But behind that report lies something much larger:

• whole genome sequencing pipelines,

• clinical interpretation systems,

• laboratory infrastructure,

• AI-assisted variant analysis,

• and the challenge of transforming complex genomic data into something humans can actually understand.

At Ebovir, this is the problem space we work in every day.

From Research Labs to Precision Medicine

Ebovir was incubated from McGill University laboratories and later admitted into the CQIB biotechnology incubator in Quebec.

Our mission is simple:

Build technologies that make precision medicine more accessible, scalable, and clinically useful.

Today, our work spans across:

• AI-powered genomic interpretation

• Whole genome sequencing (WGS)

• Diagnostic services

• Cell therapy research

• Exosome-based biotechnology

• Medical aesthetic biotechnology products

Why Genetic Interpretation Is Still Difficult

Modern sequencing technologies can generate enormous amounts of genomic data.

The real challenge is interpretation.

A single genome can produce:

• thousands of variants,

• hundreds of reports,

• and thousands of pages of medical information.

Most patients — and even many clinicians — do not have the time to manually connect:

• research papers,

• variant databases,

• clinical significance,

• and personalized risk analysis.

This is where AI becomes important.

Building AI-Assisted Genomic Interpretation

At Ebovir, we are building systems that combine:

• sequencing pipelines,

• mutation databases,

• literature-based knowledge graphs,

• and AI-powered interpretation systems.

Our workflow includes:

1. Sequencing data processing

2. AI-assisted mutation detection

3. Mutation interpretation

4. Knowledge database integration

5. Interactive report generation

The goal is not just generating reports.

The goal is helping people understand what their genomic information actually means.

Building Canada’s AI-Driven Genomic Infrastructure

One of the areas we are particularly focused on is AI-assisted whole genome sequencing interpretation.

Our platform integrates:

• large-scale genome databases,

• scientific literature,

• proprietary prediction models,

• and continuously updated medical knowledge.

Some of the systems we are actively exploring include:

• AI-based mutation interpretation

• knowledge graph systems

• genomic literature retrieval

• clinical summarization pipelines

• interactive patient-facing AI systems

As AI engineers, one thing became clear very quickly:

Medical AI is not only about models. It is about reliability, traceability, safety, and clinical usability.

That realization heavily shaped the architecture we are building today.

Beyond Software: Real Diagnostic Infrastructure

Unlike many AI startups that only build software layers, Ebovir also operates diagnostic laboratory infrastructure in Canada, including Biosafety Level II and III laboratories.

This matters because real-world clinical AI requires:

• validated workflows,

• reliable biological data,

• laboratory integration,

• and continuous collaboration between engineering and clinical domains.

Research Areas We Are Exploring

Our broader research and development efforts include:

AI + RNA Drug Development

Using AI-assisted RNA candidate generation and lung-targeted delivery systems for antiviral research.

Whole Genome Sequencing

Building interpretation pipelines for large-scale genomic reports and personalized health insights.

Early Cancer Screening

Exploring cfDNA and ddPCR-based screening systems for early detection workflows.

Precision Medicine Platforms

Creating AI systems capable of assisting both clinicians and patients with complex medical interpretation.

The Engineering Challenge Behind Medical AI

One thing we learned while building this system:

Medical AI products are fundamentally infrastructure problems.

You are not simply generating text.

You are handling:

• scientific uncertainty,

• patient-sensitive information,

• continuously evolving research,

• compliance constraints,

• and clinical reliability requirements.

This is why our engineering efforts focus heavily on:

• scalable cloud architecture,

• structured medical data pipelines,

• retrieval systems,

• validation workflows,

• and explainable AI interactions.

What Comes Next

This blog will document our journey building AI systems for precision medicine.

We plan to share:

• RAG architecture lessons

• genomic AI workflows

• medical AI reliability challenges

• cloud infrastructure decisions

• compliance considerations

• and practical engineering experiences from deploying real-world healthcare AI systems.

If you are interested in:

• AI for healthcare,

• genomics,

• medical infrastructure,

• or precision medicine engineering,

you are in the right place.

Welcome to the journey.

We are building safe technology for your genetic journey.
Proudly introduce about our products here https://ebovir.ca/

They have complex medical terminology, long explanations, and inconsistent formatting.

For that reason, we started exploring how Retrieval Augmented Generation(RAG) could help users better understand their reports while still keeping the system reliable and clinically cautious.

Why Simple AI Summarization Was Not Enough

At first, using a large language model to summarize reports seemed straightforward. But in practice, we quickly discovered several problems.

• Miss important medical context

• Long PDF reports were difficult to process consistently

Medical AI systems cannot behave like generic chat-bots. Small mistakes in interpretation can create confusion for users.

With this context, we chose to use a more structured RAG-based approach.'

Only Early RAG Pipeline

Our current workflow focuses on:

1. Extracting report content

2. Splitting documents into searchable chunks

3. Retrieving relevant medical context

4. Generating assistive explanations using AI models

Instead of replacing medical professionals, the goal is to help users navigate complicated information more clearly.

Technical Challenges We Encountered

Some of the biggest engineering challenges included

• Inconsistent Document Structures

Different clinics and labs format reports differently, making reliable parsing difficult.

• Retrieval Noise

Even small retrieval mistakes could lead to irrelevant or incomplete explanations.

• Medical Terminology

Genetic terminology is highly specialized.

• Privacy and Compliance ⭐

Because health data is sensitive, infrastructure decisions also needed to consider compliance requirements as HIPPA, PIPEDA, and Quebec Law 25.

One of the biggest lessons so far is that healthcare AI systems require much more than simply connecting a language model to a database.

Reliability, retrieval quality, explain-ability, and careful wording matter just as much as model performance.

We are continuing to improve :

• retrieval accuracy

• structured medical context handling

• multilingual support

• FHIR-based interoperability

• overvaluation and monitoring workflows

This is only the beginning of our journey, and we plan to share more lessons as Ebovir continues building reliable AI systems for healthcare.