AI Pharmacovigilance Prototype Development
Budget: ₹37,500 – ₹75,000 INR
E2D(R1) PV Multi-Agent + Graph-RAG Prototype (Synthetic Data)
Hi — I’m seeking initial quotes for building a research-grade prototype of a multi-agent AI pharmacovigilance (PV) system aligned to ICH E2D(R1).
This is not production, not SaaS. It will run on synthetic/dummy PV data only. The goal is to demonstrate how industry can meet E2D(R1) expectations using AI + governance + audit trail.
1) What I want to build (core components)
A) Multi-agent Orchestration (Required)
I need an orchestrator that routes tasks to specialist agents and enforces guardrails.
Preferred orchestration options (choose one):
• LangGraph (Python) (preferred)
• CrewAI (Python)
• Or a custom orchestrator (state machine + tool router)
Required behavior:
• deterministic routing rules (not one giant prompt)
• agent-by-agent outputs stored + traceable
• human-in-the-loop checkpoints
B) Graph-RAG + Document RAG (Required)
I want the prototype to show two retrieval modes:
1. Document RAG (vector search)
o retrieves relevant “guideline snippets / SOP snippets / ODCS plan templates / label snippets” from a synthetic corpus
2. Graph-RAG (knowledge graph + retrieval)
o a small Neo4j (or equivalent) graph representing PV entities & relationships such as:
o Product → Event → Case → Source → ODCS → Reporting Rule → Evidence
o and links like: “case derived from PSP”, “case is duplicate of X”, “case requires follow-up”, “case has day-zero basis”
Graph-RAG should be used for reasoning tasks like:
• “Show all cases sourced from ODCS digital listening last week that are serious/unexpected”
• “Which ODCS plan controlled this case’s day-zero?”
• “Which cases are duplicates or linked across sources?”
Graph tech options:
• Neo4j (preferred)
• or SQLite graph tables (if Neo4j is too heavy), but graph queries must be demonstrable
C) Models (Required + specify cost options)
I want the prototype to support two model modes:
Primary (cloud) option:
• OpenAI GPT-4.1 or GPT-4o for extraction/classification
• OpenAI text-embedding-3-large (or small) for embeddings
Secondary (local) option (nice-to-have):
• A HuggingFace model for NER / lightweight classification (optional)
Freelancer should propose a “cost-aware” design:
• most steps handled by cheap model or rules
• only hard steps escalate to GPT-4.1/4o
D) E2B(R3) Mapping (Required at draft level)
Not full regulatory submissions.
But the system must generate E2B-style field mapping drafts (JSON/CSV) with correct tagging:
• spontaneous vs solicited
• PSP / MRP vs digital ODCS
• seriousness & expectedness flags
• day-zero and evidence basis
2) What the agents should look like (minimum set)
At minimum I need these agents:
1. Orchestrator / PV Lead agent
2. Digital platform monitoring agent (MAH-owned)
3. External digital listening agent (ODCS-scoped)
4. PSP/MRP intake agent
5. ICSR minimum-criteria & identifiability agent
6. Day-zero & reporting-clock agent
7. Other-observations / med-error / pregnancy agent
8. Seriousness + expectedness agent
9. Duplicate detection agent
10. E2B mapping + narrative draft agent
11. Governance + audit trail agent
Each agent must:
• take structured input
• output structured JSON/CSV
• write to a trace log (audit trail)
3) Tools / subscriptions I expect to pay for (budget guidance)
Please quote assuming:
• OpenAI API usage (I will provide API key)
• Neo4j Aura (if needed) or local Neo4j Docker
• Optional: Pinecone (or use FAISS/Chroma locally to avoid recurring cost)
You should tell me in your quote:
• estimated monthly OpenAI token spend for a demo dataset (e.g., 1,000–5,000 synthetic posts)
• which vector DB you recommend for lowest cost
• whether Neo4j cloud is necessary or local is fine
4) Inputs (what I will provide)
I will create synthetic datasets including:
• social posts, forum posts, MAH website comments
• PSP call notes and MRP survey text
• label “expectedness” lookup table
• ODCS plan templates and run logs
No real patient data.
5) Outputs I want from the prototype
1. Case triage outputs
• ICSR vs Other Observation vs Non-safety
• seriousness/expectedness
• duplicate links
2. Governance artifacts
• ODCS documentation bundle generated from system config
• audit trail of every AI + human decision
• day-zero register + escalation log
3. Integration-ready exports
• CSV/JSON case pack for safety DB ingestion (draft)
• E2B(R3) field mapping (draft)
4. Reviewer UI (optional but valuable)
• simple dashboard to accept/reject cases and add notes
6) What I need from you now (to quote)
Please respond with:
1. Proposed architecture diagram (high-level is fine)
2. Tooling choices (LangGraph vs CrewAI, Neo4j vs alternative, FAISS/Chroma vs Pinecone)
3. Estimated timeline + cost for MVP
4. Expected OpenAI token cost range for demo run
5. Your experience with multi-agent orchestration + RAG + graph + regulated workflows
Hi — I’m seeking initial quotes for building a research-grade prototype of a multi-agent AI pharmacovigilance (PV) system aligned to ICH E2D(R1).
This is not production, not SaaS. It will run on synthetic/dummy PV data only. The goal is to demonstrate how industry can meet E2D(R1) expectations using AI + governance + audit trail.
1) What I want to build (core components)
A) Multi-agent Orchestration (Required)
I need an orchestrator that routes tasks to specialist agents and enforces guardrails.
Preferred orchestration options (choose one):
• LangGraph (Python) (preferred)
• CrewAI (Python)
• Or a custom orchestrator (state machine + tool router)
Required behavior:
• deterministic routing rules (not one giant prompt)
• agent-by-agent outputs stored + traceable
• human-in-the-loop checkpoints
B) Graph-RAG + Document RAG (Required)
I want the prototype to show two retrieval modes:
1. Document RAG (vector search)
o retrieves relevant “guideline snippets / SOP snippets / ODCS plan templates / label snippets” from a synthetic corpus
2. Graph-RAG (knowledge graph + retrieval)
o a small Neo4j (or equivalent) graph representing PV entities & relationships such as:
o Product → Event → Case → Source → ODCS → Reporting Rule → Evidence
o and links like: “case derived from PSP”, “case is duplicate of X”, “case requires follow-up”, “case has day-zero basis”
Graph-RAG should be used for reasoning tasks like:
• “Show all cases sourced from ODCS digital listening last week that are serious/unexpected”
• “Which ODCS plan controlled this case’s day-zero?”
• “Which cases are duplicates or linked across sources?”
Graph tech options:
• Neo4j (preferred)
• or SQLite graph tables (if Neo4j is too heavy), but graph queries must be demonstrable
C) Models (Required + specify cost options)
I want the prototype to support two model modes:
Primary (cloud) option:
• OpenAI GPT-4.1 or GPT-4o for extraction/classification
• OpenAI text-embedding-3-large (or small) for embeddings
Secondary (local) option (nice-to-have):
• A HuggingFace model for NER / lightweight classification (optional)
Freelancer should propose a “cost-aware” design:
• most steps handled by cheap model or rules
• only hard steps escalate to GPT-4.1/4o
D) E2B(R3) Mapping (Required at draft level)
Not full regulatory submissions.
But the system must generate E2B-style field mapping drafts (JSON/CSV) with correct tagging:
• spontaneous vs solicited
• PSP / MRP vs digital ODCS
• seriousness & expectedness flags
• day-zero and evidence basis
2) What the agents should look like (minimum set)
At minimum I need these agents:
1. Orchestrator / PV Lead agent
2. Digital platform monitoring agent (MAH-owned)
3. External digital listening agent (ODCS-scoped)
4. PSP/MRP intake agent
5. ICSR minimum-criteria & identifiability agent
6. Day-zero & reporting-clock agent
7. Other-observations / med-error / pregnancy agent
8. Seriousness + expectedness agent
9. Duplicate detection agent
10. E2B mapping + narrative draft agent
11. Governance + audit trail agent
Each agent must:
• take structured input
• output structured JSON/CSV
• write to a trace log (audit trail)
3) Tools / subscriptions I expect to pay for (budget guidance)
Please quote assuming:
• OpenAI API usage (I will provide API key)
• Neo4j Aura (if needed) or local Neo4j Docker
• Optional: Pinecone (or use FAISS/Chroma locally to avoid recurring cost)
You should tell me in your quote:
• estimated monthly OpenAI token spend for a demo dataset (e.g., 1,000–5,000 synthetic posts)
• which vector DB you recommend for lowest cost
• whether Neo4j cloud is necessary or local is fine
4) Inputs (what I will provide)
I will create synthetic datasets including:
• social posts, forum posts, MAH website comments
• PSP call notes and MRP survey text
• label “expectedness” lookup table
• ODCS plan templates and run logs
No real patient data.
5) Outputs I want from the prototype
1. Case triage outputs
• ICSR vs Other Observation vs Non-safety
• seriousness/expectedness
• duplicate links
2. Governance artifacts
• ODCS documentation bundle generated from system config
• audit trail of every AI + human decision
• day-zero register + escalation log
3. Integration-ready exports
• CSV/JSON case pack for safety DB ingestion (draft)
• E2B(R3) field mapping (draft)
4. Reviewer UI (optional but valuable)
• simple dashboard to accept/reject cases and add notes
6) What I need from you now (to quote)
Please respond with:
1. Proposed architecture diagram (high-level is fine)
2. Tooling choices (LangGraph vs CrewAI, Neo4j vs alternative, FAISS/Chroma vs Pinecone)
3. Estimated timeline + cost for MVP
4. Expected OpenAI token cost range for demo run
5. Your experience with multi-agent orchestration + RAG + graph + regulated workflows