Ensa Workspace

Agenda

Overview & Role: Project scope and my 100% solo ownership.
The Problem & Market Gap: Identifying the need for accessible financial reports.
Research & The Pivot: Shifting focus from retail to professional users.
UX Strategy & Key Decisions: Core principles for building trust in AI.
Final Solution & Designs: Interface screens and interactive flows.
Technical Execution & Roadmap: System architecture and rollout phases.
Business Impact: Driving MAU and subscription revenue.
Reflections & Takeaways: Lessons learned and navigating tight constraints.

1. Project Overview

An AI-powered research workspace for investors, where users interact with financial reports through natural language, receive AI-driven insights with verifiable citations, and publish living research reports.

My Role & Contributions

As a Senior UX Designer, I operated as the solo designer on this project, taking 100% end-to-end ownership. I acted as the de-facto Design Lead, driving the product vision, conducting research, and managing cross-functional alignment. This demonstrates my readiness and capability to lead complex, strategic AI initiatives.

	Responsibility	Detail
1	End-to-end UX design	From concept, landing experience, to complex workspace interactions.
2	Information architecture	Session management, project organization, document hierarchy.
3	AI interaction patterns	Chat UI, citation navigation, confidence indicators.
4	Cross-functional leadership	Aligning BA, Engineering, and UXR teams to execute the product vision.
5	Research Presentation	Presented comprehensive research to the PO detailing market analysis, competitive landscape, and user journeys.

2. The Problem & Market Gap

Financial reports are inaccessible to most investors.

Pain Point	Detail
Barrier to Entry	Reports are filled with jargon, dry tables, no context.
Lack of Insight	Existing tools extract data and draw charts, but don’t explain meaning.
No Interaction	Users can’t ask questions directly about a report.
Risk of Oversight	Easy to miss red flags, anomalies, and hidden risks.

Competitive Positioning
We aimed to build the “NotebookLM for Financial Reports” specifically for the Vietnamese market.

Competitor	Their Approach	Our Differentiation
FiinPro / WiChart	Data extraction and charts	AI explanation, interactive Q&A, natural language
Google NotebookLM	General-purpose document analysis	Specialized for financial reports with domain expertise
AlphaSense	$10K–$40K/user/year	Affordable, Vietnam market-first, AI-native

Our moat: “Prepared context quality”. How well we pre-process and structure documents before AI ingests them is the real defensibility. Not the AI model itself.

3. Research & Target Users

Phase 1: Retail Investors

Age 22–40, no deep finance background
Overwhelmed by financial report jargon
Want “easy answers” from complex data

Key research insight: Retail users want “cheap & easy” (commoditized by ChatGPT)

Phase 2 (The Pivot): Professionals

Research analysts, advisory teams, brokers
Content publishers who write & distribute research
Clear pain points, willingness to pay

Key research insight: Professional users have distinct workflow pain points and measurable willingness to pay for specialized tools. This forced a full redesign from a simple chat to a multi-session, project-based environment.

4. UX Strategy & Key Decisions

Challenge	Approach	Solution
Multi-session AI conversations	Evaluated navigation models	Drawer-based session management with project grouping (more scalable for power users)
Building trust in AI outputs	Trust pyramid framework	Inline citations with bbox-level source highlighting and confidence indicators
Research vs. Reading	Role-based views	Editor (full workspace) vs. Reader (clean consumption with interaction)
100+ documents in context	Information architecture	Project/section hierarchy, smart search, prepared context

Core Design Principles

1. Dual-Pane Layout

Left is AI conversation, right is original PDF.
Why: Builds trust by allowing users to verify AI claims instantly.

2. Citation-First AI

Every insight links to exact position in source (bbox-level highlighting).
Why: Eliminates “AI hallucination” anxiety.

5. Final Solution & Interface Design

Translating research and strategy into a production-ready workspace optimized for trust and deep analysis.

1. Onboarding Experience
Introducing the workspace value proposition to new professional users.

2. Core Workspace (Dual-Pane Layout)
The primary work environment featuring the conversation on the left and the source document on the right.

3. Citation Navigation (The “Aha” Moment)
When a user clicks an AI citation, the right pane automatically scrolls to and highlights the exact bounding box in the original PDF.

4. Document Management & Intelligence
Handling uploads, project organization, and AI-generated populated data reports.

6. Technical Architecture & Execution

Frontend State Management I Defined:

Service	UX Touchpoint
Agentic Agent Service	Multi-agent AI with streaming responses, chat UI, loading states, error handling.
Data Ingestion Service	OCR, markdown conversion, citation mapping, upload flows, processing feedback.

Product Roadmap

Internal MVP (Q3 2026): Core workspace UI, chat, PDF viewer, citation navigation
Public MVP (Q4 2026): Landing experience, onboarding, report views
V1.0 (Q1 2027): Session management, agent customization, publishing flow

7. Business Impact & Outcomes

Designed to drive subscription revenue by solving high-value workflow problems for financial professionals.

Target MAU (6 months): 1,000+
Monthly Rev Target: 1B VND
Feature Adoption: 80%+
Target NPS: ≥ 50
Revenue model: Subscription (Pro tier ~$40–60/month for professionals).
Business goal: 1,000 expert users × ~1M VND/month = 1B VND/month revenue.

8. Behind the Scenes: Constraints & Lessons

Working Conditions

4-day design sprints: Required ruthless prioritization. Shipped core workspace first, layered details later.
Parallel Work: Managed 2-3 concurrent projects, leading to heavy context switching.
Clear Handoff: Delivered primary screens; PM annotated interaction details in a second pass.

What I’d Do Differently

Advocate for UX Runway: Push for a minimum 2-week UX runway per feature instead of 4 days.
AI Architecture: Involve UX in AI model discussions earlier. Decisions about context limits and hallucination fallbacks happen before Figma.

The ultimate takeaway: Trust is the product. In AI-for-finance, trustworthiness > features. Every design decision, from bbox citations to dual-pane layouts, must center on verifiability.