Preetam Ramdhave
Forward Deployed Engineer · Seattle, WA

Preetam Ramdhave

I deploy AI where it has to work.

Architecting resilient, agentic AI systems and cloud-native platforms for Fortune 500s and high-growth scaleups. 18 years of enterprise experience. Based in Seattle.

15,247 prescriptions processed4 production AI systems shippedFull-stack · Agentic AI · AWS

Selected Outcomes — Real Customers, Real Numbers

17+
Years

Shipping production systems end-to-end — UK enterprise clients, Fortune 500, solo founder products.

60–80%
Reduction

Manual review effort eliminated via the org's first production agentic AI workflow.

15,000+
PDFs

Handwritten prescriptions processed in 48 hours at a charitable healthcare event.

Awards

"Delighted Customer" awards (2012–2014) voted by UK enterprise clients for direct impact.

Featured Deployments

Full-Stack · Agentic AI · Healthcare · Ed-Tech

View all deployments
Agentic AI Document Review screenshot
Enterprise AI

Agentic AI Document Review

60–80% effort reduction

First-of-its-kind production agentic AI workflow for a Fortune 500 enterprise. Embedded with the document review team, identified that reviewers were burning significant hours per week on repetitive policy checks. Designed and shipped an event-driven pipeline: API Gateway → Step Functions → parallel Lambda tools (PDF parsing, chunk planning, classification, policy validation) → RAG grounding on AWS Kendra GenAI Index. Every output traceable via correlation IDs for compliance audits.

AWS BedrockStep FunctionsKendra GenAIRAGLambdaPython
ScholarPath screenshot
Ed-Tech

ScholarPath

Live in production

Ed-tech platform for Maharashtra MSCE scholarship exam prep, built from a gap discovered by embedding with parents and exam coordinators in the scholarship ecosystem. Parent-as-gateway model with child profiles, tiered access, and 124-test end-to-end testing plan. Generically extensible exam-category configuration enables rapid expansion to new exam verticals. Supabase MCP integration with Claude Code for AI-assisted delivery.

ReactTypeScriptFastAPISupabaseRazorpayMCP
JapaApp screenshot
Spiritual-Tech

JapaApp

Live in production

Spiritual mantra-tracking PWA built for practitioners of Vedic disciplines. Originally architected on AWS (Lambda, RDS Proxy, Cognito JWT, SAM) before owning the platform-migration decision to Firebase. Implemented global admin via Firebase Custom Claims and Razorpay subscription/donation flow with tiered pricing.

React 18TypeScriptFirebaseFramer MotionRazorpayPWA

Open Source

Step 06 · Generalize — turning one customer's win into a reusable pattern.

NervaPack screenshot
Open Source AI · Developer Tooling

NervaPack

91.2% token reduction · 96% recall

Privacy-first, 100% offline knowledge graph & bi-temporal memory engine for AI coding agents. Uses Tree-sitter AST parsing, K-hop BFS graph traversal, and SQLite FTS5 bi-temporal facts storage to cut token consumption by 91.2% while achieving 96% recall on SWE-bench Lite. Features a dual MCP server suite (20 tools across code graph and memory) native for Claude Code, Cursor, and Windsurf.

PythonKnowledge GraphTree-sitter ASTBi-Temporal MemoryMCPSQLite FTS5ChromaDB
Open Source · Pro Bono
OmmSai — Healthcare AI Pipeline

OmmSai — Healthcare AI Pipeline

15K PDFs · 20× faster

Open-source Python pipeline I donated to a charitable healthcare event that needed to digitize 15,000+ handwritten prescription PDFs in 48 hours. Built with Claude Sonnet (Anthropic API), Google Drive API, ThreadPoolExecutor concurrency, and a Tkinter operator GUI so non-engineer volunteers could run it on any Windows laptop. Released publicly so other charities and clinics can reuse the pipeline.

PythonClaude APIGoogle Drive APIThreadPoolExecutorTkinter
Open Source AI
Handwriting JSON

Handwriting JSON

Published on PyPI

Open-source Python package and CLI for automating handwritten document workflows. Converts forms, notes, and scanned paperwork into structured JSON using vision LLMs and optional schema guidance.

PythonDocument AIVision LLMsLiteLLMTyperJSON Schema
timesfm-mcp diagram
Open Source AI

timesfm-mcp

Published on PyPI · v0.1.6

An MCP server that gives any AI agent zero-config time-series forecasting. Plugs Google's TimesFM 2.5 foundation model — plus a pure-NumPy statistical baseline — into Claude, Cursor, or any MCP client via one install line. The agent calls a forecast tool, gets point predictions, confidence bands, and a trend/seasonality summary, then writes the recommendation itself. Includes a backtest tool reporting MAE/sMAPE on held-out data so forecasts are validated before they're trusted.

PythonMCPFastMCPNumPyPydanticTimesFM 2.5Time-Series

How I Work

The Forward Deployed Engineer motion — same pattern across UK enterprise clients, Fortune 500, and solo founder products.

Full story
01

Embed

Sit with the customer — external client, internal department, or end user. Watch how they actually work.

02

Discover

Find the real problem. It is almost never the stated problem.

03

Design

Architect the solution that fits the customer's reality — their data, their systems, their team, their security posture.

04

Ship

Build it end-to-end. Backend, frontend, infra, security, observability. No handoffs.

05

Operate & Iterate

Stay with it after launch. Watch the customer use it. Iterate on what the field teaches.

06

Generalize

Turn one customer's win into a reusable pattern the rest of the org can leverage.

Recent Writing

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Field Notes

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