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Saas Product
Designing the AI that earns its place in the workflow
Product
Chia — Enterprise Conversational AI Agent
Role
UX Research & Design
Timeline
1 Year
0%
Faster ticket resolution
0%
Support load automated
0%
Team productivity
0%
Less agent onboarding time
What is CHIA?
Chia is Ramco Systems' enterprise-grade Conversational AI Agent — built to transform how support teams handle tickets and multi-step customer workflows. It doesn't replace agents. It works with them.
I owned the end-to-end UX: the agent interface, the workflow configuration system, the AI explainability layer, and the patterns that make human-AI collaboration trustworthy.
" My core challenge was designing a system where agents feel more capable, not replaced - and where trust is earned through transparency, not assumed through accuracy "
Three tensions every enterprise AI create
Through shadowing agents and analyzing support workflows, I found three points where AI adoption breaks.
01
Speed vs Accountability
AI could classify and respond in seconds — but agents were responsible for outcomes. An invisible AI creates invisible liability.
02
Automation vs Trust
When agents couldn't see why a suggestion was made, they either ignored it or blindly accepted it. Neither was safe.
03
Efficiency vs Control
Leaders wanted AI to handle routine cases - but not at the cost of losing the ability to intervene and improve the workflows.
Four principles that shaped every decision
01
Transparency over automation
Every AI action surfaces its reasoning. Agents see why a ticket was classified before they act - not after. Confidence scores and rationale are first-class UI, not buried tooltips.
03
Graceful degradation
When AI confidence is low, the interface escalates gracefully - surfacing uncertainty, routing to human review, and preserving audit trails instead of silently failing.
02
Human control at every step
AI suggestions are never auto-applied. Every recommendation has a clear review → accept / edit / override path. The agent is always the decision-maker.
04
Agents improve the AI
Give agents visibility into every decision the AI made. When they see a mistake, they can go back to the workflow definition and fix it - agents become the trainers.
The complete system
Where agents work, where support teams configure AI behavior, and where agents see the AI's reasoning.
The Agent Interface
The all-in-one working surface. Agents needed everything in one screen: ticket details, conversation history, extracted customer info, actions to take — without context switching.
The challenge: Most helpdesk tools make agents jump between 3-4 windows. This design kept the agent in flow.
Natural Language Workflows (NLW)
The problem we solved: Support teams couldn't define complex AI workflows without engineers. They had workflows in their heads but no way to tell the AI.
V1 (Old): Form-based workflow builder. Teams clicked through form fields, selected options from dropdowns. Non-technical users were lost.
What broke: Teams tried to build simple workflows (e.g., "collect service choice, search salons, show options") and spent hours configuring forms.
V2 → V3 (New): Natural language instructions. Support teams write plain English: "Ask the customer which service they want and assign the answer t
OLD
Agents clicking through form fields. Non-technical users drowning in UI. "I know what I want the AI to do, but HOW do I tell it?"
New
Natural language instructions + visual variable mapping. Support leads can now define complex workflows in plain English that the AI understands.
Audit Log & Explainability
What changed adoption: Transparency. When agents could see every step the AI took to reach its conclusion, they started trusting it.
The audit log shows: which workflow step executed, what data was extracted, what conditions were validated, which API calls were made, what the AI decided, and where it handed off to humans.
The outcome that mattered: Agents didn't just accept suggestions — they used the audit log to identify why the AI made a mistake, then went back to the NLW builder and fixed the workflow themselves.
This created a feedback loop: agent sees error → understands why → improves the workflow → AI gets better → fewer overrides needed.
What the design actually changed
Routine queries automated
Agents improving flows
What designing AI taught me
01
Transparency drives adoption — not accuracy
Agents who could see why the AI made a decision adopted it faster than agents who saw a correct decision without explanation. Understanding matters more than perfection.
03
The audit log is the most important UI
Once agents could see the AI's reasoning, they stopped passively accepting/rejecting. They started actively improving the system. The audit log became a feedback mechanism, not just a compliance tool.
02
Non-technical users need plain language, not forms
The jump from form-based to plain-English workflow definition increased adoption 10x. Support teams thought in workflows, not in form fields. The UI had to match how they thought.
04
The override moment is where trust lives
When an agent reviews an AI suggestion, they need context (why was it classified this way?), agency (can I change it?), and visibility (will this be logged?). Get that moment right, everything else follows.