About Qumis
Qumis is a LegalTech AI company based in Chicago and New York. Their platform uses AI to help insurance professionals navigate complex policy language - automating coverage analysis and accelerating claims decisions. At the time of engagement, the team was building their first commercial motion with a small founding team. Like many early-stage LegalTech and InsurTech startups, they needed to stand up both a CRM and an outbound sales engine before revenue could scale.
The Challenge
Qumis had already adopted Attio as their CRM, but the workspace was largely unstructured. More broadly, the team had no outbound system in place and no infrastructure to support the product-led growth motion they were planning.
Three distinct problems needed solving:
Attio data model not set up for B2B SaaS: The Attio workspace lacked a proper data model. Workspaces (their core product object) were not connected to deals, the CS pipeline didn't exist, and there was no way to track subscription changes or expansion over time.
No outbound engine: The sales and BD team had no tooling for lead generation, enrichment, or outbound sequencing. Prospecting was entirely manual - a bottleneck for any founder-led B2B sales motion.
PLG infrastructure not yet defined: Qumis was preparing a self-serve motion but had no clear roadmap for connecting product data to their commercial stack in Attio.
Our Approach
Novlini worked with Qumis across two phases to build both the CRM foundation and the outbound GTM stack.
Phase 1 - Attio CRM Rebuild and Outbound Stack with Clay and Lemlist
We started with a full audit of the Attio workspace and rebuilt the data model for a B2B SaaS commercial motion. This included connecting Workspaces to Deals, creating a CS pipeline for post-sale tracking, and adding a custom object to track subscription changes - enabling proper expansion and upsell visibility that didn't exist before.
In parallel, we built and launched Qumis's first outbound motion using Clay and Lemlist: ICP definition, enriched target lists, first sequences written and configured, and an initial campaign of 200 prospects live within weeks of engagement start.
Phase 2 - Outbound Iteration and PLG Architecture Scoping
We continued supporting the team on the first outbound campaigns - analyzing early results, iterating on the sequences, and helping prioritize the next targets. In parallel, we ran a dedicated discovery session with the technical and product team to define the product-led growth roadmap: what data to collect, how to warehouse it, and how to route product signals into the commercial pipeline in Attio.
Technology Stack
Attio — CRM, pipelines, CS tracking, subscription objects (source of truth)
Clay — ICP definition, lead enrichment, list building
Lemlist — outbound sequencing (email + LinkedIn)
The Results
Within the first weeks of engagement, Qumis went from zero outbound infrastructure to a live campaign - and from a passive CRM to a system that could actually track their commercial and CS motions.
Outbound motion live in weeks - 200 prospects in active sequences, from a standing start with no tooling
CS and expansion now visible - Workspaces connected to Deals, subscription changes trackable, upsell opportunities surfaceable for the first time
PLG roadmap in hand - phased architecture defined, ready to implement when the product-led motion launches
Engagement renewed - the team came back for more, which is the most honest signal of value
Why It Matters
Early-stage B2B SaaS teams - especially in LegalTech and InsurTech - often try to build outbound and product-led growth at the same time, and end up doing neither well. Qumis took the right approach: get the Attio CRM clean and the outbound engine running with Clay and Lemlist first, then define the PLG motion on a solid data foundation before building it.
For any LegalTech, InsurTech, or B2B SaaS startup trying to build a commercial system that scales from founder-led sales to a self-serve motion, this is the sequence that works.