NexusMed AI: Advanced Multi-Agent Clinical Research Platform - 15/08/2026 05:13 EDT

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@pkundu25

Hi there, I am a Senior AI Developer specializing in LangGraph, Python and agentic RAG architectures. I have extensive experience building multi-agent system for document segmentation using LangGraph and AI agent for Risk Mitigation using Django and deploying local SLMs. Technical Execution Plan 1. Deterministic Medical Retrieval: I will build reliable Deno modules for the PubMed Entrez API using medical MeSH terms, alongside a proxy-resilient Google Scholar extraction layer. 2. Bulletproof RAG & Citations: The retrieval node will enforce strict metadata extraction. I will inject structured inline anchor tags into the LLM context, which your React/Tailwind frontend will render as interactive, clickable source tooltips. 3. Low-Latency WebSocket Streaming: I will optimize the Deno WebSocket layer to multiplex real-time packets (thought_stream, metadata_stream, synthesis_stream) into your Framer Motion panels without UI layout shifts. 4. Biomedical Reflection Loop: I will program the local Ollama SLM as a strict compliance gate in LangGraph, evaluating hallucinations, medical contradictions, and dosing safety before approving the frontend stream. I write clean, modular, and heavily commented TypeScript/Python code that prioritizes data sensitivity and medical accuracy. Let's connect to discuss it further. I am ready to start the project immediately.

@shadabkhan92

With our solid background in AI and machine learning, our team at SM Software is able to go beyond just creating prototypes - we develop production infrastructure. We have a unique approach where we pair agentic AI with a range of different technologies to ensure successful integration into existing workflows. Our expertise stretches across all stages of production, from designing and manufacturing IoT hardware to implementing Odoo ERP end-to-end. We're also incredibly comfortable with deploying on various cloud platforms such as AWS, GCP, and Azure. In addition, our experience with an extensive set of tools is extremely well-suited to the specialized Literature Review Agent you're looking to enhance. Our familiarity with APIs like Entrez and Google Scholar sets us up as the ideal partners for your project, as does our skills in crafting a robust backend using Deno v2 (TypeScript) and effective use of WebSockets. Moreover, we understand how crucial data sensitivity is within the medical domain. With deep respect for this sensitivity, rest assured we will prioritize security and privacy every step of the way in this project. Let's leverage our strengths in integrating AI agents into challenging contexts to take your project to new heights with NexusMed AI.

@Prutheus

A Deno backend with LangGraph orchestration is a deliberate, modern choice, and for a clinical research platform the interesting problem is not agent orchestration — it is that a multi-agent system produces confident output with no obvious way to tell whether it is right. In clinical research that is the whole risk. So the work I would prioritise: - Grounding and citation as a hard requirement. Every claim an agent produces should carry a traceable source. An unsourced assertion in a clinical research tool is worse than no answer, because a researcher cannot easily tell the difference. - Agent state and handoffs made inspectable — LangGraph gives you the structure, but you need a trace a human can read when output is questioned. That is both a debugging tool and, later, an audit requirement. - Guardrails at the boundaries: what the agents may assert, when they must defer, and an explicit refusal path rather than a confident guess. - Evaluation harness with a fixed question set and expected characteristics, run on every change. Without it, prompt changes silently regress and nobody notices. Background: LLM integrations in production products, plus Python and TypeScript as daily languages and API orchestration as routine work. Questions: what specifically is unfinished — agent logic, integrations, or the evaluation side? And is the platform intended for internal research use, or will external clinicians rely on its output? The second raises the bar considerably, and I would want to plan for it. Martin

@ashinnd84

Hi, I read your post for "NexusMed AI: Advanced Multi-Agent Clinical Research Platform - 15/08/2026 05:13 EDT" and it lines up closely with the AI / ML work I do day to day. How I would approach it: 1. Agree the success metric before any modelling starts -- accuracy, latency, or cost per call -- so "done" means the same thing to both of us. 2. Stand up a small end-to-end baseline first. You see real output on your data early rather than at the end. 3. Iterate on the baseline, and hand over evaluation scripts plus notes so the numbers are reproducible on your side, not just mine. Directly relevant to your listed skills: API Integration, Artificial Intelligence, Full Stack Development, Large Language Models (LLMs), Machine Learning (ML), Node.js, Python, React.js, Retrieval-Augmented Generation (RAG), Typescript. My bid is ₹10625 against your ₹1500-12500 range, and I can start straight away. Let's connect to discuss this further -- happy to walk you through the approach and cover anything you want nailed down before you decide. Thanks for reading. Best regards, Ashish & Team

@albertc1996

Hello, I’m very interested in helping take NexusMed AI’s Literature Review Agent to the next stage. My background is in Python, AI/ML, backend development, RAG-based systems and API-driven applications, and I’m particularly interested in the engineering challenges involved in making agentic research systems reliable, traceable and evidence-backed. Based on the current architecture, I would focus on four main areas: 1. Academic Database Integration - Integrate PubMed through the Entrez API. - Build robust retrieval and normalization pipelines for papers and clinical literature. - Structure metadata such as titles, authors, abstracts, publication dates and identifiers so it can be reliably used by the RAG pipeline. - Handle pagination, rate limits, retries and API failures cleanly. 2. Evidence-Based RAG - Improve retrieval and context construction for the Literature Review Agent. - Ensure generated claims can be traced back to retrieved sources. - Attach real, clickable citations to relevant statements. - Reduce unsupported claims through retrieval validation and structured evidence handling. 3. Real-Time Research Streaming - Work with the existing Deno/WebSocket architecture to make research progress and synthesis appear smoothly in the frontend. - Handle intermediate agent states, partial responses and errors without breaking the user experience. 4. Biomedical Reflection Agent - Strengthen the existing reflection stage so the generated literature review is independently checked before being presented. - Validate whether important claims are actually supported by retrieved literature. - Flag missing evidence, contradictory findings and potentially unreliable conclusions. I’m also comfortable working with LangGraph/LangChain-style agent orchestration and understand the importance of keeping agent state, retrieval, evidence and final generation clearly separated. Since you already have the multi-agent graph, frontend and WebSocket communication in place, I would focus on extending the existing foundation rather than unnecessarily rewriting working components. I would also prioritize reproducibility and observability throughout the implementation, including structured logging, clear failure handling and tests around retrieval, citation generation and agent behavior. I can work within the existing Deno/TypeScript + React architecture and integrate the AI components around the current LangGraph/LangChain, Groq and Ollama setup. I estimate approximately 14 days for the initial implementation, depending on the current state of the existing Literature Review Agent and the completeness of the database/API integrations. Best regards, Albert

@TheRSTeam

Hi, I am a data analyst/statistician and Economist with more than 6 years of experience. I can do your project, Please take time to check my profile and then you decide to contact me.

@KolmanOuassou

Hi, I have been in the role for 6+ years and work as a data analyst, statistician and economist. I Have the ability to provide excellent work and the needs of your project. Would you be able to look at my profile and provide more information on previous projects and Reviews about my work as a contractor? Looking forward to your response. Best regards,

@NEHABHAT92

Having been in the IT industry for more than 9 years, I bring the perfect blend of experience and expertise needed to lead the next phase of development for NexusMed AI. My proficiency spans across various areas of development including Artificial Intelligence, Node.js, Python, and React.js- all of which align well with the technical stack for this project. Over the years, I've focused heavily on web and mobile application development, which I believe makes me exceptionally well-equipped to enhance NexusMed AI's multi-agent clinical research platform. Moreover, my experience in handling sensitive medical data and understanding the significance of Agentic RAG and Academic API integrations sets me apart as a suitable candidate for this role. As a seasoned developer, I understand the necessity of not just integrating PubMed and Google Scholar, but also ensuring their seamless functionality with accurate peer-reviewed medical journals and clinical trials. Plus, by choosing me for this project, you get to reap additional advantages like effective cost management, thorough post-delivery support (I offer 3 months free), generous discounts on hosting and domain services among other things. Investing in me means investing in reliable craftsmanship that will turn your ideas into reality. Let's convert NexusMed AI into a world-class clinical research platform together.

@alluankit01

Hi, I can help enhance NexusMed AI’s Literature Review Agent with stronger academic retrieval, citation-grounded RAG, live streaming output, and biomedical quality-check workflows. My approach will be to first review your existing Deno v2 backend, LangGraph state graph, WebSocket flow, RAG pipeline, frontend panels, and current Reflection Agent. Then I’ll improve the literature retrieval layer using PubMed/Entrez API and compliant academic-source integration, strengthen citation mapping, and make sure every generated claim is linked back to real evidence. I’m comfortable with TypeScript, Deno, React/Vite, WebSockets, LangGraph, LangChain, RAG, LLM orchestration, Groq/Ollama workflows, PubMed API integration, citation handling, AI agents, and medical-data-aware development. Deliverables: * PubMed/Entrez integration * Academic retrieval workflow * Improved RAG pipeline * Clickable citation mapping * Evidence cross-check logic * WebSocket streaming polish * Reflection Agent refinement * Biomedical quality checks * Frontend/backend integration fixes * Testing and handover notes I’ll focus on making the Literature Review Agent evidence-based, traceable, and reliable for clinical research use, while avoiding unsupported medical claims and keeping human review central. Best regards Ankit

@shayankhan333

Hello, I can finalize the Literature Review Agent on your Deno LangGraph backend, wiring PubMed Entrez and Google Scholar into the Agentic RAG so every claim carries a clickable peer-reviewed citation. I will also polish the WebSocket stream and tune the Ollama biomedical Reflection Agent for a solid secondary check. Questions: 1) Do you have an Entrez API key and a preferred Scholar access method, SerpAPI or direct? Looking forward to discussing further. Regards, Shayan.

@mikes126

Hi there, let's have short meeting if you wanna discuss how we can take NexusMed AI to the next level. I can help finalize the Literature Review Agent with PubMed/Entrez and Google Scholar integrations, citation-grounded RAG, and live WebSocket streaming. I’m comfortable with LangGraph/LangChain agent workflows, TypeScript/Deno, React, LLMs, and biomedical RAG pipelines. For the reflection layer, I can improve the biomedical Llama validation flow so generated claims are checked against retrieved evidence before being shown. I’ll also make sure citations are traceable, clickable, and properly linked to the source papers. I can work directly with your existing agent graph and avoid breaking the current UI/backend setup. Budget: $150 Timeline: 7 days

@ujjwalmahar01

To get the Literature Review Agent working reliably, I will start by mapping the Entrez API schema to your existing LangGraph state. Handling PubMed data requires strict XML parsing to ensure the citation metadata is clean before it hits the RAG pipeline. I have extensive experience building RAG systems that rely on strict citation grounding. For this project, I will refine the retrieval logic to ensure the Llama 3.3 orchestrator treats the PubMed and Google Scholar results as the primary source of truth. I will implement a verification step within the reflection loop that checks the retrieved document IDs against the generated claims to prevent hallucinations. Since you are already using Deno and WebSockets, I will ensure the streaming response handles the citation injection in real time so the React frontend can render the clickable links without flickering. Regarding the biomedical reflection, I will adjust the prompt engineering on your fine-tuned SLM to focus specifically on cross-referencing clinical trial identifiers. This ensures that the secondary quality check is not just summarizing but validating against the raw data pulled from your API integrations. I have handled similar complex data pipelines in my previous work using Python and Node.js for backend automation, and I am comfortable working within your established TypeScript and LangGraph architecture. Are you available for a brief call to discuss the current state of the LangGraph nodes and how you want...

@Praveenkmr583

Hi, I bring 10+ years of experience and 100+ completed projects to your NexusMed AI platform. My expertise in Python, ML, and Node.js ensures production-tested, scalable code. With a proven track record, I deliver high-quality results on time. Best regards, Praveen Kumar

@vaithiebby

Hi, I'm a full stack developer (React, Node.js, Python) and can support the next phase of NexusMed AI. I can work within your existing LangGraph-based multi-agent setup to help integrate PubMed/Entrez and Google Scholar data sources, improve citation handling in the RAG output, polish the WebSocket-based live streaming on the React frontend, and support testing of the Reflection Agent's quality checks. I write clean, maintainable code that fits into an existing codebase and I'm mindful of medical data sensitivity. I have a lot of automation and reusable tooling which helps me move quickly. Budget and timeline are negotiable - once we walk through the current codebase and exact scope together, I'll confirm a firm price and delivery date. Ready to start immediately.

@peterm461

Hello, I am Peter Mutinda, Ready to take your project foward and achieve excellent results. Your response is highly appreciated. Thank you.

@kishank499

NexusMed AI needs its Literature Review Agent to move beyond general web search for deep, evidence-based research, ensuring all AI claims have clickable citations. You need a system that connects medical professionals with accurate, verifiable information from sources like PubMed and Google Scholar. The goal is real-time, cross-referenced research synthesis, backed by clear sources. I will integrate with PubMed (Entrez API) and Google Scholar to pull medical literature. I'll enhance your RAG system so every AI claim includes direct, clickable citations. I will also polish the WebSocket stream for live research synthesis in the frontend. Finally, I'll refine the 'Reflection Agent' using your biomedical Llama model for a secondary quality check. My experience building an AI customer support chatbot involved designing a RAG system and integrating with an API for real-time data. Beyond just retrieving and citing, how critical is it for the Literature Review Agent to interpret nuanced clinical trial results, such as conflicting findings? Let's discuss how we can bring this agent to its next phase.

@CliveMSoftware

I have extensive experience building production-grade RAG systems with academic database integrations and multi-agent LLM pipelines. I'll implement robust PubMed and Google Scholar connectors via their APIs, enhance your LangGraph workflow with advanced retrieval strategies, and ensure real-time WebSocket streaming displays live citations with full traceability. The reflection agent refinement using your biomedical Llama model will validate output quality through secondary reasoning checks. With deep familiarity in Deno, TypeScript, LangChain, and medical data protocols, I'll deliver a fully functional Literature Review Agent that meets clinical research standards while maintaining the polished UI/UX your team has established.

@Waqas12301

Hi, I can help take NexusMed AI’s Literature Review Agent to the next level by strengthening the **academic retrieval, Agentic RAG, citation accuracy, streaming, and reflection pipeline**. I understand the existing architecture and can work directly with **Deno v2, TypeScript, LangGraph, LangChain, React, WebSockets, Groq/Llama, and Ollama** without disrupting the current multi-agent workflow. I can implement: * PubMed/Entrez API integration for peer-reviewed literature and clinical trials * Google Scholar integration where technically and legally supported * Evidence-grounded RAG with source verification * Clickable citations linked to original papers/records * Metadata extraction for authors, journal, PMID/DOI, dates, etc. * Improved retrieval, ranking, deduplication, and relevance filtering * Real-time WebSocket streaming for research synthesis * Biomedical Reflection Agent for secondary evidence/quality checks * Robust error handling, logging, and validation * Clean integration with your existing LangGraph state graph and UI I’ll prioritize **traceability and evidence grounding**, ensuring generated claims are supported by retrieved literature rather than relying on model knowledge alone. I’m comfortable working with sensitive medical/research data and can follow a security-conscious architecture. I’d first review the existing codebase and agent graph, then implement the literature pipeline incrementally with testing at each stage.