Book a 30-minute call with a product lead. We'll figure out what's possible.
Production-grade AI systems, ML models, and data infrastructure - built to ship, not to sit in a prototype folder.
We built and deployed an AI model that classifies diabetic retinopathy from retinal images, helping clinicians screen patients faster and flag at-risk cases earlier.
Forecasting models for demand, churn, risk, and behaviour — trained on your data, tuned for your context.
Image recognition, document understanding, and language models that turn unstructured data into structured signal.
Models deployed with monitoring, evaluation, and retraining pipelines — built to perform in real environments, not just on a research notebook.
Forecasting models for demand, churn, risk, and behaviour — trained on your data, tuned for your context.
Image recognition, document understanding, and language models that turn unstructured data into structured signal.
Models deployed with monitoring, evaluation, and retraining pipelines — built to perform in real environments, not just on a research notebook.
ETL and ELT pipelines that move, transform, and validate data across systems — engineered for reliability, not just speed.
Dashboards, real-time reporting, and decision-grade insights built on top of your data — not generic BI templates.
Modern data lakes and warehouses (Snowflake, BigQuery, Redshift, Postgres) designed to handle scale, complexity, and AI workloads.
ETL and ELT pipelines that move, transform, and validate data across systems — engineered for reliability, not just speed.
Dashboards, real-time reporting, and decision-grade insights built on top of your data — not generic BI templates.
Modern data lakes and warehouses (Snowflake, BigQuery, Redshift, Postgres) designed to handle scale, complexity, and AI workloads.
A structured review of your data, systems, team, and goals to identify what's actually buildable and what isn't yet ready.
4–8 week prototypes that validate technical feasibility and business case before committing to full development.
From adoption planning to governance and responsible deployment — we help embed AI in ways your team can actually maintain.
A structured review of your data, systems, team, and goals to identify what's actually buildable and what isn't yet ready.
4–8 week prototypes that validate technical feasibility and business case before committing to full development.
From adoption planning to governance and responsible deployment — we help embed AI in ways your team can actually maintain.
From job post to job-ready.
MazikCare unifies healthcare operations from Day 1, saving time, reducing costs, and letting providers focus on patients.
Production-grade machine learning (predictive models, computer vision, NLP), data engineering (ETL/ELT pipelines, warehouses, analytics at scale), and AI consultancy — from readiness assessment through deployed, monitored models.
Readiness assessments complete in 2–4 weeks; a proof of concept runs 4–8 weeks; full implementations from PoC to deployed system typically finish within our standard four-month transformation cadence.
Consulting answers what's buildable and what's worth building — readiness assessment, PoC, and strategy. Development builds and ships it: production ML with monitoring, evaluation, and retraining pipelines.
Modern ML tooling (PyTorch, TensorFlow, scikit-learn), cloud-native data platforms (AWS, GCP, Azure; Snowflake, BigQuery, Redshift, Postgres), orchestration (Airflow, dbt), and LLM stacks (LangChain, vector databases).
It depends on data readiness and scope. We start with a readiness assessment and a fixed-scope PoC so you validate technical feasibility and business case before committing to full development spend.
Our AI Readiness Assessment reviews your data, systems, team, and goals to identify what's actually buildable today and what needs groundwork first — so you invest where it pays off.