Sai Chandradeep Telaprolu — Senior Platform PM | AI Systems

How I Create Product Leverage

Platform Product Leadership

I reduce operational friction by building reusable systems — multi-tenant architecture, API contracts, governed data access, and workflow orchestration — so teams can move faster without multiplying engineering work.

Multi-Tenant Architecture

I design reusable product systems that separate shared platform logic from client-specific configuration, making scale easier without multiplying engineering work.

Contracts & Orchestration

I define product contracts, validation logic, and routing rules that reduce handoffs, improve reliability, and shorten time-to-completion in complex workflows.

Governed Data Access

I build trusted data products with the right controls, so teams can self-serve without sacrificing governance.

Telemetry & Reliability

I use instrumentation and experiments to find friction, improve adoption, and reduce time-to-detect when workflows fail.

Selected Work

Selected Product Case Studies

Three flagship case studies showing judgment under constraints — not just solutions and outcomes, but the context, tradeoffs, and decisions that shaped them.

Enterprise-shipped

01 / Platform Scale

Fast-Track Onboarding STP
(Digital Banking Platform)

Digital banking onboarding across web and mobile for startup and mid-market clients, covering Wires, ACH, RTP, and Check Deposits with back-office integrations.

What was happening

Onboarding was slow and manual (72 hours–1 week) due to repeated handoffs, missing information, and inconsistent data capture. Each new client required custom engineering work, and 68 distributed teams were building redundant solutions.

What I owned

I led platform strategy, defined the reusable schema layer, designed API contracts and validation rules, and directed a cross-functional squad of 18 engineers and 4 consultants through three major release cycles.

The decision I had to make

We could either build one-off client solutions faster (short-term win) or invest in a reusable schema and contract layer that would slow initial delivery but eliminate redundant work across 68 teams. I advocated for the reusable approach and secured leadership buy-in by modeling the compounding engineering cost of the one-off path.

What I learned

The "publish once, reuse everywhere" model only works if you invest equally in adoption tooling. We initially under-invested in documentation and self-service configuration, which created a secondary bottleneck. The generative AI authoring tool we later shipped was the direct result of that learning.

Problem

Onboarding was slow and manual (72 hours–1 week) due to repeated handoffs, missing information, and inconsistent data capture, leading to high rework and support load.

Solution

Standardized onboarding workflow with upfront validation, pre-populated fields, guided data capture, and automated assignment/acceptance steps tied to back-office APIs.

Impact

Onboarding time reduced from 72 hours–1 week to under 1 hour
Core API and validation redesign reduced error rates by 90%
Generative AI authoring reduced engineering tickets by 80%
Scaled the platform from 45 to 180+ accounts across 15 back-office payment integrations
Reusable schemas eliminated redundant engineering builds across 68 distributed teams

Technical Focus: Workflow orchestration · Upfront validation · API-driven straight-through processing

Digital Banking Onboarding Platform — System Topology

Client Portal / Product Intake

POST /v1/intake/submit · JSON Schema v3

AI Personalization Layer

gRPC · Vector Similarity Lookup

Product Definition Catalog

GET /v1/catalog/validate · Schema Registry

Workflow Orchestration Engine

Event-Driven · Pub/Sub Routing

API Integration Layer

REST + gRPC · Contract-First Design

Payment Services & Back Office Systems

Wires · ACH · RTP · Check Deposits

Enterprise-shipped

02 / Consumer Funnel

Checkout Funnel Optimization
Yum! Brands · 50M+ Digital Orders / Year

Consumer-facing digital ordering funnel (KFC, Pizza Hut, Taco Bell) spanning web and mobile apps, serving tens of millions of orders annually across multiple payment gateways and tender types.

What was happening

Drop-offs, declines, and timeouts were hard to diagnose with limited instrumentation. We could see that conversion was below target, but we could not isolate whether the issue was device-specific, browser-specific, or gateway-specific.

What I owned

I owned the checkout experience end-to-end: funnel instrumentation strategy, A/B test design, payment gateway failure analysis, and the product roadmap for checkout reliability improvements.

The decision I had to make

Engineering wanted to rebuild the checkout flow. I argued for instrumenting the existing flow first — measuring by device, browser, and gateway — so we could target the rebuild on the actual failure patterns rather than guessing. The data showed that 60% of drop-offs were concentrated in two gateway/browser combinations, which let us fix the problem with targeted changes instead of a full rewrite.

What I learned

Instrumentation before redesign is almost always faster than redesigning blind. The real-time diagnostics dashboard we built became a template that other product teams at Yum! adopted for their own funnel monitoring.

Problem

Drop-offs, declines, and timeouts were hard to diagnose with limited instrumentation, making it impossible to isolate issues by device, browser, or gateway.

Solution

Instrumented funnel events, analyzed failure patterns by device/browser/gateway, and ran experiments on messaging and retry flows to improve completion rates.

Impact

Reduced checkout drop-off rate via A/B-tested messaging, retry flows, and gateway failure fixes — directly improving order conversion across 50M+ annual consumer transactions
Built real-time funnel instrumentation enabling same-day diagnosis of payment failures by device, browser, and gateway — reducing operational response time from days to hours
Expanded payment tender coverage (credit, debit, digital wallets) improving checkout success rates for a broader consumer base

Technical Focus: Consumer funnel instrumentation · A/B experimentation · Payment gateway failure analysis · Omnichannel checkout · Tender type expansion

Checkout Funnel — System Topology

Menu → Add to Cart

POST /v1/cart/add · Session Event

Cart → Checkout Entry

POST /v1/checkout/init

Payment Selection + Auth

POST /v1/payment/authorize · Tokenized

Gateway Processing

gRPC · Multi-Gateway Failover

Order Confirmation

Webhook · Event Emit

Enterprise-shipped

03 / Data Platform

Payments Sales Analytics
Data Platform (CRM → Redshift)

Enterprise Payments Sales organization requiring trusted, reusable sales data across multiple Lines of Business.

What was happening

Sales data was fragmented across CRM objects with inconsistent definitions. Each team maintained their own Excel reports, and there was no single source of truth for sales performance, pipeline health, or forecasting.

What I owned

I defined the data product strategy, designed the ingestion pipeline architecture, specified the analytics-ready data models, and built the RBAC/IAM governance framework that controlled access across sales, analytics, and ML teams.

The decision I had to make

We had budget for either more features or data reliability engineering. Usage data showed that trust, not features, was the primary adoption blocker — teams were not using the platform because they did not believe the numbers. I deprioritized three feature requests to invest in data quality monitoring, lineage documentation, and automated anomaly detection. Active usage increased in the following quarter.

What I learned

For data products, trust is the feature. The governance model we built — multi-tenant RBAC with row-level security — was later adopted by two additional platform teams as a company-wide standard, which I had not anticipated when I scoped the initial work.

Problem

Sales data fragmented across CRM objects with inconsistent definitions, manual reporting effort, and limited reuse for analytics and ML models.

Solution

Built a governed analytics data product by defining metadata, data dictionaries, ingestion requirements, and analytics-ready models in Amazon Redshift.

Impact

Established a single source of truth for Payments sales reporting
Reduced manual reporting effort and clarification cycles
Improved adoption and trust across Sales, Analytics, and ML teams

Technical Focus: CRM ingestion · Governed analytics models · RBAC-secured data access

Analytics Data Platform — System Topology

CRM Source Systems (Salesforce)

REST · Bulk API v2

Ingestion Layer + Metadata Catalog

Airflow DAG · Schema Registry

Data Modeling + Transformation

SQL · dbt-style Modeling

Amazon Redshift (Analytics Store)

Columnar Store · Row-Level Security

RBAC-Secured Analytics Views

IAM Policy · View-Level Grants

Sales / ML / Analytics Consumers

Career Overview

Where I Have Led Product Work

A high-velocity advancement track across platform, data, and AI-native products — with hands-on engineering fluency as the unfair advantage.

Apr 2024 — Present

J.P. Morgan Chase & Co.

USA

Technical Product Manager

Lead platform strategy for a large-scale, multi-tenant onboarding system serving over 180 enterprise accounts.

Key Outcomes

Scaled the platform from 45 clients (15 products) to 180+ clients (45 sub-products) across 15 back-office integrations (Wires, ACH, RTP, Check Deposits) by directing a team of 18 engineers and 4 consultants through three major release cycles
Cut engineering tickets 80% by delivering a generative AI authoring tool from concept to production, enabling non-engineers to manage complex configuration changes directly
Reduced processing latency from 72 hours to under one hour and cut error rates 90% across the network by defining multi-tenant API contracts and schema governance
Eliminated redundant engineering builds across 68 distributed teams by architecting a unified, reusable schema layer enabling a "publish once, reuse everywhere" model
Enabled dynamic, client-aware request routing by building a decoupled, event-driven orchestration layer within the Catalog that directs onboarding requests to the correct validation and approval paths based on client type and product mix
Drove adoption across 1,500+ platform users through structured experimentation, telemetry analysis, and iterative rollout planning
Secured engineering headcount across three consecutive release cycles by presenting roadmap and risk updates directly to senior leadership each quarter

Platform Digital AI STP Automation Governance

Aug 2022 — Mar 2024

J.P. Morgan Chase & Co. – Product Sales

USA

Technical Product Manager, Data Platforms

Built a governed, cloud-native data platform to replace a fragmented manual reporting infrastructure.

Key Outcomes

Established a single source of truth for the sales organization by designing an end-to-end data pipeline from source systems into Redshift and the analytics layer
Translated requirements from 10+ sales leaders and data science stakeholders into a spec covering data ingestion, modeling, and access control through structured discovery sessions
Standardized governance across two additional platform teams by designing a multi-tenant RBAC/IAM and row-level security framework later adopted company-wide
Drove a 35% lift in daily active usage (DAU) by prioritizing data reliability engineering over new feature development after usage data revealed trust — not features — as the primary adoption blocker

Data Platform Analytics Product Governance & Security AI Readiness Sales Enablement

Oct 2021 — Jun 2022

Yum! Brands (USA)

USA

Product Manager

Owned the checkout experience for KFC, Pizza Hut, and Taco Bell's digital ordering platform, supporting over 50 million transactions annually.

Key Outcomes

Cut Mean Time to Detection (MTTD) for payment gateway anomalies 85% by collaborating with engineering to build a real-time analytics diagnostics dashboard
Directly informed the engineering release roadmap by instrumenting the entire checkout funnel and running systematic A/B tests on error handling and retry logic
Isolated root causes of checkout abandonment by instrumenting semantic telemetry and funnel analytics by device, browser, and payment type

Digital Analytics Payments Experimentation

Jun 2020 — Oct 2021

Reliance Jio (India)

India

Data & Analytics Engineer

Built predictive models and automated reporting pipelines for marketing and sales teams, replacing manual Excel-based tracking.

Key Outcomes

Cut manual reporting time 30% by building predictive lead-scoring models and automated ETL pipelines (SQL, PySpark, Airflow), transitioning sales leaders off manual Excel tracking
Moved teams from ad-hoc to standardized, automated reporting by developing KPI hierarchies and scorecards for marketing and sales stakeholders
Identified higher-potential markets by analyzing early registration and free-trial conversion data, giving regional managers a clearer basis for sales prioritization
Improved local launch decisions by translating usage and adoption data into recommendations alongside sales, network operations, and product teams

Automation Analytics Data Products

Jan 2019 — Jan 2020

Dell Technologies

India

Business Development Intern

Supported TAM/SAM sizing and territory planning for the regional sales team.

Key Outcomes

Improved CRM stage hygiene and forecast accuracy by supporting TAM/SAM sizing and territory planning, building weekly pipeline and forecast views in Excel and Power BI
Strengthened sales messaging by producing win/loss analyses, battlecards, and ROI inputs for the regional sales team

Go-to-Market Analytics

Education

Executive MBA

Business Administration

University of the Cumberlands

Leadership Strategy

Williamsburg, KY

MS

Business Analytics

University of Louisville

Analytics Data Science

Louisville, KY

B.Tech

Computer Science

Bharath University

Engineering CS

Chennai, India

Independent R&D

Independent AI R&D

Solo builds used to stress-test model behavior, agentic system design, and AI governance outside of managed engineering teams — kept separate from enterprise case studies to maintain appropriate scale.

Prototype

R&D-01 / Sandbox

Feedback-to-Backlog AI Copilot
Independent Build · Python + Streamlit + Gemini API / RapidFuzz / Jira REST · 2026

A working AI-assisted pipeline that turns messy customer noise from support tickets, calls, and reviews into a ranked, explainable product backlog — with full provenance, transparent RICE scoring, and optional Jira Cloud sync. Runs entirely without API keys; Gemini, semantic matching, and Jira integrations are optional.

Problem

Product managers receive feedback through disconnected channels (support tickets, calls, reviews), and important signals get lost because manually converting that feedback into a prioritized backlog is slow and inconsistent.

Solution

Built a pipeline that extracts the core issue from raw tickets, merges duplicates via layered RapidFuzz + optional Gemini embedding matching, scores every item with a fully traceable RICE formula, and syncs reviewed backlog items to Jira through the REST API.

Impact

Measured evaluation harness on a labeled 40-ticket gold set: 100% extraction accuracy and 100% dedup precision in the credential-free baseline
Explainable drag-to-reorder re-ranking adjusts Confidence by a fixed 5 percentage points per position and logs every reviewer change, with one-click reset to the AI baseline
Full provenance via SQLite links every backlog item back to its source feedback, with guardrails that retry and safely skip malformed AI output
Bulk Jira sync processes reviewed items independently so a single failure never blocks the batch

Technical Focus: LLM-assisted extraction with deterministic fallback · Layered semantic deduplication · Transparent RICE prioritization · Jira Cloud REST sync · Streamlit dashboard · Python

View live demo · View source · Dataset source

Feedback-to-Backlog — Pipeline Architecture

Raw Feedback (Tickets, Calls, Reviews)

AI Extraction (Gemini / Local Fallback)

Deterministic Metadata Extractor

Layered Deduplication

RapidFuzz + Optional Gemini Embeddings

Transparent RICE Scoring Engine

Reach × Impact × Confidence ÷ Effort

PM Review + Explainable Re-Rank

Jira Cloud Sync (Bulk, Fault-Tolerant)

Prototype

R&D-02 / Sandbox

ApexApply — Autonomous Agent Benchmarking
Independent Build · Python + Gemini API / Ollama / xAI · 2026 – Present

A solo-built benchmarking environment measuring token efficiency, tool-calling latency, and multi-step state machine performance across model families — reading live web pages, classifying form fields by intent, and completing multi-step workflows end-to-end.

Problem

Repetitive, multi-step web forms are slow to fill out manually, and most automation tools break the moment a form's structure or field labels change. Model choice also materially affects tool-calling reliability and latency.

Solution

Built an agent that reasons over dynamic web interfaces in real time, classifies UI elements by intent rather than fixed selectors, and benchmarks token efficiency and tool-calling latency across Gemini API, Ollama, and xAI.

Impact

Persistent memory layer keeps the agent's context intact across long-running, multi-stage workflows instead of losing track halfway through a form
Comparative benchmarking across model families (Gemini, Ollama, xAI) on tool-calling latency and multi-step state machine reliability
Full four-module system architecture built solo, including logging, state management, and a review dashboard for tracking agent performance

Technical Focus: Agentic AI design · LLM tool-calling · Real-time UI classification · Persistent memory architecture · Python + Gemini API, Ollama, xAI

ApexApply — Agent Architecture

Live Web Page / Dynamic UI

Real-Time Element Classifier

Intent Classification · No Fixed Selectors

LLM Reasoning Layer (Gemini, Ollama, xAI)

Tool-Calling · Cross-Model Benchmarking

Persistent Memory Store

State Machine · Context Retention

Multi-Step Action Executor

Review Dashboard + Logging

Prototype

R&D-03 / Sandbox

OKF ContextGate — Deterministic AI Trust Gateway
Independent Build · Python + FastAPI + DuckDB + Streamlit · 2026

A governance layer that sits between an AI system and its knowledge base, deciding what an LLM is allowed to know before it generates an answer — built against Google's Open Knowledge Format (OKF), published weeks prior, which standardizes trust metadata but stops short of enforcing it.

Problem

Standard retrieval finds text that sounds relevant but has no reliable way to check whether it's current, verified, deprecated, or contradicted by a newer definition — so the same question can get three different confident answers from three AI systems.

Solution

Built a policy engine that reads OKF trust metadata and issues a deterministic ALLOW / WARN / BLOCK / ESCALATE decision on every fact before it reaches the LLM, with SQL execution attested against an approved query and every decision logged to a tamper-evident, plain-language receipt.

Impact

Deterministic policy engine tested across 7 controlled failure scenarios (deprecated metrics, stale data, conflicting definitions, tampered SQL, unsupported claims) — same input always produces the same, explainable outcome
100% of requests produce an audit receipt; 0 factual claims returned without evidence in testing
Fully model-independent: governance and generation are decoupled, so swapping LLM providers never changes what counts as trustworthy
Fail-closed by design — escalates or refuses on insufficient or conflicting evidence rather than guessing

Technical Focus: Deterministic policy engine · OKF trust-metadata enforcement · DuckDB SQL attestation · FastAPI backend · Streamlit UI · Model-independent generation adapter

View live demo · View source · Full README

ContextGate — Enforcement Pipeline

Question Asked

OKF Concepts Retrieved

Freshness · Verification · Lifecycle State

Policy Engine Scores Each Concept

ALLOW · WARN · BLOCK · ESCALATE

Approved SQL Runs in DuckDB

Executed Query Attested vs. Approved Query

LLM Writes Final Wording Only

Claims Verified + Receipt Written

Credentials