hear@ai
hear@ai
Turning enterprise conversations into decisions teams can trust.
Enterprise teams lose critical hours and confidence second-guessing black-box AI summaries across calls, tickets, and documents.
hear@ai turns unstructured enterprise conversations into structured, explainable recommendations backed by clear evidence trails. As Lead Product Designer, I defined the product strategy, explainable AI UX architecture, and human-in-the-loop validation flows from concept to delivery.
TL;DR
- Problem
- Enterprise teams ignored AI summaries they could not verify or defend in high-stakes decisions.
- My role
- Lead Product Designer.
- What I did
- Defined the product strategy and an evidence-first UX: every recommendation links to its source, shows confidence, and requires human validation before high-impact actions.
Project at a Glance
Role
Lead Product Designer
Platform
Enterprise SaaS
Domain
Enterprise Decision Intelligence
Duration
12 Months
Team
2 PMs, 8 engineers, 1 designer
Users
- Operations
- Sales
- Support
- Leadership
Responsibilities
- Product Strategy
- UX Architecture
- Information Architecture
- Explainable AI Experience Design
- Design System
- Product Direction
- Cross-functional Collaboration
- Design QA
- Engineering Collaboration
Business Context
Enterprises already capture more conversations than they can use. Meetings, calls, tickets, and documents pile up—while decisions still depend on tribal knowledge, incomplete notes, and delayed follow-up.
Leadership wanted faster insight from that content. Operators needed something different: they would not act on an AI recommendation they could not defend to a customer, manager, or compliance review.
hear@ai had to solve a business problem—reduce time from conversation to confident action—without asking teams to trust a black box in high-stakes workflows.
The Challenge
During discovery it became clear that “better summaries” would not ship. Users did not primarily doubt accuracy—they doubted whether they could explain the recommendation if challenged.
When AI outputs arrived without evidence, people ignored them or re-checked source material manually. Automation without a review path created accountability risk. Each function also needed different confidence thresholds—sales wanted speed, operations wanted verification, leadership wanted signal without noise.
The challenge was to design decision support that made uncertain AI outputs inspectable, reviewable, and safe to act on—while keeping the workflow simple enough for daily enterprise use.
Enterprise AI fails when teams cannot defend the recommendation.
Discovery
I started with trust gaps, not model demos. Interviews and feedback sessions with operators and product stakeholders showed a consistent pattern: people abandoned AI help when they could not see why a conclusion was reached.
Engineering constraints mattered early—probabilistic outputs, multi-modal sources, and evolving models meant the UX could not assume perfect answers. Academic-sounding “AI explainability” was useless unless it lived inside the decision workflow as evidence, confidence, and human validation.
Research included:
- •
Stakeholder interviews across operations, product, and AI engineering
- •
Feedback synthesis on where AI recommendations lost credibility
- •
Constraint mapping for evidence trails, confidence, and review states
- •
Workflow mapping from raw conversation → insight → validation → action
Observation
Users could not verify why an insight appeared—so they treated AI as optional noise.
Design Response
Make evidence part of the default insight interaction, not a buried details panel.
Observation
Summaries without a reasoning trail collapsed confidence even when the content was useful.
Design Response
Surface source links and review steps before high-impact actions.
Discovery reframed the product from “AI that answers” to “AI that can be audited inside the decision.”
Product Strategy
Instead of optimizing for impressive AI answers, we positioned hear@ai as decision intelligence: AI proposes, evidence explains, humans validate, then the system supports action. That sequence became the product filter—if a feature increased automation without increasing defensibility, it did not ship.
Design Principles
01. AI should explain before it asks teams to act.
02. Every recommendation should be traceable to source evidence.
03. Human judgment stays in the workflow when the business risk is real.
Product Architecture
We needed a platform that could serve multiple business functions and future AI modules without inventing a new trust pattern each time.
I pushed for reusable interaction layers—evidence, confidence, review, and action—so voice, documents, and insights could share one decision model instead of becoming disconnected AI features.
Why this architecture:
- •
Keeps conversation, evidence, and action in one operational system
- •
Supports multiple enterprise functions without fragmenting trust patterns
- •
Lets new AI capabilities plug into the same explain → validate → act flow
- •
Creates a design system that scales dashboards and review states together
Brand and system foundations had to signal reliability—enterprise buyers were buying confidence in the workflow, not novelty in the model.
The dashboard ecosystem was designed as the place decisions happen—not a gallery of AI widgets.
Key Product Decisions
These decisions turned opaque AI output into a workflow teams could defend. Each one traded raw automation speed for operational confidence.
Problem
Insights appeared as finished answers. Users could not inspect the path from source conversation to conclusion—so adoption stalled in high-stakes work.
Decision
Make evidence the primary interaction around every insight.
Alternatives considered
Polished summaries only; optional “view sources” links; or full transcript dumps beside every answer.
Trade-offs
Denser UI and an extra click before action. Higher willingness to trust and use recommendations.
Why this approach
In enterprise workflows, defensibility beats polish. Evidence before conclusion made AI usable under scrutiny.
Outcome
Teams could validate reasoning before committing to follow-ups, escalations, or handoffs.
This wireframe exists to lock the interaction model early with engineering: insight, evidence, confidence, then action. We rejected “summary card first” because demos looked clean but failed the trust test in stakeholder reviews.
Problem
Voice, documents, and AI insights lived in separate views. Context switching broke the decision path and made accountability unclear.
Decision
Unify conversations, structured intelligence, and actions in one operational dashboard.
Alternatives considered
Separate tools per modality; chat overlay on existing systems; or insight inbox disconnected from source media.
Trade-offs
Higher IA and integration complexity. Lower cognitive load when deciding what to do next.
Why this approach
Decision confidence comes from staying in one workflow—from evidence to action—without losing the thread.
Outcome
Reduced context switching and made review part of the same surface where work gets done.
The dashboard is not a feature showcase. It solves the operator problem: “What should I do, and why?” Source context, AI signal, and next actions share one view so trust and throughput move together.
Problem
Fully automated actions looked efficient in demos but created accountability risk when recommendations were wrong or incomplete.
Decision
Require human validation before high-impact enterprise actions.
Alternatives considered
Auto-execute all recommendations; confidence-only gating with no review; or manual workflows with AI as a side report.
Trade-offs
Slower time-to-action on critical steps. Safer adoption and clearer ownership.
Why this approach
Enterprise buyers adopt AI when someone can still own the decision. Validation made automation socially and operationally acceptable.
Outcome
AI became decision support teams could use—not opaque automation they had to bypass.
Product narrative and UX had to say the same thing: hear@ai increases decision quality by making AI inspectable. That alignment kept sales, engineering, and design from overselling autonomous AI.
Architecture & Code
hear@ai treats every recommendation as a decision object: retrieved evidence, a constrained prompt, optional tool calls, and a UI that makes uncertainty inspectable before anyone acts.
The Agent & Context Architecture
Conversation and document inputs are embedded against enterprise knowledge. The system prompt encodes policy and confidence rules. Agents fetch evidence and structure the output as a recommendation with sources—not a summary blob.
- Input
- System Prompt
- Retrieval / RAG
- Tool Calling / Agent
- Structured UI Output
Failure States & Latency UX
Enterprise buyers reject AI that stalls, over-claims, or hides why it is slow. Latency and error states are part of the decision workflow—not afterthoughts on a loading spinner.
Hallucinations: claims without source evidence stay in a review state and cannot trigger downstream actions.
Streaming delays: insight cards show a confidence-pending state until retrieval completes, so operators never act on a half-formed recommendation.
Token overruns: long transcripts are chunked and summarized with source anchors rather than silently truncated.
Low-confidence outputs: the UI requires human validation and shows why confidence is low—missing evidence, conflicting sources, or model timeout.
Implementation Stack
Decision-intelligence UX is wired to the same retrieval and deployment path used in production—GCP-backed retrieval, structured outputs, and a frontend that treats evidence as a first-class object.
- GCP
- Vector retrieval
- Structured JSON outputs
- React
- TypeScript
- Evidence-linked UI
- Confidence states
Design Evolution
Early directions optimized for AI spectacle. Real enterprise feedback forced the product toward trust and decision quality.
Before
Assumed stronger model answers would drive adoption on their own.
After
Prioritized evidence trails and review states after users rejected unverifiable summaries.
Before
Prototyped chat-first experiences because they were easy to demo.
After
Moved to workflow-first decision surfaces after operators needed accountability, not conversation novelty.
Before
Treated explainability as a secondary details panel.
After
Made evidence a default interaction after trust collapsed without it.
Before
Pushed toward fuller automation to show “AI value.”
After
Kept human validation on high-impact actions to unlock real organizational adoption.
Cross-functional Leadership
On hear@ai, leadership meant turning trust into a shared product contract across AI engineering, product, and business stakeholders—not decorating model outputs with UI.
When engineering could not guarantee deterministic answers, I translated that into UX rules: show evidence, show uncertainty, require review. When product wanted faster automation demos, I pushed prototypes that sold defendable workflows—the product enterprises would actually approve.
What that looked like in practice:
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Facilitated decision workshops that forced explicit trade-offs between speed and defensibility
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Partnered with AI engineering on confidence, evidence linking, and failure states before visual polish
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Challenged roadmap items that increased automation without increasing inspectability
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Used research feedback artifacts to align teams on why users rejected opaque AI
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Owned design QA for review and action paths so trust patterns stayed consistent across modules
The result was cross-functional alignment around a decision model teams could execute—not a list of AI features.
Impact
Business Impact
- •
Repositioned hear@ai as decision intelligence enterprises could operationalize—not a generic AI summarizer
- •
Created a product story buyers could defend internally: faster decisions with accountable AI support
Product Impact
- •
Established a reusable explain → validate → act framework across modules
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Unified voice, documents, and insights into one decision-support system
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Defined interaction patterns that scale as AI capabilities evolve
User Impact
- •
Made AI recommendations inspectable before action
Team Impact
- •
Shared trust patterns reused across modules instead of one-off AI screens
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Research artifacts used to settle automation vs. oversight debates
Reflection
hear@ai reinforced that enterprise AI products are judged by whether teams can act with confidence—not by how advanced the underlying models sound. The work that mattered was designing the path from uncertain output to defendable decision: evidence, validation, and clear ownership inside the workflow.
Key Learnings
- 01
Trust is a product requirement in enterprise decision workflows—not a polish pass.
- 02
Evidence before automation creates safer, more adoptable AI assistance.
- 03
Cross-functional alignment beats model demos when the business risk is real.
- 04
Reusable trust patterns scale faster than feature-specific AI interfaces.