Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(20)

Clean Tech(7)

Customer Journey(17)

Design(41)

Solar Industry(7)

User Experience(64)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(10)

Enterprise Solution(28)

Technology Modernization(7)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(37)

Insurtech(65)

Product Innovation(56)

Solutions(21)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(141)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(7)

Computer Vision(8)

Data Science(18)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(47)

Natural Language Processing(14)

expand Menu Filters

“User Tracking Code” for Secure Lead Attribution

There are various insurance aggregators in the industry which frequently compare various insurance plans.

Often, a user might check out the details of a policy from the aggregator, but might not buy it from there.

There is a good chance that he goes to the actual insurance site and buys the policy from there.

In this case, both the aggregator and the insurance company agrees that the Lead has legitimately come from the Aggregator website.N000011en-zanox-Tracking-Process-3760x1400 copy

The question is, how do you understand the same?

This problem of attributing the source of a lead is solved by some third party tools. Both the websites keep a java-script code. So, if a user goes to the insurance detail page of the aggregator website, this executes a code and the third party sets a cookie on the browser.

Now, if the user visits the insurance site to make the transaction, the third party is able to identify that it’s the same user.

The data is shared with both the parties for further processing.analytics_cover

One of our clients, a health insurance major, had some concerns using third party tool. The risks with third party tool are.

  1. Information Risk
  2. Data Analytics
  3. Trust issues.

The data in this case is held by the third party.

So, we at Mantra designed a solution for them which works just like the Third party tool. The advantages this tool provides over the conventional solution are

              1.Data is safe and there is no need to share with third party.
              2.It protects Data Analytics.


Analytics

How does Mantra’s “User Tracking Code” works?

The “User Tracking Code” allows Agencies to report on each of the distinct events without skewing reporting results with Cross Channel Reporting of the same conversion event.

This User Tracking Code helps to track the visitor on their website, so that the right attribution is given without depending on the third party tools. This code becomes their property without risking data and having trust issues.

In other words our user tracking code provides detailed information about the lead customer for business in cost effective way, at more securable way and gives a better sense of potential customer.

Mantra is always looking out to work on innovative solutions to complex business problems and this has been our forte. At Mantra Labs we provide securable and cost effective Solution to your business.

Cancel

Knowledge thats worth delivered in your inbox

Is AI Ready to Replace Your Doctor?

By :

Have you ever wondered what if doctors could harness the power of many experts, all at once? Imagine every heartbeat, every lab result, and every medication being processed in seconds—faster than any human could ever dream of. No, this isn’t science fiction; it’s the new reality of Artificial Intelligence (AI) and Large Language Models (LLMs) in healthcare. The rise of AI in medicine and medical artificial intelligence is transforming the landscape of patient care and research.

Think of AI as the invisible co-pilot in a doctor’s journey—an entity that never sleeps, forgets nothing, and spots patterns that would take years for a human mind to recognize. It’s like giving healthcare professionals superpowers, enabling them to stay ahead of the curve in ways we never thought possible. But the real magic? Smart alert mechanisms jump into action when things are about to go wrong, providing warnings that save lives and make sure the right decisions happen in real-time. This is where AI for medical diagnosis truly shines, enhancing the capabilities of healthcare professionals.

AI and LLMs are changing the way healthcare works—and we’re at the forefront. Here’s how.

AI Pathology: Microscope with Superpowers

What if your microscope could not only analyze slides but also interpret them? That’s exactly what we did for Pathomiq. Our AI-powered pathology tool doesn’t just scan whole slides—it identifies disease progression and predicts patient responses with unmatched precision. By integrating LLMs, we created a system that not only analyzes images but also generates comprehensive, easy-to-understand diagnostic reports.

For Pathomiq, we trained AI models to detect malignancy patterns with 99% accuracy, and the LLMs translated the results into meaningful insights for doctors, which benefitted them with Faster diagnostics, better accuracy, and simpler communication between specialists.

Medical Image Analysis: X-Rays, But Make It Smart

X-rays, MRIs, and other medical imaging can be a treasure trove of data, but they often need an intelligent eye to make sense of it all. Abbvie came to us with this challenge. Our AI models analyze medical images to pinpoint abnormalities, demonstrating the power of AI medical diagnosis.

AI takes care of the image recognition, while LLMs convert findings into plain language summaries. For Abbvie, this resulted in faster image processing and more accurate interpretations. Clearer insights, faster decisions, and a smart system that even non-experts can understand.

AI Health Advisors

Imagine a health advisor that predicts your next treatment before you even need it. Our AI health advisor uses predictive analytics to identify patients likely to undergo surgery, showcasing how AI forecasts patient outcomes. This is similar to the Nura AI health screening concept, where early predictions combined with actionable, easy-to-read insights mean better health outcomes and proactive care.

Intelligent Document Parsing

Medical documents are notorious for their jargon-heavy content. But what if AI and LLMs could automatically extract the relevant information? That’s exactly what we did with our intelligent document parsing tool. Whether research papers or patient reports, our system extracts key data and presents it in a clear, concise format.

AI handles document parsing for faster decision-making. As there wouldn’t be any more sifting through endless documents—It streamlines the process and saves time.

Drug Discovery: Abbvie’s Fast-Track to Innovation

When Abbvie sought to enhance its drug discovery process, we stepped in with an AI-powered platform that redefines speed and accuracy. We developed a research tool that lists genes with their weighted interconnectivity from research papers, providing a visualization framework to display genes and proteins along with their interconnections. Our AI tools handle complex text parsing across various document formats and perform frequency determination and spectral clustering to identify gene pairs, their locations, and contextual details.

Our AI extracts and visualizes gene data, parses text, and determines the frequency and clustering of gene interactions. This approach accelerates drug discovery, cuts costs, and offers a clearer path from genetic research to real-world drug development.

Clinical Trials: Pathomiq’s AI-Powered Cancer Detection

Clinical trials are all about accuracy and speed, especially in cancer detection. For Pathomiq, we built AI models that analyze digital slides to identify early-stage malignancies. Our AI stepped in to explain the findings and suggest the next steps, streamlining the process for researchers and doctors.

AI detects cancer patterns in digital pathology slides and provides context-rich explanations that make trial results easier to understand. Early cancer detection paired with simplified trial documentation means faster, more accurate results.

Conclusion: AI & LLM—The Future of Healthcare, Today

At Mantra Labs, we’re not just integrating AI and LLMs into healthcare; we’re pioneering a revolution. It is said that AI has the potential to reduce diagnostic errors by up to 30% and streamline drug discovery processes by cutting research times in half. It has revolutionized healthcare by delivering faster diagnostics, improving the accuracy of medical imaging, and optimizing processes like pathology and clinical trials. Yet, even with these advancements, the human touch remains essential. Healthcare professionals bring the empathy, intuition, and ethical judgment that AI, for all its precision, cannot replace. While AI enhances decision-making and efficiency, it’s the collaboration between human insight and machine intelligence that ensures the best outcomes. The future of healthcare is not just about smarter technology, but about how human expertise and AI together can provide faster, more precise, and compassionate care.

Further Reading:

Doctor Who? AI takes center stage in American Healthcare

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot