Ajita Agrawal

Product Manager

Bengaluru, Karnataka, India2 yrs 11 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Led AI-driven product initiatives with significant impact.
Achieved 99% reduction in query SLAs across projects.
Expert in 0→1 product builds and cross-functional alignment.

Stackforce AI infers this person is a Product Manager specializing in AI-driven SaaS solutions with a strong focus on user engagement.

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Skills

Core Skills

Product ManagementProduct Strategy

Other Skills

AI assistantAgile DevelopmentAgile MethodologiesAnalytical SkillsBRDBusiness GrowthBusiness-to-Business (B2B)CRM captureCanvaCommunicationCompetitive AnalysisCompetitive LandscapeConference SpeakingContingency PlanningConvolutional Neural Networks (CNN)

About

Product professional with 1.5+ years of experience leading AI/LLM-driven product initiatives across high-scale B2B SaaS and marketplace platforms. Spearheaded 4+ internal tools and AI copilots, driving 99% reduction in query SLAs and 22% uplift in lead conversions. Proven expertise in 0→1 product builds, data-led GTM, and cross-functional alignment across teams servicing 50K+ users.

Experience

2 yrs 11 mos

Total Experience

8 mos

Average Tenure

11 mos

Current Experience

Airtel digital

Associate Product Manager

Jul 2025 – Present · 11 mos · Bengaluru, Karnataka, India

Spyne

Associate Product Manager

Jan 2025 – Jul 2025 · 6 mos · Gurugram, Haryana, India

● Launched Spyne GPT, an AI assistant using LLMs & NL2SQL for internal data querying via NL; cut resolution time by 99.9%.
● Engineered listing intelligence using OpenAI APIs, auto-generating USPs to enrich dealer listings; boosted engagement across platforms.
● Designed & shipped Common Iframe, a visual delivery layer integrating images, 360° spins and videos ; reduced delivery SLA by 99.65%, enabling upsells.
● Built auto-refreshing dashboards (Metabase + Replit) to replace manual reports, accelerating decision-making across 5+ functions.
● Embedded CRM capture, engagement analytics, and media scoring into platform; drove 22% lift in lead conversion and insights.
● Accelerated product delivery by 5x through clear documentation, agile execution, and streamlined task management in Jira.
● Rolled out AI image scoring for QC teams, flagging poor uploads & cutting review effort by 85% across 50K+ media items.

AI assistantLLMsNL2SQLOpenAI APIsdashboardsCRM capture+6

Switch to product

Product Management Fellowship

Mar 2024 – Aug 2024 · 5 mos

Shiprocket

Product Intern

Feb 2024 – Jan 2025 · 11 mos · Gurugram, Haryana, India · On-site

● Co-led Shiprocket CoPilot, an agentic AI assistant cutting query resolution time from hours to near real time for 30K+ sellers.
● Delivered KAM Assist on ICRM using NL2SQL & curated glossary; reduced query response time by 99%, supporting 100+ predefined
metrics.
● Instituted robust UAT processes & performance tracking, informed feature rollouts that boosted adoption by 10% in beta.
● Championed agentic workflows across cross-functional tools, aligning backend infra and front-end utility for better internal adoption.
● Conducted internal user research & query heatmap analysis to prioritize feature automation, resulting in 15% drop in manual effort.

AI assistantNL2SQLUAT processesperformance trackinguser researchquery heatmap analysis+1

Geeksforgeeks

SDE Intern

Jul 2023 – Feb 2024 · 7 mos · Noida, Uttar Pradesh, India · On-site

● Migrated monolith from PHP to Django, resulting in 30% performance boost and easier maintainability.
● Refactored logic for role allocation and session tracking, enhancing system reliability and UI responsiveness.
● Improved email pipelines for transactional and event-based comms; increased speed by 40%, uptime by 15%.
● Led full-stack development for student doubt forums; enabled 25% reduction in page load & 45% faster query response.

DjangoPHPfull-stack developmentemail pipelinesProduct Management

Motilal nehru national institute of technology

Research Intern

Aug 2022 – Sep 2022 · 1 mo · India

● Evaluated and analyzed the existing and new datasets best suited for emotion detection
● Orchestrated the training of a robust model through 100 epochs, resulting in exceptional accuracy rates of approximately 97% in
CNN, 93% in RF, and 86% in KNN, bolstering the performance and reliability of our predictive algorithms.
● Written a Conference paper titled,“Audio-Video Based Emotion Detection Using Machine Learning” under the guidance of Lt. (Dr.)
Divya Kumar

emotion detectionmodel trainingconference paper writing