ArsenalTech Pvt. Ltd.
Menu

AI that understands every patient, individually

Create precision treatment plans backed by data, not guesswork

Share Your Concept
  • 80+
    In-house
    Experts
  • 5+
    Team’s Average
    Years of Experience
  • 93%
    Employee
    Retention Rate
  • 100%
    Project Completion
    Ratio
Our solutions

What we design for you

AI-based clinical decision support

We build systems that analyze EHRs, past treatments, and outcomes to recommend the best next-step therapies for each patient’s profile.

Genetic & biomarker integration engines

We integrate genomic data with clinical workflows to support tailored treatment decisions for chronic illnesses, cancer care, and rare diseases.

Lifestyle & behavioral data models

We leverage patient-reported data and wearable input to adjust treatment suggestions—ensuring they align with actual habits, not just clinical theories.

Dynamic treatment adjustments

Our models continuously learn and adjust based on real-time patient feedback, treatment progress, and side effects—ensuring ongoing personalization.

Personalized treatment plans, tailored for every patient

AI-powered insights to design care pathways unique to each individual’s medical history, genetics, and lifestyle.

  • Tech Stack Language

    Powerful coding foundations that drive scalable solutions.

    javascript-logo-svgrepo-com

    JavaScript

    Python

    Python

    Java

    JAVA

    R

    R

  • Framework Extensions

    Enhanced tools and add-ons to accelerate development.

    Vue

    Vue Js

    React

    React

    Angular

    Angular

    TensorFlow

    TensorFlow

  • Cloud Services

    Secure, flexible, and future-ready infrastructure in the cloud.

    AWS

    AWS (Amazon Web Services)

    Azure

    Microsoft Azure

    Google Cloud

    Google Cloud Platform (GCP)

    power-bi-icon

    Power BI

  • Interactive Data Tools

    Smart insights and visualization that bring data to life.

    tableau-software

    Tableau

Tech talk

Developer tips & insights

Trending Blog thumbnail_RAG
4 Min read 10 September, 2025

Introduction to RAG (Retrieval-Augmented Generation) for SaaS Applications

Trending Blog thumbnail_Multitenant SaaS
4 Min read 10 September, 2025

Building Multi-Tenant SaaS Applications with Laravel: A practical guide for scaling globally

Trending Blog thumbnail_Filament Vs Laravel Nova
2 Min read 10 September, 2025

Filament vs Laravel Nova: CTO Guide

Personalized treatment plans for every patient

Precision care with AI insights

Our solutions enable healthcare providers to design personalized treatment plans based on patient data, medical history, and predictive analytics. By leveraging AI and machine learning, we deliver insights that help clinicians optimize therapies, improve outcomes, and reduce risks. Each treatment plan is tailored to individual patients, ensuring a data-driven, effective, and patient-centric approach to healthcare.

Architect an AI system that reads EHR, labs, and imaging by using a modular, event‑driven design: an ingestion layer (HL7/FHIR listeners, PACS connectors), a normalization layer (mapping to a unified clinical schema), a feature service, and an online inference API that returns ranked treatment options with explanations to EHR‑embedded UI widgets in near real time.​
For treatment‑effect prediction, start with tree‑based ensembles (gradient boosting, random forests) for tabular clinical data and add deep learning where needed: CNNs for imaging, transformers for notes, and specialized uplift/CATE models or meta‑learners to estimate heterogeneous treatment response at the patient level.
Design the data pipeline with separate batch and online paths: ETL from EHR/claims into a lakehouse, normalization and de‑identification, feature engineering into a feature store, offline training/evaluation, and online model serving backed by the same feature definitions for low‑latency inference.
Expose AI suggestions inside EHRs by wrapping inference as a FHIR‑aware service, then integrating via SMART‑on‑FHIR apps or CDS Hooks: the EHR sends patient/context (e.g., order‑select, encounter‑start), your service returns cards with recommended treatments, risks, and links to a deeper SMART app view.​​​
For PHI‑processing microservices in the cloud, enforce strong identity and access management, mutual TLS, encryption in transit and at rest, network isolation, strict logging and audit trails, data minimization, and regional residency; add key management (HSM/KMS), DLP, and role‑based access to meet HIPAA‑class requirements.
A common data model: Patient (demographics, identifiers), Encounter (visit context), Condition, Procedure, MedicationStatement/Request, Observation (labs, vitals), ImagingStudy, and Outcome (response, adverse events), all linked by patient and encounter IDs; a longitudinal layer or “patient timeline” view aggregates these into time‑ordered events used for both analytics and sequence‑aware modeling.

Every patient is unique, every plan adapts

Enable hyper-personalized care using continuous monitoring, predictive modeling, and dynamic treatment recommendations so patients get exactly what they need, when they need it.

See personalized AI plans