Deep Journey - AI powered sales insights
Helping sales teams close more deals, work more efficiently, and gain clarity on pipeline health.
The Problem: Sales leaders were overwhelmed by a fragmented, reactive tool ecosystem that made it hard to do effectively manage their reps and campaigns
• Reps were context-switching between 6+ tools to run meetings, track notes, log CRM activity, and follow up.
• The quality and volume of data from sales meetings and touch points was often too limited to provide deep insights
• Sales leaders lacked real-time visibility into what was happening in calls or across the funnel.
• Reporting was delayed, and coaching was reactive.
• CMOs and CROs spent hours piecing together insights on campaign performance, rep productivity, and deal health — often relying on RevOps or custom dashboards just to understand what was working.
Research:
Before jumping into design, I spent weeks deeply immersing myself in the world of sales teams — using both qualitative and quantitative methods to identify pain points and patterns.
What I did:
• Analyzed over 50 of customer and prospect calls to map real user behavior and friction points using notebook LM
• Performed deep dives into the workflows of AEs, SDRs, sales managers, CROs, and RevOps leaders
• Organized themes and surface repeated pain signals, Identified the specific pain points CRO’s face
“My team can run killer sales calls, but then I spend two hours just logging, syncing, and trying to make sense of what happened.”— AE at a mid-market SaaS company
”right now our events, calls, data, logging, and analytics are all run on different platforms, managing all of them is a nightmare"
Notebook LM example query
I then summarized all of my findings into the following persona:
Identifying Principles of Design for Deep Journey
Collaborating with engineers
I worked hand-in-hand with an AI engineer to translate the design requirements and user journey into a technical prototype - we determined the backend architecture, technologies, and product touchpoints, this allowed me to then design a front end user experience that was aligned from a technical and design perspective.
Core experience
Landing Page
Conversation memory, AI generated suggestions, and context filtering
Filters
Filter by any previous scoot meeting or event, and date range
Chat Interface
AI generated titles, formatted text responses that also include reference “footnotes”. Clicking on these "footnotes" allows the user to trace back to the source of truth
Source Information
Clicking on the footnote allows the user to explore the source information that informed the response
Transcript Linking
The user can trace all the way back to the meeting transcript and find the relevant information highlighted
Results
• Reduced time spent on sales reporting and analysis from 2–6 hours/week to under 15 minutes.
• Simplified workflows by reducing tech stack from 6–8 tools and 4–5 integrations to just one platform.
