We build bespoke clinical software for hospitals and medical networks — on a platform refined over years, by senior engineers who know that adoption, not features, is the hardest problem in medical IT.
AI tools have made code generation trivial. But clinical environments are unforgiving: they demand security, confidentiality, and reliability — and they involve users who cannot afford to fight their tools. Most vendors ship code fast. Very few deliver software that doctors will actually trust and use.
Off-the-shelf AI, packaged for the masses — fast to spin up, impossible to trust in a clinical setting.
Software designed around clinical workflows, regulatory constraints, and the reality of clinician adoption.
Developers who generate code with AI prompts — even if what they ship has never been tested against real clinical data.
We know what makes or breaks a medical product. Security, confidentiality, auditability, and adoption are our starting point.
Long projects, heavy teams, fixed pipelines — and the bill that follows.
A proven platform means we never rewrite the foundation. Every hour goes into what is specific to your hospital.
We have spent decades building software for real markets. That experience means we understand what makes or breaks a medical product — and these are not problems we discover mid-project. They are our starting point.
End-to-end protection built into every layer — not bolted on after launch.
PHI-aware architecture from day one. Data classification, de-identification, and audit trails are native to the platform.
Every action logged, every export watermarked, every data pipeline tamper-evident.
Clinicians adopt tools that feel right — not just tools that work. We design for trust.
The #1 reason medical software fails is not technology — it is adoption. We build for the people who will use it.
Every Iterlane application runs on a common platform we have refined over years. Because the foundation is never rewritten, we guarantee both proven robustness and a development speed no from-scratch approach can match.
Built into the platform layer. Every app inherits the same foundations.
PWA — fully compatible across iOS, Android, tablet, desktop.
New functionality tested before production. See changes before they ship.
Built-in AI inside every application — natural language queries on data.
Foundation is proven. All effort goes into client-specific features.
Experience and platform together produce the result that matters: the speed and robustness to deliver true custom-built software — shaped to each client's needs, not forced into a template. Delivered through the iterative methodology that is our signature.
Senior engineers ship working software in tight increments.
Every cycle ends with something real in your hands.
Real screens generate real reactions. We measure and listen.
We fold learnings into the next increment — add, remove, refine.
No template. No generic product. True bespoke software — at platform speed.
Co-founder & Chief Technology Officer
Co-founder & Chief Revenue Officer
A white-label platform that transforms informal medical communities — often running on WhatsApp — into structured, secure, and scalable digital environments. Each deployment is fully branded per client.
Medical societies and clinical networks rely on WhatsApp and email to share high-stakes knowledge. Discussions are fragmented, unsearchable, and ungoverned. Case data is mixed with personal messages. No audit trail, no structure, no scalability.
We mapped how clinicians actually communicate, what they'd never give up, and where AI could quietly remove friction. Then we built it: AI-assisted case posting, semantic search, structured discussion, e-learning, expert messaging — in a privacy-first architecture.
Hospitals without a Hospital Information System track activity on paper or Excel. DataPal ingests raw operational data, validates it clinically using AI, and serves interactive dashboards — no BI tool expertise required.
No visibility on OR utilisation, surgeon efficiency, or waiting lists. Data sits in spreadsheets. Decisions are based on intuition. When clinical data quality is unchecked, bad data silently distorts averages and undermines trust.
AI learns clinically valid ranges for each procedure, a human approves them, and every imported row is validated automatically. Implausible data is blocked. Natural language queries generate interactive dashboards. Multi-site, multi-service — one platform for the entire hospital network.
Start there. We'll figure out the software together.