About Involabs
Involabs is a data and applied AI engineering firm. We take organizations from digitally enabled to AI-ready and AI-operational: fragmented, poorly governed data turned into production-grade foundations, and AI systems that run reliably in real business environments.
Who we are
Our work spans data engineering, analytics modernization and applied AI, with a focus on systems that are safe, observable and built to scale. Most of our clients arrive needing one layer and discover they need the one beneath it, so we build all three: the data foundation, the intelligence on top, and the operations that keep both trustworthy.
Our position is simple. An AI system is finished when someone who did not build it can run it, measure it and explain it to an auditor. Everything before that is a prototype.
We work with healthcare, life sciences and financial services organizations in the United States, Saudi Arabia, the United Arab Emirates and beyond, and we take work outside those sectors when the problem suits us. Our team is based in Pakistan.
We also say no. If an assessment shows the problem is a process problem, or that a simpler system would do, that is what the report will say.
What we hold ourselves to
We build AI that runs in the real world: monitored, maintainable and handed over with documentation. Not notebooks and demos.
Reliable AI starts with clean, governed, well-modelled data. We do not skip this step, even when a client would like us to.
Evaluation sets before the build, regression runs on every change, tracing on every call. If we cannot measure a claim, we do not make it.
Systems that are observable and explainable, a known-gaps register the client reads, and a clear recommendation when a simpler solution would serve better.
Team
Both founders are practitioners, and at least one stays on every account for its duration.
Co-founder and Chief Executive Officer
More than ten years in enterprise business intelligence, data engineering, analytics strategy and data governance. Has built analytics capability for organizations across fintech, retail, healthcare and telecommunications, turning reporting functions into decision systems that executives use.
Co-founder and Chief Technology Officer
More than eight years in data science and AI product development. Has built and scaled machine learning and generative AI systems for companies across retail, healthcare, marketing and finance, and leads the evaluation, observability and delivery standards Involabs engagements run on.
Team
Research, platform and delivery depth behind the team.
Chief AI and Data Scientist
More than fifteen years in data science and AI, a PhD from KTH Royal Institute of Technology in Sweden, and leadership roles in multinational companies. Has driven work on forecasting platforms, generative AI and analytics tools, with more than 30 published articles focused on optimization problems.
Senior Data Consultant
More than five years in data engineering and MLOps with multinational companies, covering data strategy, ETL pipeline design, and data lakes and warehouses integrated with AI engines.
Project Manager and Business Analyst
More than seven years in product and project delivery for B2B SaaS in healthcare IT and HR tech, including care management, EMR and payroll platforms. Has shipped HIPAA-compliant products, FHIR R4 and HL7 integrations, and LLM-powered features, leading cross-functional teams of up to 15.
Team
Senior engineers who design and lead delivery.
AI Solution Architect
More than seven years in AI development and data science, covering machine learning, deep learning, DevOps and MLOps, with deep experience in time-series forecasting, computer vision and predictive modeling.
Solution Architect
More than seven years in front-end engineering with React and Vue, covering responsive web applications, state management, API integration and performance, working in agile delivery teams.
Clients
Several healthcare and pharmaceutical engagements are covered by confidentiality agreements and are not shown.
Send us the problem and the constraints. You get a written architecture, a risk register and a costed roadmap, and you keep them whatever you decide next.