SaaS ProductModernization
Identify where AI can improve onboarding, discovery, navigation, task completion, reporting, or decision support inside existing products.
User-Centric Agentic Experience DesignUser-Centric Agentic Experience Design
Design AI-Augmented Product Experiences Users Can Trust and Use
AI succeeds in products when users can quickly see value, understand the interaction, and act with confidence. Coditas’ design experts help product teams identify where AI can improve user journeys, reduce friction, guide decisions, and create product experiences that feel clear, useful, and build-ready.
Transform AI Into Product Experience Decision
AI-Driven User-Centric Design turns AI ambition into build-ready product concepts grounded in UX, business value, and feasibility.
Find the Right AI Opportunities
Identify product journeys where AI can reduce effort, guide action, improve decisions, or create a more useful experience.
Make AI Interactions Tangible
Convert product ideas into concept screens, interaction flows, and prototypes that help stakeholders see the future experience.
Connect Design With Build Readiness
Assess data availability, API readiness, Large Language Model feasibility, and integration risks before deeper engineering begins.
What is AI-Driven User-Centric Design?
AI-Driven User-Centric Design is a design-led approach to AI integration for digital products. It helps product teams understand where AI creates real user value, how human and AI interactions should work, and what the future product experience could look like before full-scale development begins.
The focus is specific: AI-augmented product experiences through copilot panels, smart nudges, predictive paths, contextual actions, assisted workflows, and decision-support moments that fit naturally into the user journey.
Who Is This For?
01
Product Organizations Reimagining Existing Experiences
Product-led businesses looking to bring AI into mature products in ways that feel useful, natural, and tied to real user needs.
02
Design Functions Defining AI-Augmented Journeys
Design groups that need to benchmark UX maturity, identify high-value AI moments, and shape future-state product experiences.
03
Technology Leaders Assessing Build Readiness
Engineering and technology stakeholders who need early clarity on data availability, API readiness, Large Language Model feasibility, and integration risks.
04
Business Leaders Prioritizing AI Investments
Decision-makers who need to understand where AI can create measurable user value, stronger product differentiation, and practical business impact.
How Our Agentic Experience Design Engagement Works
Our engagement brings product strategy, UX assessment, AI opportunity mapping, concept design, and technical evaluation into one focused design sprint.
PHASE 1
Assess the Current Product Experience
We review the current product experience to understand usability gaps, friction points, workload issues, feedback gaps, and visual system maturity.
We look at
Navigation, clarity, task friction, cognitive workload, feedback loops, visual consistency, workflow depth, and product complexity.
Outcome
UX maturity benchmark with scored findings and opportunity areas.
PHASE 3
Define the AI Interaction Model
We shape how users will experience AI inside the product.
We look at
Copilot behavior, smart nudges, predictive paths, contextual actions, recommendation patterns, user control points, review moments, and feedback flows.
Outcome
An AI interaction model that explains how users will discover, use, question, and act on AI assistance.
PHASE 5
Assess Technical Readiness and Build the Roadmap
We evaluate whether the proposed AI experiences can move into delivery with current systems, data, and APIs.
We look at
Data availability, API readiness, Large Language Model feasibility, integration risks, dependencies, and build complexity.
Outcome
Prioritized action plan and 6–12-month AI modernization roadmap.
PHASE 2
Identify AI Opportunity Areas
We map where AI can support users inside the product journey.
We look at
High-friction tasks, repetitive workflows, decision-heavy moments, data-rich screens, manual analysis steps, delayed actions, and areas where users need guidance.
Outcome
AI opportunity map with 3-5 prioritized integration points scored by user impact, business impact, and technical feasibility.
PHASE 4
Create Concept Screens and Prototypes
We translate opportunity areas into future-state product concepts.
We look at
Key screens, assisted workflows, AI copilot panels, decision-support moments, contextual prompts, predictive journeys, and prototype flows.
Outcome
Modern UX + AI concept screens or clickable prototype for stakeholder review and alignment.
PHASE 1
Assess the Current Product Experience
We review the current product experience to understand usability gaps, friction points, workload issues, feedback gaps, and visual system maturity.
We look at
Navigation, clarity, task friction, cognitive workload, feedback loops, visual consistency, workflow depth, and product complexity.
Outcome
UX maturity benchmark with scored findings and opportunity areas.
PHASE 2
Identify AI Opportunity Areas
We map where AI can support users inside the product journey.
We look at
High-friction tasks, repetitive workflows, decision-heavy moments, data-rich screens, manual analysis steps, delayed actions, and areas where users need guidance.
Outcome
AI opportunity map with 3-5 prioritized integration points scored by user impact, business impact, and technical feasibility.
PHASE 3
Define the AI Interaction Model
We shape how users will experience AI inside the product.
We look at
Copilot behavior, smart nudges, predictive paths, contextual actions, recommendation patterns, user control points, review moments, and feedback flows.
Outcome
An AI interaction model that explains how users will discover, use, question, and act on AI assistance.
PHASE 4
Create Concept Screens and Prototypes
We translate opportunity areas into future-state product concepts.
We look at
Key screens, assisted workflows, AI copilot panels, decision-support moments, contextual prompts, predictive journeys, and prototype flows.
Outcome
Modern UX + AI concept screens or clickable prototype for stakeholder review and alignment.
PHASE 5
Assess Technical Readiness and Build the Roadmap
We evaluate whether the proposed AI experiences can move into delivery with current systems, data, and APIs.
We look at
Data availability, API readiness, Large Language Model feasibility, integration risks, dependencies, and build complexity.
Outcome
Prioritized action plan and 6–12-month AI modernization roadmap.
Let’s start your modernization together
Start With an AI Modernization Sprint
Validate AI opportunities before full-scale build.
AI Modernization Sprint is a 2-4-week engagement for product teams that want clarity before committing to a larger build. The sprint helps teams assess product experience quality, identify high-value AI opportunities, make future-state concepts tangible, and understand technical readiness.
Why Choose Coditas for AI-Driven User-Centric Design?
Product Thinking Before AI Features
AI should solve a clear user problem within the product journey. Coditas helps teams identify where AI can reduce effort, improve decisions, guide action, or create a better product experience before teams commit to development.
AI-Native Design Engineers
Our AI-enabled design engineers bring deep expertise in product and interaction design to AI-augmented experiences, using modern AI tools to accelerate research, concept development, prototyping, and design validation.
Let’s begin your modernization journey
Design Concepts Grounded in Technical Reality
Future-state concepts need more than strong visuals. We connect product experience, data readiness, API feasibility, Large Language Model fit, and integration risks so design decisions stay build-ready.
Clear Path From Concept to Engineering
The engagement is designed to support deeper AI engineering and build work. Teams leave with prioritized opportunities, tangible concepts, readiness inputs, and a roadmap that helps product, design, and technology teams move in the same direction.
Product Thinking Before AI Features
AI should solve a clear user problem within the product journey. Coditas helps teams identify where AI can reduce effort, improve decisions, guide action, or create a better product experience before teams commit to development.
Design Concepts Grounded in Technical Reality
Future-state concepts need more than strong visuals. We connect product experience, data readiness, API feasibility, Large Language Model fit, and integration risks so design decisions stay build-ready.
AI-Native Design Engineers
Our AI-enabled design engineers bring deep expertise in product and interaction design to AI-augmented experiences, using modern AI tools to accelerate research, concept development, prototyping, and design validation.
Clear Path From Concept to Engineering
The engagement is designed to support deeper AI engineering and build work. Teams leave with prioritized opportunities, tangible concepts, readiness inputs, and a roadmap that helps product, design, and technology teams move in the same direction.
Let’s begin your modernization journey
AI-Driven User-Centric Design Use Cases
Enterprise WorkflowProducts
Design AI-assisted workflows for internal products where users need faster decisions, clearer guidance, and reduced manual effort.
AI CopilotExperiences
Design copilot panels, contextual prompts, guided actions, source-backed responses, and review flows that fit naturally into product journeys.
Predictive ProductJourneys
Create future-state experiences where AI suggests next steps, highlights risks, predicts user needs, or guides users through complex tasks.
Product ExperienceModernization
Benchmark current UX maturity and define a design-led roadmap for better navigation, clarity, workload reduction, feedback, and visual system quality.
Product AI Experience Work in Action
See how Coditas helps product teams identify AI opportunities, design future-state experiences, and create a practical path from concept to build.
Data Science-Driven Sales Forecasting Solution
To optimize sales strategy and maximize cargo vessel capacity, th...
Read Case Study
Migration to Thought Machine's Value Core
The client’s business was functioning on a semi-digital legacy system and needed to be transformed into a fully digital banking service provider.
Read Case Study
Data Evolution with Real-time Sync & Upgrade
Revitalizing outdated database with seamless sync and real-time...
Read Case Study
Driving Sales Operations and Revenue Growth
The client develops sales growth tools and helps create a sales force...
Read Case Study
Optimizing Data Infrastructure for a Fintech Leader
The client is cited as the industry’s largest commercially available...
Read Case Study
Automating Outreach at Scale with AWS-Native GenAI
Boost conversions and cut decision-making time with AWS-native GenAI. Automate call analysis, gain real-time insights, and scale outreach efficiently.
Read Case StudyOur AI Design Insights
Explore practical thinking on AI product design, product modernization, AI copilots, predictive UX, user-centric AI, and design-led AI integration.
The Acceleration Continues: Reflecting on the Key Themes of HIMSS 2026 and How Our Focus Areas Provide a Path Forward
From Full-Stack to AI-Stack: The New Developer Badge
2025: The Year Context Took the Lead in AI Engineering
Why Governance is Critical for Healthcare AI To Succeed
AI's Big Leap in Healthcare in 2026
A Step-by-Step Guide to Automating CloudFormation Deployments Using GitHub
Revolutionizing Prior Authorization with GenAI: A Blueprint for Faster, More Efficient Care Outcomes
The Next Frontier in Healthcare: AI-Powered Connected Ecosystems
Never Lose Uptime Again: Building a Self-Healing AWS Infrastructure with Terraform
Frequently Asked Questions
Build an AI Strategy Your Teams Can Act On
Subhash Verma
Growth Officer
When you win, we win.
Our Offices
DelawareUSA
DubaiUAE
PuneIndia
