SOM.ai

Summary

"Think Clearer. Decide Better." SOM.ai is a Vedic intelligence engine grounded in the Bhagavad Gita , a philosophical reasoning system built for people who think for a living. No surface-level advice. No generic platitudes. Just timeless clarity. With 700 verses indexed and instantly searchable, an 8-agent reasoning chain, and a RAG pipeline backed by pgvector, SOM.ai transforms your real-world struggles into grounded, actionable wisdom in under 5 seconds.

Features

• Vedic Intelligence Core — Every answer is matched to a specific Bhagavad Gita verse, not scraped from random web pages
• Structured Output — Responses follow a three-part format: Meaning Synthesis → Apply Today → Reflect
• Multi-Agent Reasoning — 8 specialized agents collaborate on context retrieval, synthesis, and moderation
• RAG + Vector Search — pgvector-backed semantic search across all 700 Gita verses for precise retrieval
• Conversation Memory — Remembers your full journey across sessions, not just the last few messages
• Safety Moderation — Active moderation layer ensures responsible, grounded output for sensitive queries
• Instant Recall — Sub-5-second response time from query to philosophical insight

Architecture with Real-Life Use Case

The Vedic Intelligence Pipeline

When a user says: "My startup is pivoting and I feel like a failure."

1. Query Intake & Sanitization (instant) → Input is parsed and moderated for context
2. Semantic Verse Retrieval (< 1 sec) → pgvector search finds the most relevant Gita verse(s)
3. Multi-Agent Reasoning Chain (2–3 sec) → 8 agents collaborate on meaning, application, and reflection
4. Structured Synthesis (instant) → Output is formatted as Meaning → Apply → Reflect
5. Moderation Pass (instant) → Safety layer validates response before delivery
6. Session Memory Update (instant) → Conversation context is persisted for continuity

Total: < 5 seconds vs. years of trial-and-error, therapy queues, or hollow internet advice

Real-World Outcome

Generic AI Approach:

"Take a deep breath." "Have you tried journaling?" "Consider talking to someone."

Surface-level. Forgettable. Treats the symptom, not the cause.

SOM.ai Approach:

User asks: "My startup is pivoting and I feel like a failure."

Results in < 5 seconds:

1. Verse Retrieved: Bhagavad Gita 2.47 — "You have a right to perform your prescribed duty, but you are not entitled to the fruits of action."
2. Meaning Synthesis: Anxiety stems from obsessing over outcomes outside your control. Reclaim agency by focusing strictly on execution.
3. Apply Today: Define success today entirely by the effort expended on your deepest work block — disregard external validation.
4. Reflect: "What outcome are you mentally attaching to right now that is causing friction?"

✅ Result: Root-cause clarity grounded in 5,000 years of philosophical truth.

Tech Stack

• Frontend: Next.js 14 (App Router), TypeScript, Tailwind CSS
• Backend: FastAPI, Python, SQLAlchemy (async)
• AI / RAG: Groq API (LLaMA), pgvector, Retrieval-Augmented Generation
• Database: PostgreSQL with pgvector extension
• Architecture: Multi-agent orchestration, context-aware synthesis, moderation layer

Key Technical Achievements

• Fixed Philosophical Canon: Unlike general-purpose LLMs, SOM.ai is grounded exclusively in the 700 verses of the Bhagavad Gita — eliminating hallucinations and ensuring every response has a traceable source
• 8-Agent Reasoning Engine: Specialized agents handle retrieval, synthesis, application framing, reflection prompting, memory management, moderation, and more
• pgvector Semantic Search: Verse embeddings are stored in PostgreSQL via pgvector, enabling meaning-level similarity search far beyond keyword matching
• Structured Output Format: Every response is intentionally shaped into three actionable layers — Meaning, Apply, and Reflect — making wisdom immediately usable
• Conversation Memory: Full session history is retained, enabling SOM.ai to track your mental and philosophical journey over time

Architecture Highlights

Vedic RAG Pipeline:

User Query → Sanitization → Embedding Generation →
pgvector Semantic Search → Verse Retrieval →
Multi-Agent Reasoning Chain → Structured Synthesis →
Moderation Pass → Final Response

Architecture Comparison

• Where answers come from

  • Typical AI: Scraped from random web pages
  • SOM.ai: Matched to a specific Gita verse

• What you actually get

  • Typical AI: A wall of text
  • SOM.ai: Meaning → Apply → Reflect

• Conversation memory

  • Typical AI: Forgets after a few messages
  • SOM.ai: Remembers your full journey

• Depth of thinking

  • Typical AI: One-shot answer
  • SOM.ai: Multi-agent reasoning chain

Real-World Impact Example

Founders, Engineers & Therapists

The Problem with Generic AI:

• Produces engagement-optimized answers, not truth-optimized ones
• No philosophical grounding — answers shift based on phrasing
• Surface-level coping advice that treats symptoms, not root causes
• Forgets context between sessions; no continuity of thought

With SOM.ai:

• Every answer is traceable to a specific Gita verse with a 5,000-year track record
• Multi-agent reasoning pinpoints the root cause of your mental friction
• Structured output gives you something concrete to act on today
• Session memory means SOM.ai grows more useful the more you use it

Built for people who think for a living : the founders making high-stakes pivots, the engineers debugging their decision-making, the therapists seeking evidence-backed philosophical frameworks for their clients.

Development Journey

Technical Challenges Overcome

1. Grounding an LLM in a Fixed Canon

   • Problem: General LLMs hallucinate and drift from authoritative sources
   • Solution: RAG pipeline strictly limits retrieval to the 700 indexed Gita verses via pgvector similarity search

2. Multi-Agent Coordination at Speed

   • Problem: Chaining 8 agents risks compounding latency and coherence loss
   • Solution: Async FastAPI architecture with parallel agent execution where possible, keeping responses under 5 seconds

3. Meaning-Level Verse Retrieval

   • Problem: Keyword search misses semantically relevant verses
   • Solution: Sentence-level embeddings stored in pgvector enable meaning-aware retrieval even for abstract emotional queries

4. Structuring Philosophical Output for Action

   • Problem: Raw verse translations are cryptic and hard to apply to modern problems
   • Solution: Dedicated synthesis and application agents transform ancient text into a three-part actionable framework

5. Conversation Memory Without Context Bloat

   • Problem: Full history injection bloats the context window and slows reasoning
   • Solution: Memory agent maintains a compressed session summary that preserves continuity without sacrificing speed

Performance Benchmarks

• Average Response Time

  • Target: < 10 sec
  • Achieved: < 5 sec

• Verses Indexed

  • Target: 700
  • Achieved: 700 (complete Bhagavad Gita)

• Moderation Coverage

  • Target: Key sensitive categories
  • Achieved: Active moderation on every response

Resources & Links

• Live Platform: som.ai.thevanshgarg.com
• GitHub: github.com/vanshxdevs
• Free Access: Start free , no credit card required, takes 30 seconds

For Employers & Collaborators

What This Project Demonstrates:

• RAG Systems Engineering: Production-grade retrieval pipeline grounded in a fixed philosophical canon with pgvector
• Multi-Agent Architecture: 8-agent orchestration system handling retrieval, synthesis, application, memory, and moderation
• LLM Prompt Engineering: Structured output design translating ancient text into modern, actionable three-part responses
• Semantic Search: pgvector-backed embedding search enabling meaning-level verse retrieval from a 700-verse corpus
• Product Philosophy: Opinionated AI product design and grounding intelligence in truth, not engagement optimization
• Full-Stack Ownership: End-to-end architecture from RAG pipeline to Next.js 14 frontend

Contact: Available for technical interviews or architecture deep-dives

Project Status

Current: Live at som.ai.thevanshgarg.com
Corpus: 700 Gita verses indexed • 8 reasoning agents • < 5s response time
Next Milestones:

• Expanded verse corpus with Upanishads and Stoic philosophy
• Personalized insight tracking and journaling layer
• Team / shared session features for coaching use cases
• API access for therapists and mental health practitioners