🧬 Life Sciences & MedTech
AI Engineering for Life Sciences & MedTech
Only 22% of life sciences leaders say they’ve successfully scaled AI. The gap isn’t ambition — it’s engineering. Vikgol builds the device software, clinical data platforms, and AI systems that actually reach production in regulated environments.
70+
Senior engineers on demand
72hrs
Working POC guaranteed
NDA
Signed before every engagement
100%
Code & IP ownership to client
Trusted by MedTech & Life Sciences Companies
🩺 IFMedtech
⚕️ IF Med
Documentation-first engineering
Full audit trails & traceability
HL7 / FHIR / DICOM interoperability
Human-in-the-loop by design
Security built in, not bolted on
The 2026 Reality
Enormous Investment. Very Little in Production.
Life sciences and MedTech organisations are investing heavily in AI. Almost none have scaled it. The bottleneck is consistently the same — engineering capacity in a regulated environment.
22%
of life sciences leaders have successfully scaled AI
Deloitte, 2026 Executive Outlook
48%
say digital transformation will substantially impact them in 2026
Deloitte, 2026 Executive Outlook
1,400+
FDA-authorised AI/ML-enabled medical devices — nearly double since 2023
FDA, end of 2025
~12mo
MedTech lags biopharma in AI adoption — a closing window
Deloitte Tech Trends 2026
Industry Challenges
What MedTech & Life Sciences Teams Actually Struggle With
Not theory. These are the recurring engineering bottlenecks that stall AI and product initiatives in regulated life sciences environments.
Data Exists. It’s Unusable.
Device telemetry, imaging, EMR extracts, trial data — all siloed across systems that don’t talk to each other. Most AI projects die here, before a single model is trained.
Interoperability Is Still Broken
HL7, FHIR, DICOM — the standards exist, but real-world implementations diverge wildly. Every hospital integration becomes a bespoke engineering project.
Regulatory Documentation Is Manual
Technical files, design history, risk assessments, submission packages — teams of specialists spending months on documentation that AI could draft in days with the right human oversight.
AI Security Is an Afterthought
For MedTech specifically, model manipulation can lead to device failure and patient harm. Security has to be embedded from the first architecture decision — not audited in later.
No Engineers Who Understand Both
Engineers who know modern AI are plentiful. Engineers who know AI AND understand regulated environments, traceability, and validation requirements are extremely rare — and expensive to hire.
Pilots That Never Graduate
A demo that works on clean data means nothing. Most life sciences AI pilots collapse when they meet real-world data variance, audit requirements, and clinical workflow constraints.
Vikgol Solutions
What We Build for Life Sciences & MedTech
Production systems, not proofs of concept that go nowhere. Every engagement starts with your real data, your real constraints, and your real regulatory obligations.
Medical Device Software
Companion apps, device configuration tools, firmware interfaces, and cloud connectivity layers for connected medical devices. Built with traceability and version control designed for technical file requirements.
Interoperability Engineering
HL7 v2, FHIR R4, and DICOM integration layers that connect your product to hospital systems, EMRs, and PACS. We handle the messy real-world variance that standards documents don’t cover.
Clinical & Device Data Platforms
Unify device telemetry, imaging, and clinical data into a governed lakehouse architecture. Turn siloed, unusable data into an AI-ready foundation with full lineage and audit trails.
Regulatory Documentation AI
LLM systems that draft and cross-reference technical documentation, literature reviews, and submission sections from your own source material. Always human-reviewed — AI accelerates specialists, it doesn’t replace them.
AI for Imaging & Signal Data
Computer vision and signal processing pipelines for diagnostic imaging, pathology, and sensor data — engineered for the throughput, latency, and explainability that clinical settings demand.
Dedicated Engineering Pods
A senior engineering team embedded in your product organisation — full stack, AI, DevOps, or QA. Available in 72 hours, scaled up or down as your roadmap demands. No hiring cycle, no bench cost.
Who We Work With
Across the Life Sciences Value Chain
Different segments, same underlying need — engineering capacity that understands regulated environments.
🩺
MedTech & Devices
Connected device software, companion apps, telemetry pipelines, and hospital system integration.
🔬
Diagnostics & Labs
LIMS integrations, result delivery platforms, imaging AI, and lab workflow automation.
💊
Pharma & Biotech
Research data platforms, regulatory documentation AI, and clinical operations tooling.
📱
Digital Health
Patient-facing platforms, remote monitoring, care coordination, and health data infrastructure.
How We Work
Built for Regulated Environments
Our engagement model is designed around how life sciences teams actually need to buy and validate engineering work.
Step 01
NDA First
Before we discuss anything technical, we sign a mutual NDA. Your device design, clinical data, and product roadmap stay protected from the first conversation.
Step 02
Scoped Discovery
We map your existing systems, data sources, regulatory constraints, and the specific bottleneck you need solved. No generic proposals — the scope reflects your actual environment.
Step 03
72-Hour Working POC
A working prototype on your real data — not a slide deck, not a mockup. You validate the approach before committing budget to a full build.
Step 04
Build & Full Handover
Production build with documentation, test coverage, and traceability your quality team can work with. 100% code and IP ownership transfers to you. No lock-in.
Technology Stack
Chosen for Reliability and Traceability
In regulated environments, “it works” isn’t enough — it has to be documented, versioned, and auditable.
🔗
Interop
FHIR / HL7 / DICOM
EMR, PACS, and hospital system integration
🤖
AI / ML
PyTorch + LangChain
Imaging models, RAG, LLM pipelines
📱
Device Apps
Flutter / React Native
Companion apps, BLE connectivity
⚡
Backend
Python / Node.js
API-first, versioned, documented
🗄️
Data
Databricks / Delta Lake
Governed lakehouse, full lineage
☁️
Cloud
AWS
Data residency, DR/BCP, audit logging
🔐
Security
VAPT + SAST/DAST
Security testing built into CI/CD
📋
Quality
Git + Test Coverage
Traceable commits, documented releases
FAQ
Common Questions from Life Sciences Teams
Is Vikgol a regulatory consultancy?
No — and we’re clear about that. We are an engineering partner. We build software and AI systems with the documentation, traceability, and version control practices that regulated environments require, and we work alongside your regulatory and quality teams. Regulatory strategy, submissions, and formal validation remain with your specialists or your regulatory consultant. We make their job easier; we don’t replace them.
How do you handle sensitive clinical or patient data?
Our default position is that we work with synthetic, anonymised, or de-identified data wherever the engineering problem allows it — which is most of the time. Where real data access is genuinely necessary, we work within your infrastructure and your access controls, under a signed NDA and data processing agreement. We do not move client clinical data onto our own systems.
Do your engineers have life sciences experience?
Our engineers bring deep technical capability — AI/ML, interoperability standards, cloud architecture, mobile and device connectivity — and we’ve delivered for MedTech clients. What we don’t claim is clinical or regulatory expertise; that comes from your side. The most effective engagements pair our engineering depth with your domain knowledge, and we structure discovery specifically to transfer that context early.
What does the 72-hour POC actually include?
A working, running prototype that addresses one specific problem — a data pipeline that ingests and normalises your device telemetry, an interoperability layer that speaks to a test EMR, or an AI system that drafts a documentation section from your source material. It runs on real or realistic data, not a mockup. The goal is to let you judge our engineering on evidence rather than a proposal.
Can you work with our existing engineering team rather than replacing it?
That’s the most common engagement. Many life sciences and MedTech companies have a capable core team that’s simply capacity-constrained or missing one specific skill — AI/ML, DevOps, or mobile. We embed senior engineers into your existing structure, working to your standards and processes. Dedicated pods are available in 72 hours and scale up or down without a hiring cycle.
Who owns the code and IP?
You do — 100%, with no exceptions and no lock-in. All code, documentation, models, and intellectual property transfer to you on completion. This is a standard term in every Vikgol engagement, not something negotiated separately.
Your AI Pilot Is Stuck. Let’s Fix the Engineering.
Book a free 30-minute call with our engineering team. NDA first, no pitch deck — just a direct conversation about what’s blocking your product.
Available Now · 72-Hour POC
Ready to Ship Your AI Product?
Let’s Build It Together.
Senior engineers on demand. Working prototype in 72 hours. NDA before we discuss anything. 100% code ownership to you — no lock-in, ever.
70+
Senior engineers on staff
90+
Projects delivered globally
72h
Working POC guaranteed
5★
Client satisfaction rating