AI built to run in production, not just in a pilot deck
We design, build, and operate AI systems for healthcare, life sciences, social impact, and government organizations across Asia-Pacific, the same machine learning, language, and automation technology already running inside our own AwardScience™ and Pati™ platforms.
Building AI for global healthcare, life sciences and public-sector organizations
Built on enterprise-grade AI infrastructure
Why AI, why now
The gap between organizations piloting AI and organizations running it in production is where the advantage now sits.
73%
of organizations report faster, more confident decisions after integrating AI into core workflows.
66%
of organizations using AI in customer- and applicant-facing service report measurably higher satisfaction.
90%+
of executives expect AI to be a primary driver of competitive advantage within five years.
Figures reflect broad industry research on enterprise AI adoption.
Three capabilities, one AI practice
We don't sell "AI" as a single line item. Every engagement draws from the same three disciplines, in whatever mix your problem actually needs.
Turn historical and operational data into models that make decisions, not just dashboards.
- Custom model development. Bespoke machine learning models for fraud detection, scoring, and recommendation systems, matched to your data and your risk tolerance.
- Predictive analytics & forecasting. Anticipate behavior, from applicant conversion to patient adherence to donor churn, before it happens.
- Data analysis & visualization. Turn siloed, messy operational data into dashboards your team actually opens.
Language is most of your operational data. We make it usable.
- Ask-AI & insight chat. Conversational interfaces built into forms, dashboards, and review workflows, grounded in your own records, not generic answers.
- AI document extraction. Applicants and staff upload paperwork, the platform reads it and auto-fills the record, no retyping.
- Multilingual & medical translation. AI-assisted translation and language review for regulated, multi-market content.
The unglamorous part of AI is what makes it safe to run at scale.
- AI strategy consultation. A roadmap that starts with your operating model, not a vendor's product catalog.
- Automation engine. Conditional branching, scheduled jobs, and trigger-based actions that quietly run your process.
- AI usage & governance. Every AI operation logged with user, model, token count and cost, an auditable ledger, not a black box.
Where does your organization sit with AI?
Answer three quick questions. We'll point you to the right starting point below, no email required.
How would you describe your data today?
Has AI been used in production at your organization?
What's your biggest constraint right now?
A quick pointer in the right direction, not a formal audit.
Advisory Sprint
A focused two to four week engagement to assess your data, use cases, and build a prioritized AI roadmap.
This is not a lab demo, it's what we run
AwardScience™, our own award and grant management platform, runs the same AI stack we build for clients: scoring with written reasoning, meaning-based duplicate detection, and chat grounded in real records. If it's trusted with someone else's judging panel, it will hold up in your operation too.
- AI-generated scores come with written reasoning, not just a number
- Semantic similarity detection catches look-alike submissions keyword search would miss
- RAG-grounded chat stays accurate to your data instead of guessing
Five stages, one accountable team
The same team that scopes the problem is still there when it's running in production. Nothing gets handed off between vendors along the way.
Discover
Understand your operating model, data, and objectives before any technology gets chosen.
Design
Blueprint how AI fits your workflow, including exactly where a human stays in the loop.
Develop
Build and train models against your own data, not a generic public benchmark.
Deploy
Ship into your real environment, integrated with the systems your team already uses.
Monitor
Track performance, drift, and cost continuously, and retrain when the data moves.
Every stage produces something you keep: a roadmap, a working model, or a monitored system, never just a deck.
Governed AI, not just performant AI
Every model we ship inherits the same security discipline as the rest of our engineering, plus an audit trail built specifically for AI.
93%
Source: IDC Report
of organizations have been attacked in the past three years
AI expands your attack surface as fast as it expands your capability. We treat AI security as part of the build, not a review that happens after launch.
ISO 27001 & Secure SDLC
We practice ISO 27001, follow a code review process, and adopt code scanning tools to ensure security, reliability, and maintainability throughout development.
Independent Penetration Testing
An independent security team conducts vulnerability scanning and penetration testing, so the controls we describe are the ones actually holding.
SIEM & Continuous Monitoring
Security Information and Event Management tooling proactively detects, analyzes, and acts on threats in real time.
AI Usage & Cost Ledger
Every AI operation is logged with the user, model, token count, and cost, a transparent ledger for spend reconciliation and audit.
Three ways to start, one team all the way through
Whichever stage you're at, mapping the opportunity or already mid-build, you work with the same people from strategy through to production.
Advisory Sprint
A focused two to four week engagement to assess your data, use cases, and build a prioritized AI roadmap.
- Stakeholder workshops
- Data & readiness audit
- Prioritized roadmap with ROI estimates
Embedded Build Team
Our engineers and data scientists work inside your team to design, train, and ship a production model.
- Dedicated ML & NLP engineers
- Iterative delivery with your stakeholders
- Integrated into your existing systems
Managed AI Operations
Once it's live, we monitor performance, retrain models, and keep the governance ledger current.
- Drift & performance monitoring
- Scheduled retraining
- AI usage & cost reporting
Where our AI is already at work
AI-driven medical translation for clinical trials
AI-assisted translation workflows cut turnaround time on regulatory-grade clinical documentation while keeping medical accuracy intact.
Read more on our healthcare practice Social Impact & NGOSemantic search for Hong Kong's largest NGO directory
Replaced keyword-only search with meaning-based matching for HKCSS, helping residents find the right social service faster.
Read more on our social impact practice Platform · AwardScience™AI-assisted judging at scale
AI scoring with written reasoning and semantic duplicate detection cuts first-pass review time for panels judging thousands of submissions.
Explore AwardScience™The people behind the models
Mike aligns AI investment with client needs. With 14+ years of technology consulting experience, he decides which use cases are worth building and which are worth saying no to.
Sarah Lee
AI Research Lead
With a Master's in Computer Science and over 10 years in AI, Sarah leads our applied research, keeping every model we ship grounded in current, peer-reviewed technique rather than hype.
James Wong
Data Scientist
A data scientist with a background in mathematics and statistics, James builds the predictive models and scoring algorithms behind our client work.
Ready to move your AI initiative from pilot to production?
Tell us what you're trying to automate, predict, or understand. We'll show you the fastest credible path to a system you can actually run.
