Sarassaras

CTO & Co-Founder · Sept 2024 – Present

A Trust Layer for Smarter Trading Decisions

Saras was built to bring transparency to the noisy world of stock market recommendations — helping retail traders evaluate advisor credibility before acting on recommendations.

Featured on Shark Tank India
Antler-backed · $500K pre-seed
Built for retail traders

150K+

Downloads

200K+

Messages received / day

400

Daily Live trades

98%

Uptime

100K

Registered users

Saras app — trade feed
Saras app — home
Saras app — trade details

Core Engineering

Core Engineering Systems

Flagship realtime systems that power ingestion, execution, and multidimensional market intelligence.

Ecosystem

Platform Ecosystem

Product, growth, operations, and infrastructure surfaces that made the core computation systems usable in production.

Product

Product Surfaces

The mobile and web experiences that make advisor intelligence tangible for retail traders.

Home

App home — discovery entry and live market pulse

Trade

Realtime recommendation stream with advisor context

Filters & Search

Multidimensional discovery across live universe

Trade Details

Targets, stoploss, live P&L, and exit state

Advisor Profile

Credibility, history, and segmented performance

Premium Signal

Subscription surfaces and gated intelligence

Product Walkthrough

A short walkthrough of the Saras product experience, advisor discovery flows, and realtime recommendation surfaces.

Localized Product Experience

The Saras product experience was designed to support multilingual expansion from the beginning using structured localization workflows inside the Flutter application.

UI copy, labels, notifications, and interaction surfaces were mapped through JSON-based language key systems, allowing product surfaces to scale across multiple regional languages without changing application logic.

This enabled faster iteration across bilingual experiences while keeping delivery workflows operationally consistent across platforms.

Designed for scalable regional rollout.

Platform Evolution

How Saras Evolved Into a Production Ecosystem

Saras evolved from a lightweight Telegram recommendation tracker into a realtime multi-source platform focused on advisor transparency, execution realism, and operational reliability.

Phase 01

Early Prototype

Finosauras & Telegram Tracking

Saras initially started as a lightweight Telegram recommendation tracker using approximate market prices from public sources. The early prototype focused on exposing advisor performance publicly through a simple web interface.

Key Themes

Telegram SignalsPublic TrackingAdvisor TransparencyPrototype Infrastructure

Phase 02

Platform Expansion

Expanding Beyond Telegram

The platform evolved into a multi-source ingestion system capable of processing recommendations from research reports, Twitter/X, YouTube livestreams, and financial news feeds.

Key Themes

Multi-source IngestionAI ParsingOCR PipelinesData Normalization

Phase 03

Realtime Infrastructure

Rebuilding the Execution Engine

The first execution engine ran on cron jobs and Lambda functions — matching trades on an interval, minutes behind the market, and breaking in ways that were hard to trace. It was rebuilt as a persistent realtime engine: Redis state management, websocket market feeds, and event-driven processing.

Key Themes

Redis StateWebsocket FeedsEvent ProcessingRealtime Matching

Phase 04

Consumer Platform

Launching the User Ecosystem

After raising pre-seed funding, Saras expanded into a consumer-facing platform with Flutter applications, realtime notifications, advisor analytics, and premium recommendation delivery.

Key Themes

Flutter AppNotificationsPremium FeaturesConsumer Platform

Phase 05

Intelligence Systems

Advisor Intelligence & Market Analytics

Saras introduced multidimensional filtering, advisor ranking systems, and realtime performance analytics designed to help users evaluate recommendation quality and advisor credibility.

Key Themes

Advisor RankingProfit PotentialFiltering EngineAnalytics Systems

Phase 06

Operational Scaling

Moderation & Reliability Systems

As platform scale increased, Saras added operational tooling for trade verification, advisor moderation, notification management, replay systems, and infrastructure reliability.

Key Themes

Operational ToolingReplay SystemsModerationReliability

Conclusion

What Saras Ultimately Became

A trust layer designed to help retail traders make more informed decisions by analyzing advisor credibility, recommendation quality, and realtime execution outcomes across fragmented financial content sources.

Operations

Reliability & Operations Highlights

  • Realtime monitoring across ingestion, execution, and API tiers
  • Redis cluster scaling for ranking and stream workloads
  • ECS migration for containerized, repeatable deploys
  • Automated recovery playbooks and health-checked services
  • WebSocket stability for live trade and price fan-out

A Critical Incident We Overcame

An exposed AWS key led to unauthorized infrastructure deletion. We rebuilt core services within six hours, then introduced scoped credentials, rotation policies, and deployment guardrails to prevent recurrence.

Public Presence

Press, Recognition & Community Presence

Saras evolved into a publicly visible platform through media coverage, startup ecosystems, founder conversations, and community-driven distribution.

Feature

LiveMint

Coverage around retail trading ecosystems and advisor transparency.

Feature

Antler India

Mentioned as part of AI-driven innovation discussions in fintech.

Video

Founder Conversation

Public discussion around building Saras and operational challenges.

Recognition

Featured on Shark Tank India

Pitched Saras on national television — bringing advisor transparency to a mainstream audience.

Platform

Signal By Saras

The platform I architected and laid the foundation for — now live as Signal by Saras.

Credibility

What I Owned

Honest scope of technical ownership and cross-functional collaboration at Saras.

What I owned

  • System architecture across ingestion, execution, and intelligence
  • Hiring and leading the five-person permanent tech team
  • Backend engineering and API design
  • Infrastructure, ECS, and deployment pipelines
  • Redis strategy — streams, sorted sets, and ranking layers
  • Data pipelines and aggregation systems
  • Virtual execution and trade lifecycle engines
  • Scaling, observability, and incident response
  • Operational tooling for moderation and rollout

Collaborated with

  • +Product-driven requirements and roadmap prioritization
  • +Frontend implementation with dedicated app and web teams
  • +Marketing experimentation and growth funnel support
  • +Analyst workflow tooling and backtesting interfaces
  • +Advisor onboarding and verification operations