BhuvaneshwariThammanaboina

APM · B2B SaaS · AI Products · Analytics · Engagement

I build data-driven, AI products that help users discover, adopt, and stick with what works. 2+ years in B2B SaaS, from LLM support tools to retention analytics.

LLM / AI Products OKRs & Roadmapping Analytics & Metrics User Research Agile / Scrum RICE Framework
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product_metrics.live
−30%
Repeat Queries
↓ AI Triage
+12%
Discoverability
↑ Contextual
−15%
Response Time
↓ Automation
+7%
CTR Uplift
↑ A/B Test
AI / LLM Product Thinking
OKRs & Roadmapping
Analytics & Metrics
Deepta.AI. APMActive
Apxor Tech. 2yr APM✓
Gold Medalist · Rank 4🏅
2+
Years PM
12%
Discoverability
30%
Fewer Queries
15%
Faster Response
20%
Faster Decisions
4+
Products Shipped

My PM Process

How I Build Products

A simple, data-first process I use on every project, from discovery to launch and beyond.

01
Discover
Qualitative + quantitative research. Interviews, funnel analysis, ticket clustering, session recordings.
MixpanelInterviews
02
Define
Problem framing, OKR alignment, success metrics. PRD + MRD with clear acceptance criteria.
NotionOKRsPRD
03
Prioritize
RICE scoring against quarterly OKRs. Impact vs. effort trade-offs with engineering and design.
RICEJIRA
04
Build
Agile sprint planning, backlog grooming, design reviews, cross-functional alignment across cycles.
ScrumSprints
05
Measure
Post-launch KPI monitoring, A/B test analysis, cohort retention, anomaly detection, iteration.
A/B TestsKPIs
📊
Data before opinions
Every decision is backed by real numbers like drop-offs, segments, and cohorts, not just gut feel.
🎯
OKRs as north stars
I tie every sprint goal and roadmap item to quarterly OKRs. If it doesn't move a key result, it waits. Alignment removes ambiguity at execution time.
🔁
Ship small, learn fast
Iterative delivery with tight feedback loops. I prefer a scoped V1 with clear instrumentation over a big-bang launch with no data.
🤖
AI as a product layer
LLMs are capabilities, not features. I define requirements around model confidence thresholds, fallback logic, and human-in-the-loop layers.
👥
Research is ongoing
Discovery doesn't end at launch. I synthesize support tickets, NPS signals, and behavioral data continuously to surface the next opportunity.
🤝
Cross-functional clarity
Detailed acceptance criteria for engineering. Early design reviews to prevent rework. Weekly stakeholder updates to prevent surprises.

Case Studies

Problems I've Solved

Structured PM framework applied to every case: Problem → Research → Solution → Outcome. Click to expand.

AI / LLM Support Automation GTM
LLM-Powered Support Triage System
Automated case sorting, agent help, and self-serve answers for busy B2B support. Cut repeat questions by 30% and first-response time by 15%.
−30%
Repeat Queries
−15%
Response Time
// PM FRAMEWORK · PROBLEM → RESEARCH → SOLUTION → OUTCOME
🔍 Problem
Support teams overwhelmed with repeated, predictable queries. SLA breaches rising. Agents stuck on L1 triage instead of complex cases.
📊 Research
Support ticket clustering, chatbot query log analysis, SLA breach trend tracking, resolution-time heatmaps, agent workload segmentation.
🛠 Solution
MRD for LLM triage: contextual retrieval pipeline, chatbot interaction layer, auto case categorization, agent-assist suggestions, escalation routing.
📈 Outcome
30% reduction in repeated queries. 15% faster first-response. Higher agent satisfaction. AI automation with reliable manual-review fallback.
// Key Product Decisions
  • Defined model confidence thresholds to determine when AI auto-resolves vs. escalates. balancing automation depth with reliability and compliance.
  • Led Agile sprint planning for automated case creation and SLA-based routing triggered via agent dashboards.
  • Mapped chatbot → ticket escalation flows with UX and Ops using Scrum ceremonies, eliminating case diagnosis ambiguity.
  • Prioritized automation roadmap via impact vs. effort analysis aligned to OKRs. highest-value automations shipped first.
  • Co-defined GTM strategy including internal enablement, external messaging, and rollout sequencing for AI product launch.
// OKR Alignment
// Q OKRs · AI Support Product
O: Reduce L1 support load through intelligent automation
  • Reduce repeated queries by 25%+ (achieved 30%)
  • Improve first-response time by 10%+ (achieved 15%)
  • Launch chatbot deflection with 70%+ case categorization accuracy
User Engagement In-App RICE
Contextual Engagement to Drive Feature Adoption
Identified low feature discoverability as a growth ceiling via funnel analysis, then shipped FAB Icons, Embedded Cards, and Story Templates to guide users contextually.
+12%
Discoverability
+7%
CTR Uplift
// PM FRAMEWORK · PROBLEM → RESEARCH → SOLUTION → OUTCOME
🔍 Problem
Users not discovering high-value features despite them being built. Funnel showing drop-offs at key moments. Growth ceiling from poor in-product guidance.
📊 Research
Funnel drop-off analysis, session behavior tracking, feature usage segmentation, heatmaps, and user interviews to pinpoint discovery failure points.
🛠 Solution
RICE-prioritized contextual engagement suite: FAB Icons for guided discovery, Embedded Cards for in-context education, Story Templates for interactive onboarding.
📈 Outcome
12% feature discoverability lift. 7% CTR uplift via contextual delivery. Validated via A/B testing across user cohorts across multiple sprint cycles.
// RICE Prioritization Rationale
  • Reach: All active users hit the discovery gap. high reach score made this top priority over niche requests.
  • Impact: Discoverability directly correlates to retention and expansion revenue. scored 3/3 on impact.
  • Confidence: Funnel data + user interviews gave 85%+ confidence in root cause before building.
  • Effort: 3 components built as a suite sharing a rendering layer. lower total effort than 3 independent builds.
Analytics Retention Platform V2
Retention Analytics V2. Deep Behavioral Insight Platform
Rebuilt the analytics platform with frequency curves, event-property breakdowns, cohort saving, and 3rd-party integrations. enabling truly data-driven decisions across product, growth, and marketing.
+20%
Faster Decisions
V2
Full Rearchitect
// PM FRAMEWORK · PROBLEM → RESEARCH → SOLUTION → OUTCOME
🔍 Problem
Product, growth, and marketing teams lacked granular retention visibility. V1 analytics too surface-level for actionable insights or cohort-level decisions.
📊 Research
Stakeholder interviews across product, growth, marketing. Audit of V1 gaps vs. competitor tools. Usage analysis of abandoned V1 workflows.
🛠 Solution
V2 with frequency curves, event-property breakdowns, custom filters, cohort saving, Metric Trees, Alerts, Annotations, and 3rd-party integrations.
📈 Outcome
20% faster product decisions. Full adoption by product, growth, and marketing. Cohort saving became the most-used feature post-launch.
// Key Capabilities Defined
  • Frequency Curves. revealed behavioral cadence patterns invisible in simple retention tables, unlocking new re-engagement strategies.
  • Event-Property Breakdowns. allowed teams to segment retention by any event attribute, enabling hypothesis-driven analysis.
  • Cohort Saving. reduced repeated setup time for recurring analyses; became the most-used feature post-launch.
  • Metric Trees + Alerts. gave leadership a single pane of glass for KPI monitoring with automated anomaly detection.
  • 3rd-Party Integrations. unified data from fragmented tools into one view, eliminating manual cross-tool reconciliation.

Skills & Expertise

My PM Toolkit

// Core Competencies
OKRs & Roadmapping
AI / LLM Product Thinking
Agile & Sprint Planning
PRD / MRD / User Stories
Analytics & Metrics Definition
User Research & Discovery
A/B Testing & Experimentation
Go-To-Market Strategy
// RICE Prioritization (Live Example)

// RICE = (Reach × Impact × Confidence) / Effort

Contextual Engagement SuiteRICE: 840
Retention Analytics V2RICE: 720
LLM Triage AutomationRICE: 680
In-App Design BuilderRICE: 560
// Product Management
JIRAClickUpNotionLovableSnov.ioLemlist
// Analytics
MixpanelAmplitudeWebEngageExcel
// Methodologies
AgileScrumRICEOKRsA/B TestingSprint Planning
// LLM Workflow Architecture. Support Triage System

// PRODUCT ARCHITECTURE · Deepta.AI Support Automation · Zammad (Open Source Ticketing)

USER QUERY
User Query Input
Customer submits query via chatbot or support portal
↓
MODERATION LAYER
Filters unsafe, spam, or restricted content
Content safety check before any LLM processing
↓
INTENT DETECTION
Identifies user intent and request type
Classifies: billing / technical / feature request / escalation
↓
RAG. RETRIEVAL (OPTIONAL)
Fetches relevant KB articles, policies, history
Contextual retrieval pipeline from knowledge base
↓
LLM PROCESSING
Generates contextual response
Uses query + retrieved data + conversation history
↓
CONFIDENCE CHECK
HIGH CONFIDENCE
Auto-response → Zammad ticket
LOW CONFIDENCE
Escalate to human agent
↓
ACTION LAYER. ZAMMAD
Auto-categorize ticket Route query Send response Trigger workflow/API
↓
SATISFACTION SURVEY
Chatbot satisfaction check
User rates resolution → feedback loop into model improvement
👍 Satisfied 👎 Not satisfied → re-escalate

PM Teardowns & Thinking

Product Analysis

How I think about product: teardowns, frameworks, and lessons from the field.

🤖
AI Product Thinking
Why LLM products need a "confidence architecture"
Most AI product failures aren't model failures. they're product failures. How I think about thresholds, fallbacks, and human-in-the-loop design.
5 min readRead article →
📊
Analytics Teardown
Mixpanel vs Amplitude: a PM's breakdown
Having used both heavily. how I choose between them based on team maturity, product stage, and the questions you're actually trying to answer.
7 min readComing soon →
🎯
OKR Framework
How I write OKRs that actually change what gets built
Most OKRs are either too vague or too output-focused. A framework for writing key results that align sprint work to real business outcomes.
6 min readComing soon →
🔍
User Research
Teardown: How Intercom does in-app onboarding
A structured PM teardown of Intercom's onboarding flow. what works, what doesn't, and what I'd change based on behavioral design principles.
8 min readComing soon →
🚀
GTM Strategy
What I learned co-defining a GTM for an AI product
The product was ready but the market wasn't. Lessons from building GTM infrastructure, enablement assets, and rollout sequencing for a B2B LLM product.
6 min readComing soon →
📈
Metrics
The metric that saved our retention roadmap
We were optimizing the wrong number. How switching from 30-day retention to frequency curves completely changed our prioritization and what we shipped next.
5 min readComing soon →

About Me

Engineer. Customer. Product Builder.

I started as a Civil Engineering gold medalist (4th university rank, CGPA 8.61 at Osmania University) before deliberately pivoting into product. a path that gave me something rare: systems thinking from engineering, customer empathy from CS, and product strategy built on both.

My journey from Associate CSM → Associate PM gave me a full-stack view of product: I've sat with frustrated customers, written the PRDs that fix their problems, and tracked the KPIs that prove it worked.

I gravitate toward AI products where defining the right human-machine boundary is the hardest design decision, and analytics platforms where the challenge is making data actionable, not just visible.

// MY CAREER OKRs · 2025–2026
O: Become a full-stack PM who ships AI products at scale
  • Lead 2+ AI product initiatives with measurable post-launch outcomes
  • Develop deep LLM product architecture literacy (confidence layers, RAG, evals)
  • Contribute to GTM for at least one 0→1 product launch
O: Build in public. share PM thinking with the community
  • Publish 6 PM teardowns / analytical essays in 2025
  • Grow LinkedIn following by sharing data-driven product frameworks
B.E. Civil Engineering
Matrusri Engineering College, Hyderabad · Osmania University · 2019–2023
🏅 Gold MedalistCGPA 8.61Rank 4
Intermediate (TSBIE)
Nano Junior College · 2017–2019
89.7%
// CAREER JOURNEY
Sept 2025 – Present
APM · Deepta.AI. AI products, LLM triage, GTM
Sept 2023 – Sept 2025
APM · Apxor. Engagement, analytics, Retention V2
Mar – Sept 2023
Assoc. CSM · Apxor. Customer discovery, product inputs
2019 – 2023
B.E. Engineering · Gold Medal, Rank 4

Get In Touch

Let's Build Something

Open to PM opportunities

If you're building an AI product or a data-driven SaaS, and want a PM who cares about metrics and users, let's talk.

AI Products Analytics Platforms B2B SaaS User Engagement
✉️
Email
bhuvaneshwarithammanaboina@gmail.com
📞
Phone
+91 9553387337
💼
LinkedIn
linkedin.com/in/Bhuvaneshwari
📄
Resume
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