AI Integration

AI Delivery

End-to-end AI delivery. No orphaned proofs of concept.

Roughly 87% of machine-learning models never reach production. The work here is specifically about the ones that do — built with the data readiness, MLOps, and governance it takes to survive the handover.

Review recent work

The problem

Scattered data. No in-house ML. POCs that impress but don't ship. Fear of black-box vendors.

These are the four patterns that consume most AI budgets. My approach is to name them out loud at the start of an engagement, then do the unglamorous work — data readiness, deployment, monitoring — that actually addresses them.

Capabilities

AI solutions we build.

Large Language Models

Custom LLM work — from local Ollama deployments to cloud APIs. Document analysis, natural-language querying of internal data, conversational interfaces on top of existing systems.

— LLMs— RAG— NLP

Computer Vision

Quality inspection, document digitisation, and automated visual analysis. Deployed on-prem or at the edge where data can't leave the building.

— CV— OCR— Edge

Predictive Analytics

Bayesian inference, MCMC, neural networks, GLMs. Models built to survive audit and regulatory scrutiny, not just a demo.

— ML— Bayesian— GLM

MLOps & Deployment

Full lifecycle — training, versioning, deployment, monitoring, scaling. On AWS, GCP, Azure, or on-prem if required.

— MLOps— Cloud— On-prem

Data Pipelines & ETL

Data engineering on Databricks, Snowflake, and custom stacks. The unglamorous work that decides whether any of the above succeeds.

— Databricks— Snowflake— ETL

Voice AI & Speech

Custom voice agents, real-time transcription, and conversational speech synthesis. Deployed securely with low latency for customer service and accessibility.

— ASR— TTS— Voice Agents

Process

From zero to AI in five phases.

PHASE 01

Discovery

Assess business needs, data maturity, and where AI creates real value. No jargon, clear opportunities.

PHASE 02

Data Readiness

Audit, clean, and structure data. Build pipelines that feed models reliably; modernise platforms if needed.

PHASE 03

Build & Train

Custom models trained on client data. LLMs, NLP, predictive — whatever solves the specific problem.

PHASE 04

Deploy

Production deployment with proper MLOps. Integration into existing systems via APIs, dashboards, or UIs.

PHASE 05

Monitor & Scale

Ongoing monitoring, drift detection, and performance optimisation. Scale as needs grow.

Portfolio

AI projects delivered.

2024

Natural Language Query Interface

Users query PostgreSQL databases in plain English; LLMs translate questions to SQL and return grounded answers.

— OpenAI— LangChain

2023

RAG for Nuclear Procedures

Semantic search across NRC Technical Letters using Retrieval-Augmented Generation. Retrieval quality engineered, not hoped for.

— RAG— Vector DB

2024

LLM Sentiment Alpha

Real-time sentiment analysis on financial news using LLMs to generate predictive trading signals.

— LLM— Finance

2022

Change Point Detection

Bayesian + MCMC pipeline detecting maintained cost changes in large healthcare datasets.

— Bayesian— MCMC

2022

Hospital Readmission Prediction

GLM-based pipeline predicting readmissions; used in investigations into medical complications.

— GLM— Healthcare

2023

Local LLM Execution Platform

On-prem LLM execution for FinTech and InsurTech proof of concept and proposal. Post IPO stock analysis and predictions.

— Ollama— On-prem

Technology

A short list of tools used daily. The judgement about when to use which is the actual work.

Languages

Python
SQL
R
C++

Scikit-learn
PyTorch
TensorFlow
XGBoost

LLM

OpenAI
Ollama
LangChain
Vector DBs

Data

Snowflake
Databricks
PostgreSQL
Kafka

Cloud

AWS
GCP
Azure
On-prem GPU

MLOps

Docker
MLflow
Airflow
Monitoring

Get started

A thirty-minute readiness assessment, no deck required.

We evaluate your data, identify the highest-value AI opportunities, and outline a clear implementation roadmap. No obligations.

+971 55 176 3478