

Fourteen things we do. Five ways to buy them.

Advise

Where AI and IT spend pays off, and what's worth keeping

AI Consultancy

A two-week plan for where AI pays off first

Read

Two weeks that end with a ranked list of where AI pays off in your business, and what each item costs to build and to run. We interview the people who do the work, read the data you already have, and write one page per candidate: the workflow it changes, the data it needs, the price. Where an off-the-shelf tool is good enough, the list says so and names it. You keep the document whether or not you build anything with us.

IT Transformation

From legacy and spreadsheets to a stack your team can run

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Most mid-market companies run on a legacy system nobody dares touch, a dozen spreadsheets, and three SaaS tools that do not talk to each other. IT transformation is the plan and the migration: which systems stay, which go, what replaces them, and in what order, so the business keeps running while it changes. We map the data first, move one process at a time, and leave every system documented, integrated, and owned by your own people rather than by us.

AI Optimisation

Your existing AI pilot, faster and cheaper to run

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A pilot that works in a demo and is slow, expensive, or flaky in daily use is the most common thing we are asked to fix. We measure the system you already run on real traffic first: latency, cost per request, failure rate. Then we change a copy, measure again, and keep only what helps: smaller models where accuracy allows, caching, batching, prompt and retrieval fixes. You get the numbers before and after, and an evaluation set your team can rerun without us.

Build

Systems that run on a schedule inside your own accounts

Forecasting

Demand, occupancy, and cash forecasts you can act on

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Occupancy, covers, demand, cash: most teams forecast these in a spreadsheet by feel, and the spreadsheet is usually not wrong, just late. We build a model on your own history, backtest it walk-forward against what your team actually forecast on the same dates, and run it in parallel before anyone relies on it. It lands every morning where the team already looks, with an interval on every number. The 13-week cash forecast from a Xero ledger, every figure drilling to its source rows, is the first of these we productised.

Automation

Repeated manual work, moved into software

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The work that eats a team's week is usually the same twenty tasks: copying between systems, chasing approvals, building the same report, triaging the same inbox. We shadow the team for two days and count the hours rather than ask for them. Then we take the three tasks with the most hours and the least judgement, and move them into software that runs in your own cloud account. Anything that touches money or a customer still waits for a person to approve.

Company Brain

Your documents, answered by one internal assistant

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The answer to most internal questions exists in a document someone wrote years ago, and the person who knows where it is has left or is on leave. A company brain is one assistant inside your own systems that answers from your documents, respects who may see what, and shows its source every time. Accuracy is measured on a question set your team writes, before rollout and every month after. It reads your wiki; it does not replace it.

Custom Software

The dashboard, integration, or internal tool nobody sells

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Not every problem needs a model. Sometimes the job is a dashboard that reads three systems at once. Or an integration between the PMS and the accounting software. Or an internal tool for the one process your SaaS cannot do. We build that plain software with the same squad and the same rules as everything else: in your accounts, with a runbook, with the source in your repository. Where a model is the cheapest way through one messy step, it goes in; where it is not, it stays out.

Run

Our people in your tools, our pager on your systems. Monthly.

Top AI Talent

Senior engineers and analysts, managed from Singapore

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When the plan needs more hands than a project gives you, we supply them. Senior AI engineers, software engineers, data analysts, and back-office operators, hired and managed by us, working inside your tools and your hours, at a price a mid-market budget can carry. You get named people, not a bench: a Singapore lead who is accountable, a two-week trial on real work before any commitment, and the same runbook discipline as our builds. Scale from one person to a team, monthly, and stop when the work stops.

Managed Operations

We keep running what we built. Stop any month.

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Every system we hand over is designed to run without us. Some teams would rather it ran with us anyway. Managed operations is a monthly line. We watch the forecasts and retrain the models on schedule. We refresh the governance evidence. We answer the pager when an automation stops at two in the morning, and send a one-page report of what ran and what broke. The runbook stays yours, the source stays yours, and the arrangement ends the month you say so.

Grow

The commercial side: pipeline and introductions, on retainer

Business Development

Pipeline, partnerships, and the systems that keep them moving

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Growth usually stalls for a boring reason: leads sit in an inbox, follow-ups depend on one person's memory, and nobody can say which channel actually pays. Business development is the commercial side of what we build. A pipeline your team can see. Outreach and follow-up on a schedule, with a named person approving every send. Partner and channel introductions from our own network in Singapore and the region. And a monthly number that says what each channel returned. We work on retainer, and the pipeline is yours.

Regulate

For CROs and Heads of Compliance under MAS. A named person signs

Model Validation

Rebuilt, diffed, and signed by a named validator

Read

Independent checking of the models a financial institution already runs: credit, pricing, capital, forecasting, and the AI ones. We rebuild the model ourselves and compare our outputs against yours, review the documentation against a standard test battery, and issue a signed validation report with a remediation plan. Every finding points at evidence, a test result or a document line, so a reviewer can check it without trusting us. A named validator signs, with the credentials the regulator will ask about. Sold per model, to the Head of Model Risk or the CRO.

AI Governance

Your AI controls evidenced continuously, not once a year

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Most firms review their AI controls once a year and assemble the evidence in a three-week scramble when the regulator asks. We map every AI system you run to the controls a regulator expects of it. Then we collect the proof that each control is working, and keep it in a pack that is current on any day it is asked for. It ships as code and scheduled checks in your own repository, not as a platform you rent, and it is refreshed under managed operations. Sold to the CRO or Head of Model Risk.

Regulate Pilot

Pilot. The first engagement funds the build.

Compliance Operations Pilot

Calendar, filings, and evidence for MAS-licensed firms

Read

An outsourced compliance function for payment institutions and digital-asset firms that hold a MAS licence. We hold the compliance calendar and file on time. We write and maintain the manuals, and keep the evidence behind every obligation. When MAS publishes, we tell you what it means for your licence specifically. We read each publication once and know which of our clients it binds; everyone else reads it again, by hand, for every client. Your own MLRO stays the accountable officer; a named compliance lead of ours signs the work. Sold by introduction while the practice is small.

Suitability Screening Pilot

Every advisor conversation screened against your suitability rules

Read

Today a firm hand-checks a small random sample of advisor conversations and never listens to the rest. We screen every recorded conversation against the firm's own suitability rules. The conduct team gets a short queue of flagged cases, each cued to the moment that triggered the flag. People spend their time only on what was flagged, and the sample becomes the whole population. Priced per advisor, sold to the Head of Compliance or Head of Conduct at an insurer, bank, or advisory firm. A pilot: the first engagement funds the speech pipeline and the PDPA handling.

See it work

Same 52 weeks. Three forecasts. You pick.

White is what really happened. Amber is the model's guess. Drag the slider to swap the model.

Next 12 weeks

The model

What really happenedThe model's guess

Same data, three answers. Picking the right one is the job.

Sample data, generated for this page. Illustrative, not a client's numbers.

See it sort

Fourteen leads. One formula you can read.

Every lead here has five numbers: last contact, replies, company size, where it came from, and a budget signal. The score is a weighted sum of those five, nothing else. Move the slider and the ladder re-sorts. Click any lead and the receipt on the right shows its five products and their sum.

Ranked by score4 Hot8 Warm2 Cold

All five signals weigh 20 points each. Our starting point, before we tune it with you.

Last contact20 Replies20 Company size20 Came from20 Budget20

Hot Call this week.Warm Keep the thread going.Cold Park it. Look again in 30 days.

Sample leads, generated for this page. Illustrative, not anyone's pipeline.

Our profile

The people building JAW AI.

Aaron

Aaron is a digital transformation and AI business leader with over 13 years of experience across Oracle, Deloitte, and Accenture, helping organisations translate technology and emerging innovations into tangible business outcomes. He has worked across strategy, technology consulting, cloud, AI, and large-scale transformation programmes across both the public and private sectors. With a background spanning strategy, technology, product, and business development, Aaron brings a practical, business-led approach to helping organisations identify, adopt, and scale AI and digital technologies. He holds an MBA from Singapore Management University and a Bachelor of Computing (Information Systems) from the National University of Singapore.

  • AI Transformation Strategy
  • Digital Transformation
  • Business Process Reengineering
  • Data & Cloud Transformation
  • Technology Product Management

Mei Ling

Mei Ling has 20 years of experience in technology consulting, service delivery and market development across the private and public sectors, with senior roles at global consulting firms, a leading systems integrator and government agencies. She has led large, high-stakes programmes in enterprise platforms and IT operations, managing teams of more than 70 consultants. Her expertise spans go-to-market strategy, client development, solution design, programme delivery, quality assurance, and stakeholder engagement. She has consistently beaten growth targets and helped organisations adopt new technology, most recently in enterprise AI. She holds a BSc in Electronics Engineering from a UK university.

  • Technology and AI Adoption
  • Digital Transformation
  • Business Development
  • Programme and Delivery Management
  • Service Operations Management

Wilfried

Wilfried leads the technology side at JAW AI, spanning engineering and AI. He previously worked as a quant and Head of Mathematics at a Y Combinator-backed company, building its pricing models and trading strategy, and as an AI engineer at Singapore Management University. He has also run systematic trading books across crypto, forex, and equities markets for over five years. He studied at leading technical universities in Indonesia, Taiwan, and Singapore, and is now a PhD candidate at Nanyang Technological University's College of Computing and Data Science, researching AI for finance and economics.

  • Quantitative Trading & Market Making
  • AI Research & Model Development
  • Systems & Automation Engineering
  • Data Infrastructure & Backtesting
  • Technical Product Strategy

Tell us the problem. We'll tell you which of the fourteen it is.

Twenty minutes, and it ends in one of three things: a yes, a no, or the name of someone better suited than us.

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