01 — Assess · 2–4 weeks
Find the expensive version of the problem
- Workflow and cost mapping
- Data and system inventory
- Failure and exception analysis
- A written recommendation you can act on without us
Software engineering · Data · Applied AI
Build the systems yourbusiness will run on next.
AYAI Pro designs and engineers digital products, data platforms, and applied AI systems for companies with complex operations.
We work as a senior partner rather than a supply of developers: we take positions on architecture, own the decisions, and stay accountable through production and beyond.
Controlled automationLive schematic
Confidence threshold · human approval · audit trail
01The problem
Most companies do not have a technology problem. They have a system that was never designed to carry the work it now carries.
The systems that cause the most damage are rarely the ones on the architecture diagram. They are the workarounds that grew around a gap, became load-bearing, and never got an owner.
AA process that moves real money runs in a spreadsheetNobody owns it, everybody depends on it, and it fails quietly. The cost surfaces two months later in a reconciliation.
BSeveral systems of record, no agreed source of truthFinance, operations and product each report a different number, and the meeting is spent arguing about the data rather than the decision.
CAn AI pilot that demonstrated well and changed nothingIt was never connected to an approval path, an audit trail, or anyone’s actual job, so the workflow stayed where it was.
DA platform that gets more expensive to change every quarterDelivery slows, estimates inflate, and the roadmap starts being shaped by what is safe to touch rather than by what the business needs.
We work on the version of the problem that is actually expensive, which is often not the one described in the first conversation.
02Practices
Five connected practices
One accountable team, across the whole system.
Most failures happen at the boundaries — between product and platform, between the data model and the decision, between a working prototype and something operations can rely on. We hold those boundaries rather than handing them between vendors.
Product and platform engineeringCustomer products and internal platforms built to hold up under real operating load, and to remain changeable after they succeed.Data platforms and analyticsIngestion, modelling, contracts and governance, so a single number means the same thing across finance, operations and product.Applied AI and automationDecision systems with grounding, evaluation, human approval and an audit trail designed in from the first week rather than added at review.Cloud and platform modernizationMoving critical systems off what they run on now, in reversible steps, without interrupting the business that depends on them today.Dedicated engineering teamsSenior cross-functional squads accountable for a defined outcome and for the architecture decisions that reach it.
03Applied AI
Controlled automation
Automation is only useful when someone can be accountable for the result.
A model produces an opinion. A system produces a decision that a named person is willing to stand behind. Almost all of the engineering sits in the gap between those two things — in where the approval step is placed, what evidence the reviewer sees, and whether last Tuesday’s decision can be reconstructed.
Records enter from the left. Those that clear the confidence threshold complete automatically; the rest divert to human review with their evidence attached. Where that threshold sits determines what the system can ever be trusted to do.
Cleared automatically
High confidence, low consequence, reversible. Sampled for quality rather than reviewed case by case.
Routed to a person
Low confidence, high value, or irreversible. Arrives with sources, extracted facts and the rule that was applied.
Recorded either way
Inputs, version, policy, output, approver and timestamp. This is what separates a pilot from a production system.
2014
Building production systems
Leadership has been building technology companies and production systems in Vietnam since 2014.
5
Connected practices
Product, platform, data, applied AI and modernization, held by one team rather than five vendors.
4–6wks
Typical first engagement
A fixed-scope assessment ending in a written recommendation you can act on without us.
5
Markets served
United States, Singapore, Australia, Japan and Vietnam, with delivery run from Vietnam.
04Selected work
Representative engagements
Systems we have built before.
Drawn from our leadership’s delivery history since 2014. Client names and figures are withheld until we hold written permission to publish them.
01Trade capture and reconciliation, commodities deskEvent-sourced trade log, deterministic reconciliation with per-instrument tolerances, and a ranked exception queue with human sign-off.02Clinical and administrative operations platformCanonical patient model across sites with different standards, consent evaluated at read time, full traceability on every access.03Payment operations and ledger integrityDouble-entry ledger as source of truth, idempotent processing across retries, automated break detection, exception-first console.04Supply and pricing data platformStreaming ingestion with per-partner quality gates, contracts enforced at the boundary, pricing models explainable to a merchant.
05Coverage
Sector and practice
Where our experience is deep, and where it is not.
Depth of delivery experience by sector and practice. Where a cell is light we will say so in the first conversation rather than after the contract.
| Sector | Product | Data | Applied AI | Modernization | Teams |
|---|---|---|---|---|---|
| Financial services | |||||
| Healthcare | |||||
| Trading and commodities | |||||
| Commerce and marketplaces | |||||
| Enterprise operations |
SelectiveSubstantialDeep
01Financial servicesLedger integrity, reconciliation and controls that hold up in an audit rather than in a deck.02HealthcareClinical and administrative data that has to be correct, consented and traceable.03Trading and commoditiesPosition, exposure and settlement across systems that were never designed to agree.04Commerce and marketplacesSupply, pricing and fulfilment data moving at the speed the business actually operates.05Enterprise operationsThe workflows still held together by inboxes, spreadsheets and one person’s memory.
06How we engage
Delivery model
Four phases, sized to their real duration.
Assess is small and deliberately inexpensive. Build is the long phase. Operate is open-ended, because a system that stops being maintained is a system about to be replaced.
Assess2–4 weeks
Architect2–4 weeks
Build8–20 weeks
OperateOngoing
02 — Architect · 2–4 weeks
Decide before building
- Target architecture and trade-offs
- Data model and contracts
- Approval, audit, and control design
- Delivery plan with real sequencing
03 — Build · 8–20 weeks
Ship in slices someone can use
- Working software every two weeks
- Migration alongside the live system
- Evaluation harnesses for anything AI-driven
- Operations tooling built with the operators
- Handover documentation written as you go
04 — Operate · Ongoing
Stay accountable
- Reliability and cost ownership
- Model and pipeline monitoring
- Roadmap and architecture review
- Transfer to your team when that is the right answer
Ways to begin
Start narrow. Expand when it has earned it.
↳Workflow Baseline SessionA 45-minute, application-only session around one expensive workflow. You leave with a draft map, a candidate failure list, and a baseline sheet. No cost. Twelve a month.↳AI + Data Operations SprintFour to six weeks. We take one expensive, fragmented, high-friction workflow and turn it into a working, measurable operational system — with the approval path and the numbers attached.↳Product discovery and architecture sprintFor a product or platform you are about to commit budget to. Ends with an architecture, a delivery plan, and an honest view of what it will cost.↳Platform modernization assessmentFor systems that still work but are getting expensive to change. Sequenced migration options, with the risk of each one written down.
07Track record
Our leadership has been building technology companies and production systems in Vietnam since 2014.
That decade covers healthcare systems, financial technology and financial operations, trading platforms, commodities technology, data products, applied AI and enterprise operations software, for Vietnamese and international businesses.
Vietnam is where we build. It is not the reason to hire us. Clients choose AYAI for the systems we have shipped and the decisions we are willing to own.
About the companyClient profile
50 to 500 people, with systems already in production
Engagement shape
Fixed-scope assessment first, expanding only when it has earned it
What we do not do
Staff augmentation, chatbot projects, or lowest-cost developer hours
08Questions
Before you get in touch
Questions we are usually asked first.
How does an engagement usually start?
With a fixed-scope assessment of two to six weeks, depending on the problem. It ends in a written recommendation, an architecture position and an honest view of cost. You can act on it with or without us, and a meaningful number of clients take the first one in-house.
Do you work with our existing engineering team?
Usually, yes. Most of our work sits alongside an internal team that is capable but constrained, or that needs senior architecture ownership on a specific problem. We integrate with your product and engineering processes rather than running a parallel delivery organisation.
What does senior ownership actually mean in practice?
The people who make architecture decisions stay on the engagement. Every significant trade-off is written down with its alternatives and its reasoning, so your team can argue with our decisions in two years. There is no pattern of experienced people appearing at the sales meeting and again at escalation.
Why are your case studies not named?
Because we do not yet hold written permission to name those clients or publish their figures. We would rather label the work as representative and show you the architecture than imply results we cannot evidence. Where we do have permission, we say so.
How do you approach AI specifically?
As a systems problem rather than a model problem. We design the workflow, the grounding, the evaluation set and the approval path before selecting or tuning anything. In most engagements the difficult work turns out to be data quality and organisational agreement about what "correct" means.
Where is the team based, and how do you work across time zones?
Delivery runs from Vietnam, with clients in the United States, Singapore, Australia, Japan and Vietnam. We hold dedicated evening blocks for US clients and overlap directly with Asia-Pacific working hours. Engagements run on a written cadence of decisions, demos and metrics, so progress does not depend on anyone being in a call.
Insights
Working notes from production systems.
Perspective
Why enterprise AI fails between prototype and production
The model is rarely the whole problem. Production AI succeeds or fails on workflow design, source quality, permissions, evaluation, and ownership.
Guide
Designing AI workflows with human approval and auditability
Human review is not a single button. It is an operating design problem involving thresholds, evidence, escalation, and accountability.
Field Note
The hidden operating cost of reconciling data by hand
When every important report begins with exports and spreadsheet repair, the organization pays in delay, uncertainty, and duplicated judgment.