AI Pharmacovigilance Prototype Development

@sajjadtaghvaeifr

1. Proposed architecture diagram (high-level is fine) The architecture is based on working with multimodal model and adding rag to them 2. Tooling choices (LangGraph vs CrewAI, Neo4j vs alternative, FAISS/Chroma vs Pinecone) I use lang chain 3. Estimated timeline + cost for MVP we need to discusse it i details 4. Expected OpenAI token cost range for demo run if you want to use them online n8n is 20$ per hours and 20$ oer hour for LLM in GPT. 5. Your experience with multi-agent orchestration + RAG + graph + regulated workflows I have worked with graph RAG and the original RAG

@EightVeer

Hi there, Based on your brief, we understand you're building a research-grade AI pharmacovigilance prototype aligned with ICH E2D(R1) using synthetic datasets, focused on multi-agent orchestration, Graph-RAG, and E2B-style mapping. At 8veer Consultancy, we’ve developed AI governance frameworks and compliant prototype systems using LangGraph, Neo4j, and OpenAI tooling, tailored for traceability, auditability, and modular orchestration. Our team proposes a cost-aware solution balancing deterministic agent routing, lightweight NLP layers, and selective GPT-4.1/4o use, with structured JSON/CSV outputs across all 11 agents. We will configure Graph-RAG using Neo4j or optimized SQLite tables for demonstrable reasoning tasks, and recommend FAISS for cost-effective vector storage. Estimated OpenAI token usage and tooling considerations will be provided with the architecture draft. We estimate delivery within 18–22 working days and are available for further discussion this week. Best Regards, 8veer Consultancy

@masiahm4

Hi! I’m a full‑stack developer (Python/JS, 5 years) specializing in multi‑agent automations, RAG, and auditable workflows. I’ll deliver a research‑grade PV prototype that proves E2D(R1) alignment, governance, and traceable decisioning on synthetic data. - Architecture (high‑level): Ingestion → LangGraph Orchestrator → Specialist Agents → Document RAG (Chroma/FAISS) + Graph‑RAG (Neo4j Docker) → Audit Log → JSON/CSV/E2B(R3) drafts → Optional reviewer UI. - Tooling: LangGraph (deterministic routing, HITL), Neo4j local (upgradeable to Aura), Chroma/FAISS (lowest cost), OpenAI GPT‑4o/4.1 with rules/cheap models first; optional HF NER local. - Timeline & cost (MVP): 4–6 weeks, $12k–18k fixed. - Token cost (demo 1k–5k posts): ~$150–600, embeddings ~$5–25; tunable via routing. - Experience: Built multi‑agent AI agent, RAG automations, audit‑ready invoice/CRM systems, governance and trace logs. Would LangGraph + local Neo4j + Chroma suit your cost targets? If yes, let’s schedule a 30‑min call to confirm scope and finalize a fixed quote.

@Rahulsingh5xz

Hello, I’m Rahul Singh, leading Team Velora, with 3 years of experience in multi-agent AI prototypes, RAG retrieval systems, and graph-based reasoning workflows. We can design a synthetic-data E2D(R1)-aligned pharmacovigilance prototype, including multi-agent orchestration, Graph-RAG + document RAG retrieval, structured JSON outputs, audit trails, and draft E2B(R3) mappings. Come into private chat to review similar multi-agent & regulated-workflow prototypes we’ve built and refine tooling choices for your MVP.

@Joelite17

I have extensive experience building exactly this type of multi-agent, RAG, and knowledge graph system for regulated domains, and I am confident I can deliver a robust research-grade prototype that meets your goals for E2D(R1) alignment, auditability, and cost-aware AI design. Phase 1 (Weeks 1-2): Core Architecture & Data Setup LangGraph orchestrator skeleton, agent definitions, Neo4j schema design, synthetic data ingestion pipelines. Phase 2 (Weeks 3-5): Agent Development & Core RAG Build the 11 specialist agents with tooling (Document RAG, Graph Cypher queries, LLM calls). Implement audit log. Phase 3 (Weeks 6-7): Integration, E2B Mapping & UI Wire outputs into draft E2B(R3) JSON/CSV. Build simple Streamlit dashboard for reviewer checkpoint. Phase 4 (Week 8): Testing, Refinement & Documentation Run end-to-end demo, refine agent prompts/rules, generate ODCS documentation bundle from system config.

@amaank66

Hello, I’m Amaan Khan from CUBEMOONS PVT LTD, and we specialize in building research-grade multi-agent AI prototypes with structured outputs, audit trails, and RAG/graph integration. Your E2D(R1) PV system prototype aligns well with our experience in regulated workflows. Proposed Architecture: Orchestration: LangGraph (Python) for deterministic agent routing with human-in-the-loop checkpoints. Agents: 10+ specialized PV agents handling triage, ODCS monitoring, PSP/MRP intake, seriousness/expectedness, duplicate detection, E2B mapping, and audit logging. Graph & RAG: Neo4j (cloud or local) for PV entity graph; FAISS or Chroma for document RAG of SOPs, templates, and label snippets. Models: Cost-aware design using lightweight HF models for preliminary classification; escalate complex cases to GPT-4.1/4o for extraction/classification; OpenAI embeddings for vector search. Outputs: Structured JSON/CSV case packs, E2B(R3) draft mappings, governance logs, day-zero registers, and optional reviewer dashboard. Timeline & Cost: MVP in ~6–8 weeks; token spend for 1k–5k synthetic posts estimated at $200–$400/month depending on use of GPT-4.1/4o. Local Neo4j and FAISS recommended to minimize recurring costs. We bring deep experience in multi-agent orchestration, graph-based reasoning, RAG integration, and regulated AI workflows, delivering reproducible, auditable, and demonstration-ready prototypes. Best regards, Amaan Khan P. CUBEMOONS PVT LTD.

@GameOfWords

As an experienced AI professional with a keen focus on building data-centric, AI-driven applications, I am highly qualified to tackle your complex pharmacovigilance prototype project. My varied background in integrating AI solutions across multiple platforms and my proficiency in the tools you plan to use, such as OpenAI, LangChain, Pinecone, n8n, and Neo4j positions me well to develop the multi-agent, graph-related task routing and document retrieval elements your project needs. With my assurance, your prototype will demonstrate how your industry can effectively meet ICH E2D(R1) expectations using the powerful blend of artificial intelligence and governance with an audit trail. My skills as an AI developer extend past just building technology-advanced solutions. I bring a cost-aware design approach that will ensure your prototype is affordable yet robust. For example, I use cheap models or rules for less difficult tasks and better manage the deployment of GPT-4.1/4o models, thereby saving costs while performing effectively. In addition, my expertise in HuggingFace models can be utilized as a secondary local option for NER or lightweight classification where applicable. In me, you'll find not only an AI specialist but also someone who understands the financial dynamics real-world projects function under. I am confident that together we can create a research-grade prototype that exemplifies best-in-class AI pharmacovigilance alignment with E2D(R1) standards.

@Mayanks010

Dear Sir, I have studied your project requirments deeply .However the proposal is so detailed that I am unable to put everything here. [Note: FREELANCER ALLOWS ONLY 1500 CHARACTERS IN THE PROPOSAL]. . hence I request you to message me in the chat so that I can ask every questions in the chat. Awaiting your response Mayank Sharma

@AlfiyaMulla21

Hello, I can build a research-grade, non-production multi-agent AI pharmacovigilance prototype aligned with ICH E2D(R1) using synthetic data only, exactly as described. Proposed approach (brief): • LangGraph (Python) for deterministic multi-agent orchestration with human-in-the-loop checkpoints and full audit trail • Dual RAG: – Document RAG (FAISS/Chroma – low cost) for SOPs, labels, ODCS plans – Graph-RAG (Neo4j – local Docker) for Product–Case–Event–Source–ODCS reasoning, duplicate linking, and day-zero traceability • Cost-aware model design: rules + lightweight logic first, GPT-4o/4.1 only for complex PV reasoning • Draft E2B(R3) field mapping (JSON/CSV) with source type, seriousness, expectedness, and day-zero basis • Agent-by-agent governance & audit logging (regulator-friendly) Deliverables: • Multi-agent PV pipeline (11+ agents) • Graph + Document RAG demo queries • Case triage outputs & duplicate detection • Draft E2B(R3) exports + ODCS governance artifacts • Optional lightweight reviewer dashboard I have hands-on experience with multi-agent orchestration, RAG/Graph-RAG, and regulated workflow design, with strong focus on traceability, explainability, and audit readiness. Happy to share a clear architecture plan or start immediately.

@ThynkLoop

We are Thynk Loop, and this prototype fits exactly how we approach regulated, research-grade AI systems: deterministic orchestration, strict guardrails, and full traceability. I’d propose LangGraph in Python for orchestration, using explicit state transitions and rule-based routing. Each agent would consume and produce structured JSON, with all outputs written to an audit log. Human-in-the-loop checkpoints would be built directly into the flow, not added later. For retrieval, I’d combine document RAG using a local vector store (FAISS or Chroma to minimize cost) with a small Neo4j graph to model PV entities and relationships. Vector RAG handles guideline and SOP lookup; Graph-RAG is reserved for reasoning tasks like duplicates, ODCS linkage, and day-zero attribution. The model layer would be cost-aware: rules and lightweight logic first, embeddings for retrieval, and GPT-4.1/4o only for judgment-heavy steps such as classification edge cases and narrative drafts. For 1,000–5,000 synthetic records, expected token spend would typically stay in the low hundreds of dollars per month. E2B(R3) outputs would be draft-level JSON/CSV mappings with correct tagging for source type, seriousness, expectedness, and day-zero basis, clearly marked as non-submission. I have experience with multi-agent systems, RAG pipelines, and audit-heavy workflows where explainability matters more than novelty. If this aligns, I can share a simple architecture sketch, timeline, and MVP cost next.

@Raghavjhag01

Hi, This is a well-defined research prototype and very feasible as described. I can build a deterministic, traceable multi-agent PV system using LangGraph (Python) with clear orchestration rules, human-in-the-loop checkpoints, and full audit logging. The prototype will demonstrate Document RAG + Graph-RAG, draft E2B(R3) mappings, and governance artifacts aligned to ICH E2D(R1)—all running on synthetic data only. Do you want strict step-by-step agent sequencing or conditional branching based on outputs? Human-in-the-loop at which stages (ICSR decision, seriousness, E2B mapping)?