CS 01: PartnerIQ, Co-sell (Collaborative sales) Intelligence
A sales intelligence platform that replaced spreadsheets, CRM rabbit holes, and cold dinners
with a system that told software sales teams exactly who to reach out to, and why.
My role:
Sole designer. Owned everything from problem framing to dev handoff. Strategy, flows, wireframes, prototypes, and iterative testing with real users, daily. The founders knew the sales world inside out, I turned that knowledge into something people could actually use.
The Problem:
At the start of every year, software sales reps and AWS reps each get their account lists. Then comes the hard part: figuring out who on the other side is working the same accounts. There's no directory. No shared system. Reps dig through Salesforce and HubSpot, lean on whoever they met at the last dinner, and hope the timing works out.
No way to find the right contact
A rep managing 50 to hundreds of accounts had no reliable way to identify which AWS rep covered the same territory, which accounts overlapped, or who had actually performed in that segment.
Account mapping done by hand
Every co-sell conversation started the same way. Export a CSV, email it across, match rows manually. By the time the overlap was found, the moment had usually passed.
No way to evaluate a new partner
When an AWS rep encountered an unfamiliar software company, there was no single place to understand their customers, their track record, or whether the partnership was worth pursuing. Trust was built slowly, informally, or not at all.
User interviews made one thing clear: Sales reps are smart about relationships and impatient about everything else. Every screen had to be immediately obvious.
Who Uses It
PartnerIQ was built for the ISV AE. The AWS AE benefits from the system, but the primary unlock: visibility, intelligence, structure, was always on the ISV side.
Persona 1:
ISV Account Executive
Who they are
Owns the co-sell relationship from the ISV side. Starts the year with an account list and spends the rest of it figuring out which AWS reps to partner with and which accounts to prioritise. CRM-trained, time-poor, skeptical of new tools unless they save real time.
Goals
Know who to reach out to on the AWS side. Understand account overlap fast. Get warnings before deals go cold.
Pain points
No visibility into AWS rep activity. Account mapping requires manual effort. Deals stall and they find out too late.
Persona 2:
AWS Account Executive
Who they are
Works their own account list, looking for ISV partners who can help move deals. Encounters dozens of ISVs, needs a fast, structured way to evaluate whether one is worth investing a relationship in. Benefits from PartnerIQ's shared surfaces, but the product was not built primarily around their workflow.
Goals
Identify ISV partners with account overlap. Understand the ISV's customer base and credibility. Coordinate without being onboarded to someone else's internal tool.
Pain points
No single place to evaluate an ISV. Account mapping requires the ISV to send a spreadsheet. Collaboration happens over email with no shared record.
The Solution
Reps have zero patience. Every pattern had to survive five seconds of real-world use before it earned a place in the product. This loop ran almost daily for the length of the project.
The AI Layer
Every signal in PartnerIQ anchors to ICP (Ideal Customer Profile).
The AI layer threads through the product. ICP Fit %, PTB scores, warnings, contact summaries, and the natural language chat agent all sit on top of the same intelligence backbone anchored to the ICP the rep defines on day one.
The design system
Every screen sits on top of one design system, built from scratch as Labra's first designer. That's why this case study has no wireframes. Once the system was created, using ready components for iterations was faster than wireframing from scratch.
Setting up the ICP (Ideal Customer Profile)
Before anything else, the rep tells the product who they're hunting for.
01.
ICP comes first, not later. No "skip for now." Nothing in the product personalises until the rep has defined this.
02.
Multiple criteria, not a single filter. Revenue, industry, employee size, segment, cloud stack, region, marketplace presence.
03.
Multi-select inside each field. A rep can hold multiple industries or regions in one ICP. Real territories aren't single-tag.
Drives every AI score that follows. ICP Fit % shows up on every contact, list view, and AI summary after this point.
Landing on the dashboard
The first screen a rep sees every morning. The job is fast scanning.
ICP Fit % on every row. The rep's own definition, applied to every contact, visible without clicking.
"Ask Labra AI" sits in the dashboard, not behind a menu. The rep can ask questions about their pipeline in plain language.
03.
Activity as a bubble pattern, not a timestamp. Two tracks per row. Contact's activity vs. rep's.
Bubble size is engagement intensity.
04.
Top performers and segments at the top. Who's winning, where, in what segment. Context before the rep starts scanning their own list.
05.
Opportunities split three ways.
In progress (deals running), closed won (revenue acquired), closed lost (deals dropped). Pipeline state in one row.
Opening a contact
The rep clicks a row. The drawer slides in.
AI summary above the tabs. First thing the rep reads. Prose, not bullets. "Re-summarise" sits next to it because summaries go stale.
02.
Team hierarchy shows reporting lines. The rep sees who reports to whom before deciding who to engage.
03.
Mapped accounts visible right inside the contact. ISV AEs work in Labra day-to-day, not in the Collab Room. The accounts they share with this contact, opportunities, status, ICP fit, PTB score, sit one click below the summary.
"Ask Labra AI Agent" handles depth. The chatbot exists, but it's a click away. The default view is a briefing, not a chat.
Checking warnings
If a deal is going cold, the rep needs to know before the next standup.
Warnings is a tab, same level as Accounts and Activity. Permanent destination. Buried warnings get checked too late.
Originally we thought of it as last layer, below the accounts table. Watching real reps use the early version, we saw they were spending more time on warnings than expected. So we pulled it up.
Each warning names the signal and the action. "Ghosting" "Consider re-engaging." "Deal overdue" "Consider closing this opportunity." Tells the rep what's wrong and what to do.
Inviting to a Collab Room
The rep wants to bring the AWS counterpart into a shared space. They send an invite.
01.
Email-only login. For an AWS rep evaluating an unfamiliar ISV, friction kills trust.
02.
Co-branded ISV + AWS, "Powered by Labra." The header signals shared ownership between the partner and AWS. Labra is the infrastructure, not the brand in the foreground. the AWS rep enters a neutral space, not an ISV tool.
03.
Privacy statement front and center. "This Collab Room is private and can only be accessed by the invited email address." Trust is stated, not implied.
The ISV's profile
Once inside, the AWS rep needs to know who they're dealing with.
01.
Trust signals. Customer logos, testimonials, certifications. The first thing the AWS rep sees is who already trusts this ISV.
02.
Co-sell revenue and opportunity counts visible. $420M in co-sell revenue. AWS-originated and ACME-originated opportunities. The track record is the credential.
03.
Alliance team and sales team named. Real people, real roles. The AWS rep knows who to talk to and what they own.
04.
Collateral and solutions in one place. Decks, briefs, playbooks. No "let me send that over" follow-up.
Mapping accounts with the other side
Once both sides are in the Room, the first concrete step is comparing books.
01.
Both sides upload, both sides see everything. No one-sided visibility. No "send me yours first."
02.
Common accounts highlighted, others still visible. Overlap is called out, but the full territory stays in view.
03.
PTB and ICP Fit on every row. Reps prioritise overlap by fit, not by alphabetical order.
04.
Comments scoped to a single account. The conversation lives on the row it's about. No context loss across threads.
Chatting inside the Room
01.
Chat lives where the work lives. No platform-switching.
02.
All files in one tab. Decks, lists, reports surfaced in a single view.
Impact
ISV AEs went from coordinating co-sell through CRM, spreadsheets and dinner conversations to a system that surfaced partner intelligence before they knew to look for it.
Before
Account mapping done manually over email, took days
No visibility into AWS rep activity or engagement
Deal health discovered reactively, after damage done
AWS reps had no structured way to evaluate an ISV
Co-sell coordination scattered across email and Slack
After
Both parties upload lists and see overlap immediately
Activity timeline shows engagement pattern at a glance
Warnings tab surfaces ghosting and stalls before they compound
Collab Room functions as a full ISV profile and trust surface
Single shared space with chat, files, and account mapping
Adoption and revenue data sit with Labra post-launch.
Applying AI to the co-sell motion was largely unexplored territory. The product launched at AWS re:Invent and the AI layer drew the strongest response from partners and reps on the floor.
Reflection
The AI layer was the most-praised part of PartnerIQ at re:Invent. It was also the part I'd build differently today. It worked. It scored, summarised, flagged, and answered questions in plain language. But shipping AI is not the same as shipping a complete AI product, and I see the distance between them now.
What I underweighted:
Trust calibration
Reps overtrust confident AI by default. Verification should be one click from every inference, not buried.
Bias in PTB scoring
PTB learns from where past deals closed, not where the real opportunity might be. A rep following PTB blindly reinforces past patterns instead of testing new ground. No override, no feedback loop in v1.
What I'd carry forward:
Treat AI like a system that's confident and sometimes wrong. Make verification one click away. Use disagreement as training signal. Audit the training data before deciding what to surface.
The response at re:Invent was real. The work I'd take on next is closing the gap.
More Work
Pruthviraj Chandak · Lead Product Designer, India