Hi, I’m Raagini
I’ve been designing conversational experiences & client solutions through
NLP-powered AI Products.
I now manage & design Analytics Products to drive AI optimization.
Why invest in design for your AI product?
Brian Reed was spot on when he said “Everything is designed. Few things are designed well.”
Design is a part of everything, every single thing. So it’s a no-brainer, good design makes products useful, usable, and used. I’d hate to see good products go to waste because of bad design.
My expertise is in the digital product space, specifically in Conversational AI. I specialise in managing AI products & solutions.
AI Product Management & Design:
My expertise
Crafting the product roadmap to align with organizational objectives and market dynamics. Serving as a cross-functional leader, fostering collaboration among teams throughout the product lifecycle. Analyzing metrics and iterating strategies to achieve continuous growth, maximizing product adoption and user satisfaction.
Creating intuitive and visually appealing product designs that align with both user needs and the brand’s essence. Excelling in UX, wireframing, prototyping, and rigorous usability testing. Driving through customer advocacy, actively gathering and leveraging feedback to optimize user experiences and enhance overall product value.
Improving user engagement by uncovering the problem space, and understanding user behavior & needs, thereby arriving at usability outcomes. Initiate Discovery by mapping business goals to user needs, culminating in the creation of design artifacts that guide the Design process, eventually leading to the design of conversational diagrams that illustrate user journeys.
Conducting Natural Language discovery, defining the intent framework and disambiguation strategies, scoping out use-cases, deciphering algorithm intricacies, training classification models, and maintaining models to optimize performance across training iterations. Maximizing efficiency through Generative AI (LLMs).
PRODUCT MANAGEMENT
I take forward my experience in delivering complex AI solutions in a customer-facing role to productizing optimization techniques for a product suite of AI & Analytics tools, with an Agile approach.
Managing the functional and business aspects of the product along with leading UI/UX design gave me the opportunity to find the sweet spot where business goals and user needs overlap.
Product Strategy & Vision
Requirement Gathering
Market Research & Analysis
Feature Prioritization
Planning & Development
User Stories
Roadmap Management
Cross-Functional Collab
Stakeholder management
Performance Analysis
Highlights from my Product Management journey
Leading the development and growth of products within the Quality Management & Analytics suite for CCaaS (Contact Center as a Service) offerings.
Key Responsibilities:
- Define product strategy and roadmap based on market research and customer feedback.
- Collaborate with cross-functional teams (engineering, marketing, sales) to drive product development and go-to-market strategy.
- Ensure alignment with company goals and objectives.
Key Achievements and KPIs:
- User Growth and Adoption:
- Metric: Monthly Active Users (MAU) increased by 29% within 6 months of feature enhancements released.
- Context: Implemented reporting enhancements based on behavioural analytics and made dataset revisions based on customer research feedback.
- Result: Improved user engagement and retention, with MAU growth contributing to a 5.6% increase in product-driven revenue through up-sell opportunities, with a targeted 20% direct growth in revenue once we move to consumption-based pricing.
2. Feature Adoption and Impact:
- Goal: Adoption of a feature with historically low-usage increased by 40% within 3 months of launching a low-effort, high-impact cross-functional connector build.
- Context: Conducted in-depth research and led workshops to understand the need for a service connecting two independent products within the suite.
- Result: Improved overall user engagement metrics, with increased session durations and frequency of feature usage.
3. Market Expansion and Revenue Growth:
- Metric: Increased TAM by repackaging existing products, bringing in contracts with an average value of $500K within the quarter it was launched.
- Context: Revised packaging strategy to allow customers more flexibility in purchasing our SaaS offerings.
- Result: Diversified customer base and expanded market reach, driving incremental revenue and market share gains.
4. Team Collaboration and Leadership:
- Metric: Improved Scrum team metrics reflecting improved team efficiency and effectiveness, quality of customer support, and delivery of value.
- Context: Put Agile methodologies in practice and streamlined communication channels across teams.
- Result: In 3 months, backlog health doubled from 72% to 145%, say:do improved 28% and the resolved defect ratio went from a consistent 1.5-2.- down to 0.75-1.
PRODUCT
DESIGN
As an experienced Conversational Experience Designer, Product Design seemed like a natural extension.
With a strong foundational knowledge of implementing UX design & research methodologies to AI Products, leading Product Design gave me the exposure to round off my skills on UI/UX and visual design, as a practioner and design mentor.
User Research & Insights
Product Ideas Management
Prototyping & Wireframing
User Interface Design
Stakeholder Management
Quality Assurance & Testing
Accessibility and Inclusivity
Feedback Integration
User Experience Flows
Trend & Tech Awareness
Making Design Decisions: A Case Study in Product Design
Walk through my design process to redesign the landing page for a
Bot Optimization product
Background
The landing page for the Bot Opimization product is a dashboard meant to provide a quick snapshot on the bot’s performance and what’s causing for shortcomings in performance. The dashboard should cater primarily to executives & managers of bot programs.
The Problem
The dashboard in production had an overload of information, with all metric details presented upfront, and only covered high-level details on bot performance metrics, which are all informational not actionable. At a quick glance, it was tough to know where the bot performance lacks and why.
Challenge
Part of the dashboard had to be redesigned to allow the target persona to know what’s wrong, how severe the issue is and what needs to be fixed, so they can follow up with analysts to understand what’s causing the issues leading to performance gaps. The changes had to be made with limited dev bandwidth, while working within the product’s design system.
Solution
To understand why the dashboard in production felt incomplete, product demo notes were studied to understand what the target persona cares about. Knowing that the potential buyers (i.e. executives) and product advocates (i.e. program managers) focus on performance metrics, program ROI, and issues causing lower ROI, the dashboard redesign project commenced.
CONVERSATIONAL UX DESIGN
As a Conversational UX Designer, I have led conversational automation engagements for enterprise clients across industries like Banking, Insurance, Retail, Telecommunications, etc.
As a design leader, I have formulated training courses and mentorship programs to introduce design maturity in the organizations I’ve worked for.
Discovery Workshopping
Design Strategy
Use Case Scoping
People Management
Customer Journey Mapping
Stakeholder Management
Conversational Architecture
Dialog Design (UX Writing)
Conversation Flow Design
Conversation Data Analysis
Design Framework
Discover • Define • Ideate • Develop • Test • Analyze
The design framework I follow provides high-level visualization of the design process from start to go-live and beyond. I use it to organize the information and ideas of the problem, enabling me to work on it more effectively.
Putting the framework in action
Applying UX frameworks to the discipline of Conversational AI Design is unique in that it offers an opportunity to take a data-driven approach in making design decisions and defining design rationales. How to best leverage conversational data points when designing or auditing experiences is covered in the case studies below.
“To comply with my non-disclosure (confidentiality) agreements, I have obfuscated classified information in the case studies.
The designs in the portfolio are a reinterpretation of the original.”
Conversational Discovery & Design Process for a New Implementation
Experience Design Audit for an Existing Implementation
Leveraging User Feedback & Conv Data for Design Enhancements
NLU
DESIGN
With an educational background in Analytical Linguistics and Communication, I have worked as a Cognitive Linguist and AI/NLP Consultant for different modules of NLP & ASR.
My approach to NLU Design is at the intersection of linguistics, user needs, and behavioural psychology, with language being the medium of need-expression.
NLU Architecture
Data Curation
Intent Recognition
Data Sourcing
Entity Recognition
Data Annotation
Classifier Training
Test Case Generation
Classifier Maintenance
NLG Templating
The framework for natural language recognition I follow provides a well-structured & organized intent recognition strategy from start to go-live and beyond. I use it to organize disambiguation guidelines, enabling me to build and maintain NLP models more effectively and efficiently.
NLU Framework
Scope • Discover • Architect • Curate • Train • Test • Optimize
Putting the framework in action
The framework for natural language recognition I follow provides a well-structured & organized intent recognition strategy from start to go-live and beyond. I use it to organize the information and disambiguation guidelines, enabling me to build and maintain NLP models more effectively and efficiently.
- Defining the Intent scope
In defining the intent recognition scope for the virtual assistant, I define what knowledge we want the virtual assistant to have and how we want our virtual assistant to act on that knowledge. Scoping the intents helps formulate the distinction between use cases that are in-scope v/s out-of-scope. In defining the scope for the project and the intents, I end up with clear boundaries for each use case.
- Conducting Natural language discovery
In uncovering natural language trends of the user group our solution caters to, I uncover our users’ mental model and natural language tendencies. The more nuanced that understanding is, the better our training of the virtual assistant would be. Discovering how intents are (likely to be) expressed has a direct impact on the virtual assistant’s ability to learn and perform.
- Creating Intent Recognition Architecture
In shaping intents and forming strategies for disambiguation, I create a matrix of use-cases and intents, offering a high-level overview of intents and use-case mapping, expected volume, priority, and NLP layers aiding recognition. The architecture helps keep all stakeholders on the same page around the NLP strategy and helps maintain control as use-cases are added/removed/changed.
- Curating data
In sourcing, cleaning, modelling, and annotating language data, I curate what we need to train, validate, and test classifier models with. I use a variety of sources to collect the data — transcripts, recordings, subject matter experts, agents, crowdsourcing platforms, etc. The labelled corpus is created as per the intent recognition architecture.
- Training classifiers
In getting the training data ready, I take into consideration multiple aspects like linguistic analysis of the data, preprocessing, feature engineering, and choosing the right algorithm to train the data with. The classification model that gets created depends heavily on the algorithm, training parameters, type & quality of data.
- Testing & optimization
In deploying the model, trained against a fixed validation set, I work towards improving intent recognition and avoiding regression to attain a model that is well fit and balanced. I work towards optimizing the model’s performance by using unique test data to measure it against and analyze gaps. This is done consistently from the development stage to
go-live and beyond.
A sneak peak into the intent matrix for use cases developed on a proprietary NLP tool.
The matrix is designed to cover three main elements:
Stucture, scope, and detection mechanisms.
We meet again :)
How to pronounce my name, you ask?
r aa g ih n ee
/ˈrɑːɡəni/
With 6+ years of design and management experience in Conversational AI Products, I have always found myself working on innovative, industry-disruptive technologies.
Time for a quick backstory?
While doing my undergraduate studies in English literature, I was inclined to pursue academic research. So, I naturally gravitated towards attending graduate school for a Masters of Arts in English.
I graduated with a dual major in Linguistics and Communication Studies, with distinction, and an offer to contribute towards AI model development from Samsung Research and Development.
And the rest, as they say, is history!
2021 - PRESENT
Product Design Manager, Wysdom.AI
2020 - 2021
Lead Conversational AI Designer, Acronotics
2019 - 2020
AI/NLP Consultant (Independent Consulting for a NY startup)
Confidential by NDA
2018 - 2020
Conversational Experience Designer, Amelia
2017 - 2018
Language Expert (Analytical Linguistics), Samsung R&D
Tools & Applications
JIRA Project Management, JIRA Product Ideas Management, Figma, Figjam, Amplitude, Productboard, Lucidcharts, Miro, Amelia, Google Dialogflow, Salesforce Einstein, Microsoft PVA, Kore.ai
Accomplishments
Specialization in
Creativity & A.I. from Parsons School of Design, New York
(2022)
Speaker at National Open University, India for a programme on Artificial Intelligence and it's applications in ICT industries
(2022)
Panelist at Conversational Collective’s ConvUX Conference for a session on “Getting Technical with Conv AI (2021)
Featured as a leading #WomanInAI, through an initiative run by Amelia.AI to recognize the contribution of women in STEM roles (2020)
Say hi!
Looking to hire? Up for a coffee chat?
Interested in discussing everything about AI disruptions, great products and innovative solutions?
Let’s start a conversation.
chadharaagini@gmail.com
Toronto, Ontario
Canada
