Allen Huang | AI Engineer & Fractional AI Lead

What I do

Services

Four ways I help teams put AI to work — pick the one that fits where you are.

01

AI Integration & LLM Engineering

Embed LLMs into your existing product or workflow — conversational interfaces, document processing, automated decision support. Built for production: typed, tested, observable.

— Conversational AI & chatbots
— Document generation & extraction
— Agent workflows & tool use

02

AI-Powered Data Pipelines

Bring AI into your data modelling cycle — intelligent review, automated testing, anomaly detection. Turn repetitive data work into automated workflows your team can trust.

— AI-assisted dbt modelling
— Automated data quality review
— LLM-powered analysis

03

Specialist

Healthcare AI

Clinical transcription pipelines, structured clinical data extraction, insurance billing automation. Experience with US patient data, encounter records, CCM/RPM billing workflows, and HIPAA-adjacent systems.

— Call transcription & summarisation
— Clinical NLP & structured extraction
— Billing code automation

04

Fractional AI Lead

Embed me in your team on a retainer — I help set AI strategy, evaluate tools and vendors, build internal capability, and ship incrementally. No full-time hire needed.

— AI roadmap & tool selection
— Hands-on implementation support
— Team enablement & documentation

Selected work

Production systems I've built

Client names withheld for confidentiality — happy to talk specifics on a call.

Clinical AI Pipeline

US Healthcare Platform

Designed and built a production AI pipeline for a care-management platform that turns recorded patient calls into structured clinical insight: automated transcription, LLM-based extraction of clinical and social-need signals, and a clinician review step to verify results before they're used. Engineered for real-world reliability — asynchronous processing, graceful failure handling, and end-to-end monitoring.

Healthcare LLM NLP Speech-to-Text Python

Revenue Recovery & BI

US Healthcare Startup

Built the organisation's BI function from scratch: hired the team, implemented the full analytics stack (Snowflake, Fivetran, dbt, Sigma Computing), and automated KPI tracking across 10+ departments. Developed insurance billing dashboards surfacing unbilled, underpaid, and denied claims — recovering tens of millions of dollars in revenue. Also delivered near real-time P&L tracking for the finance department.

Healthcare BI Snowflake dbt Revenue Cycle

AI Cost & Observability

AI-Native SaaS

Built a token-usage and cost-tracking model for a company running large-scale, multi-provider LLM infrastructure. The hard part was correctness: normalising inconsistent billing data across providers and accounting for prompt-caching properly — fixing the cache accounting alone corrected a roughly 30% overstatement in one provider's costs. Delivered a finance-grade cost model with per-call attribution, versioned pricing, and automated validation tests.

AI Observability dbt BigQuery FinOps

ML at Scale

Major Financial Asset Manager

As Technical Lead at one of Africa's largest asset managers, built and led a data science team applying ML to improve business processes. Delivered a clustering-based behavioural segmentation toolkit used by business development managers to personalise advisor relationships, and a neural network-based workflow automation system. Operated at institutional scale across a five-year engagement.

Machine Learning Data Science Financial Services Python

How it works

A simple way to start

01
Discovery

A 30–45 min call to map your AI goals, existing data, and constraints.
02
Proposal

Scope, milestones, and success metrics. Fixed-price or time & materials.
03
Build & Enable

Ship iteratively, document thoroughly, enable your team to own it.

Tools

Tech stack

AI & LLMs

Claude (Anthropic) Gemini GPT-4o AssemblyAI LangChain

Data

SQL Python dbt Snowflake BigQuery Fivetran AWS GCP

Engineering

Django Celery ReactJS Looker Metabase

About

A builder, not just an advisor

I'm Allen. I've spent years building data platforms and pipelines for startups — and I've moved into where data meets AI: LLM integrations, clinical pipelines, and the infrastructure that makes AI production-ready rather than demo-ready.

My background in AI goes back further than the current hype cycle. I hold an MSc in Computer Science (with distinction) from the University of Cape Town, where my research focused on neuro-evolution for collective autonomous systems. I've published at IEEE CEC and AAMAS, lectured Machine Learning at university level, and spent five years leading a data science team at a major financial institution before going independent.

I've also built and exited my own business — so I understand what founders are actually dealing with.

I'm based in Portugal, work remotely across EU and US time zones, and keep my client list small so I stay hands-on.

I build AI that ships — not slide decks.

Most AI projects fail — not because of the model, but because the data is messy, the integration is shallow, or no one can see both layers at once.