Our Team
RideWise is built and maintained by Sriram Manoharan, a software engineer who combines a decade of experience in financial technology with a data-driven approach to rideshare pricing.
Founder & Lead Engineer
Sriram is a lead software engineer with over a decade of experience building data-driven platforms. Before founding RideWise, he worked at The Carlyle Group (Lead Software Engineer), Bloomberg (Senior Software Engineer), and Haven Technologies (Full Stack Engineer). He holds a graduate degree from the University of Illinois at Chicago.
After years of manually switching between the Uber and Lyft apps before every ride to find the better price, Sriram built RideWise to automate that comparison. The idea was simple: aggregate the publicly available rate cards that Uber and Lyft publish for each city, apply the same fare formulas the apps use internally, and show riders a side-by-side estimate — without needing to open two apps. What started as a weekend project grew into a comprehensive tool covering 300+ US cities and 47+ airports, with verified rate data for 8 service tiers and time-of-day surge estimates.
Technical Expertise
Education
University of Illinois at Chicago — Graduate College (2011–2013)
Founder & Lead Engineer — RideWise
2025 – Present
Built the rideshare fare comparison platform from scratch — data pipelines for rate card aggregation, pricing estimation engine, and the consumer-facing comparison tool.
Lead Software Engineer — The Carlyle Group
Previous
Led full-stack development supporting global investment operations. Built AI-driven POCs using LangChain and mentored junior engineers.
Senior Software Engineer — Bloomberg
Previous
Worked on financial data platforms using Node.js, TypeScript, and MySQL at scale.
Full Stack Engineer — Haven Technologies
Previous
Contributed to building an end-to-end insurtech platform for life insurance products.
As a software engineer commuting across New York City, I was spending a significant portion of my transportation budget on rideshares. The routine was always the same: open Uber, check the price, switch to Lyft, compare, then decide. It was tedious, and I knew I was leaving money on the table when I didn't bother to compare.
The turning point was realizing that both Uber and Lyft publish their rate cards — the base fares, per-mile rates, and per-minute rates for every city. These are public data. The fare formula is straightforward: Base + (Rate × Distance) + (Rate × Time) + Booking Fee. If I could aggregate these rate cards, I could estimate and compare fares for any route without opening either app.
That weekend project became RideWise. I built the data pipeline to aggregate rate cards from Uber, Lyft, and local taxi commissions across 300+ US cities. The estimation engine applies each provider's published formula to any given route. The result: riders can compare fares from every major provider in one place, instantly.
I maintain the platform myself — updating rate data monthly when providers adjust pricing, validating estimates against real fares, and expanding coverage to new markets. The goal hasn't changed: help riders stop overpaying by making rideshare pricing transparent.
Every RideWise estimate is built on publicly available data, not opinion. I aggregate rate cards from Uber's published fare pages, Lyft's pricing page, and local taxi commission regulatory filings across 300+ US markets. The pricing engine factors in base fares, per-mile and per-minute rates, booking fees, airport surcharges, and time-of-day demand patterns.
I validate estimates against actual fare data and update the rate card database monthly as providers adjust pricing. When academic research is available — such as the January 2026 Johns Hopkins Carey Business School study analyzing 2,200+ rides — I incorporate those findings to refine our methodology and editorial content.
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