Building in public

Sri Panchavati

Director, AI Engineering & Innovation

Technology executive with a Big 4 consulting background and deep financial-services execution. International career spanning India, UK, Sweden, and 14+ years in Canada — scaling engineering organizations, architecting multi-million-dollar cloud migrations, and pioneering the governed adoption of AI.

24+ Years in Tech
Big 4 Consulting
9 Agent Estimation Pipeline
57 Open-Source Tools
Free · MIT 57 browser tools — JSON, regex, cron, kanban, habits, color, markdown… single-file · no signup · no tracking tools.parallelromb.dev

Twenty-four years of building, in two modes

By day I lead an AI Engineering & Innovation team at one of Canada's largest banks — agentic systems, LLM platforms, and the governance that lets a regulated business actually use them. Before that I ran the bank's wealth-management data and reporting organization through its move to the cloud, spent seven years in Big 4 consulting leading data and automation practices, and started out building BI and ETL systems for banks and manufacturers across India, the UK, Sweden and Canada.

By night I build and run Lumen, a self-hosted family AI operating system — a Chief of Staff on Telegram, long-term memory, and a bedtime-story studio with its own author, composer and sound designer. Its memory layer became Smara, a product. Everything on this page is something I use.

Find me
Working on right now
  • PJ Tales — the bedtime-story studio: research → author → editor → narration → composed score → sound design → QA, one tap to approve
  • Smara — getting the MCP server in front of the people who install memory tools · smara.io
  • Lumen Tools — 57 single-file browser tools, no signup, no tracking · tools.parallelromb.dev · source

What I Bring to the Table

The intersection of enterprise architecture discipline and hands-on AI product development. I don't just design systems on whiteboards — I build and operate them.

Data & Analytics
Enterprise Data Strategy
Lakehouse architecture (Azure/Databricks), dimensional data modelling, ETL/ELT pipeline design, enterprise BI (Power BI, Workday), data governance (PIPEDA compliance), semantic search with pgvector & vector embeddings
Architecture
Cloud & System Design
Multi-service orchestration (15+ microservices), event-driven architecture, 4-layer memory systems, graph databases (Neo4j), caching strategies (Redis ring buffers), CI/CD pipelines (Azure DevOps), infrastructure-as-code
AI / ML
Operationalized AI
Multi-agent orchestration, LLM routing & fallback chains, RAG pipelines, embedding-based retrieval, Ebbinghaus decay scoring, MCP protocol, prompt engineering, AI governance & ethics frameworks
Leadership
Executive & Organizational
Scaled teams 4→37, P&L ownership (multi-million-dollar portfolios), global delivery models (onshore/offshore), C-suite advisory, board presentations, practice generation from zero, vendor & contract management
Data Modelling
Relational & Graph
Star/snowflake schemas, temporal bi-directional graphs, multi-tenant SaaS data models, vector embedding schemas (1024-dim), tenant-isolated security models, PostgreSQL extensions (pgvector, pg_trgm)
Consulting
Big 4 & Functional
Seven years in Big 4 consulting serving Canada's largest banks, after a decade at global consultancies. Pre-sales, technical proofs-of-concept, practice leadership (Workday Prism), rapid turnaround in chaotic environments.

Where I've Operated

Deep domain expertise across financial services and technology consulting, with product ventures in AI infrastructure, education, and digital media.

🏦
Banking & Wealth
Several of Canada's largest banks — wealth management, core banking data, portfolio management, regulatory compliance
📊
Management Consulting
Big 4 — practice generation, enterprise transformation, pre-sales engineering
🤖
AI Infrastructure
Smara (Memory API), MCP Doctor, Lumen — developer tools, agent memory, protocol compliance
📚
EdTech & Digital Media
PJ Tales (AI storytelling), InBlue (financial education) — content pipelines, TTS, curriculum design

What I've Built

Production-hardened systems running on real infrastructure, serving real users. Not demos. Not tutorials. Shipped products.

Smara live

Memory-as-a-Service API for AI agents. Temporal Memory Scoring with Ebbinghaus decay curves. SDKs for Python, JS/TS, MCP server, LangChain, and LlamaIndex. 96% cheaper than competitors.

Fastify PostgreSQL pgvector Voyage AI Stripe Railway MCP
--status
3-callintegration
$19dev/mo
Lumen Tools live

58 single-file browser tools — JSON formatter, regex tester, cron builder, kanban, habits, color palette, markdown editor, encoder, chmod, IP tools, more. MIT-licensed, no signup, no tracking, your data stays in localStorage. Built in one night to fill the gaps in a daily workflow.

Vanilla JS Single-file HTML Cloudflare Pages KV D1 MIT
58tools
0trackers
1file each
Scoper shipped open source

AI project estimation platform. Upload a BRD/SOW → 9 AI agents run a visual drag-and-drop pipeline → complete project estimate in under 2 minutes.

React Vite React Flow FastAPI SQLite YAML Agents
9AI agents
<2 minestimation
5workflow templates
PJ Tales live

AI bedtime story production pipeline. Full screenplay format (Pixar/Ghibli quality), 14 TTS voices, 8-dimension quality scoring, visual pipeline IDE.

FastAPI React Flow Orpheus TTS Edge TTS Ollama FFmpeg
14TTS voices
8quality dims
MCP Doctor shipped

MCP server compliance testing and monitoring. 50+ automated checks against the official MCP spec. A-F grading system, CI/CD ready.

Fastify Next.js MCP Protocol
50+compliance checks
A-Fgrading
InBlue live

Covered call ETF investment guide. 5-tab dashboard with strategies, ETF reference, wisdom library, portfolio builder, and DRIP calculator.

React Financial Data
5dashboard tabs

The 4-Layer Memory Stack

Lumen's memory architecture is the core IP. Four layers, each solving a different retrieval problem, unified into a single context pipeline.

Layer 1 · Speed
Redis Ring Buffer
40-turn sliding window per user, cross-channel unified, compressed at 500-char boundary
Layer 2 · Relevance
Hybrid Search + RRF
BM25 + pgvector cosine → Reciprocal Rank Fusion → LLM re-ranking of top-8
Layer 3 · Relationships
Neo4j Bi-temporal Graph
Expire+create edges, full relationship history, temporal asOf queries
Layer 4 · Salience
Ebbinghaus Decay
Importance feedback loop (+0.05 per retrieval, capped 0.95). Memories that matter persist.
LLM Routing
Local-First Fallback Chain
Ollama → Anthropic → Gemini → OpenAI. Free local inference first, cost tracking per call.
Agent Framework
10 Persona Agents
Specialized agents with soul configs, composable skills, A2A protocol, multi-channel routing

Wealth Tech Footprint

The toolchain in active use across a Canadian bank — the platforms, custodians, and legacy systems I work across daily.

Azure Platform
Azure ODSADFDatabricks Delta LakeADLSAzure SQL MI ASE v3Function AppsKey Vault Azure DevOpsARMExpressRoute Data Mesh (EDGE)
Custodians & Wealth Platforms
FIS WealthwareBroadridge CGI Wealth360Croesus Unified AccountMid-Office eCRMFIX protocol
Data & Orchestration
AutosysPower BI SSRSSSIS SQL ServerPython PowerShellTypeScript SFTP
Legacy & Enterprise Heritage
SybasePowerBuilder EBCDICVSAM KSDS AS400SQL Server 2012

Technologies in Production (Build Track)

Not technologies I've studied. Technologies running in production right now, serving real workloads on parallelromb.dev products.

TypeScript / Node.js
Python / FastAPI
Next.js / React
PostgreSQL / pgvector
Redis
Neo4j
Ollama / Gemma / LLaMA
Claude / GPT / Gemini APIs
Voyage AI Embeddings
React Flow
Cloudflare (Pages/Tunnel)
Railway
Stripe Billing
MCP Protocol
Azure / Databricks
Docker / launchd
Orpheus / Edge TTS
Playwright Testing

How I Think

Architecture is the art of making the right tradeoffs visible. Good systems reveal their intent through structure — the best documentation is the code itself.

The measure of an architect isn't the complexity they can build — it's the complexity they can eliminate while preserving capability.
Design with Intent
Every layer, every interface, every naming convention should communicate why it exists. If a junior engineer can't trace a decision back to a business constraint, the architecture failed.
Govern, Then Automate
AI adoption without governance is technical debt with a marketing budget. Establish guardrails first — compliance, data lineage, access controls — then accelerate.
Simplicity is a Feature
Three well-designed microservices beat twelve tangled ones. Attention to detail in naming, spacing, error handling, and observability is what separates production systems from prototypes.

Where I'm Going

Building at the intersection of enterprise architecture and AI product development, with a clear trajectory toward executive leadership and entrepreneurship.

AI Engineering at Enterprise Scale
What I Want to Build
  • Large-scale cloud transformations — migrating legacy platforms to modern, governed architectures with measurable ROI
  • AI/ML operationalization in regulated industries — bridging the gap between proof-of-concept and production-grade deployment
  • Data strategy and platform architecture — building the data foundations that make AI adoption possible at enterprise scale
AI Products & Developer Tools
What I'm Exploring
  • Smara — building the default memory layer for AI agents — persistent context at a fraction of the cost of existing solutions
  • Developer tooling — MCP Doctor, Scoper, and open-source contributions to the AI developer ecosystem
  • Multi-agent systems — production orchestration patterns, memory architectures, and autonomous workflows

Interested in working together? I'm open to architecture & cloud leadership, functional consulting, and AI product partnerships.

Blog

Technical deep-dives, product decisions, and lessons from building in public.