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

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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.

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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.

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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.

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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).


I take forward my experience in delivering complex AI solutions in a customer-facing role to productizing optimization techniques for a suite of Bot Operations & 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



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

Creative and analytical thinking concept

Walk through my design process to redesign the landing page for a

Bot Optimization product


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.


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.


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.

How was the final solution formulated?

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Yellow robot with bright conversational technology and smart pho

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

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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.

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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.”

Wireless Router Concept

Conversational Discovery & Design Process for a New Implementation

Online shopping and delivery concept

Experience Design Audit for an Existing Implementation

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Leveraging User Feedback & Conv Data for Design Enhancements



AI (Artificial Intelligence) concept. Communication network.

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

NLU Framework

Scope • Discover • Architect • Curate • Train • Test • Optimize

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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.

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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


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


Specialization in

Creativity & A.I. from Parsons School of Design, New York


Speaker at National Open University, India for a programme on Artificial Intelligence and it's applications in ICT industries


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.


Toronto, Ontario