CHAPTERS
- 0:04 – 0:27
Confido’s $55M Series B and what the company is today
Diana opens by congratulating Kara and Justin on Confido’s $55M Series B and sets up the arc from near-missing Demo Day to becoming a scaled enterprise business. The founders quickly frame Confido’s current mission and scope.
- •$55M Series B led by Insight
- •From YC-era uncertainty to current traction
- •High-level definition of Confido today
- •Sets context for origin story vs. present-day platform
- 0:27 – 0:42
The “AI agent workforce” for consumer brands
Justin explains Confido as an AI-agent workforce that automates key operational work for consumer brands. He describes the range of functions covered—from accounting to sales and materials planning.
- •AI agents applied to back-office and planning workflows
- •Targets brands found in everyday household products
- •Use cases: accounting, sales planning, raw-materials planning
- •Core promise: automate labor across the business
- 0:42 – 0:54
Customer roster: from global CPG giants to fast-growing challengers
The conversation moves to who buys Confido, spanning divisions of the largest CPG companies and newer breakout brands. This establishes credibility and the breadth of customer fit.
- •Customers include Mars and Unilever divisions
- •Also serves high-growth brands like Olipop and Dude Wipes
- •Demonstrates enterprise readiness plus mid-market appeal
- •Signals large contract sizes and strategic adoption
- 0:54 – 1:16
What Confido actually delivers: a broad platform powered by retail data
Justin outlines the product as a unified system handling multiple workflows rather than point solutions. The differentiator is leveraging brands’ unique retail data to automate and coordinate work across teams.
- •Single platform spanning accounting + planning workflows
- •Broad automation across operational functions
- •Retail data is the underlying engine
- •Focus on workflow automation vs. dashboards alone
- 1:16 – 2:18
From “Outfit” to pivot pressure: the year-long search for a real idea
Diana revisits Confido’s YC application as “Outfit,” a COVID-era concept for fitness instructors. Kara describes getting early feedback to pivot and the ensuing “pivot hell” that nearly kept them from Demo Day.
- •Original company: Outfit (WeWork for fitness instructors)
- •YC partners pushed them to pivot after reviewing numbers
- •Remote batch context; founders met in Boston post-MIT/Harvard
- •A full year of iterating before landing on Confido
- 2:18 – 3:14
The idea hiding in plain sight: founders’ personal exposure to the problem
Justin explains how prior work at consumer companies exposed him to the exact operational pain Confido now solves. He connects this to family background in retail and software sales, making the eventual direction feel obvious in hindsight.
- •Prior experience at a football helmet manufacturer and Anheuser-Busch
- •Family ties: retail store management + selling software to consumer companies
- •Recognizing the problem as longstanding and under-addressed
- •Why it still took time: overlooking “too simple” opportunities
- 3:14 – 3:48
Cold-emailing the biggest brands and discovering massive unmet demand
The founders validated demand by cold-emailing major CPG companies and getting fast, high-engagement responses. Long calls with leaders at companies like Coca-Cola revealed how manual the workflows still were.
- •Cold outreach to large CPGs (e.g., Coca-Cola, Dr Pepper)
- •Surprisingly quick replies and long discovery calls
- •Signal of acute pain and underserved market
- •Revelation: critical processes were still highly manual
- 3:48 – 4:16
First customer closed with a Figma—before writing code
Kara recounts selling the first customer using a Figma prototype, proving urgency and willingness to bet on a tiny team. This chapter highlights their early go-to-market pragmatism and focus on pain over polish.
- •Closed first customer with a Figma prototype
- •No code required to get an initial commitment
- •Customers valued solving pain over vendor maturity
- •Strong validation: willingness to work with a two-person startup
- 4:16 – 5:04
Why CPG brands buy AI: they’re really buying labor replacement
Justin reframes CPG as historically service-heavy buyers, accustomed to BPOs, consultants, and brokers. Confido wins by selling “we do the work for you,” positioned as labor replacement/augmentation rather than traditional software.
- •CPG buys services more readily than “software”
- •Alternatives: BPOs, consulting, third-party brokers taking % of revenue
- •Confido positioned as doing the work, not just providing tools
- •Buyer language centers on labor replacement/augmentation
- 5:04 – 5:26
The first workflows: portal scraping to cash application and accounting automation
Kara details the initial wedge into a painful, repetitive workflow involving multiple portals and manual data pulls. Success there led customers to ask Confido to expand into more functions.
- •Initial problem: logging into multiple portals and pulling data
- •Automated cash application and accounting workflows
- •Expanded roadmap driven by customer pull
- •Land-and-expand motion based on adjacent operational pain
- 5:26 – 6:04
Why legacy ERPs didn’t win: Excel, point solutions, and lack of agentic execution
The discussion contrasts Confido with ERPs like NetSuite, explaining why many brands relied on spreadsheets and fragmented tools. Confido’s claim is a cohesive platform where data flows automatically and agents drive outcomes, not just storage.
- •Incumbent reality: Excel and disparate point solutions
- •Some tools function as basic data wrappers, not workflow automation
- •Missing piece: integrated platform + automated data flow
- •Agentic insights/execution vs. ERP as “basic database”
- 6:04 – 7:13
The economics of six- and seven-figure deals: touching every P&L line item
Justin explains why Confido can command large contract values: it affects headcount costs and unlocks missed revenue recovery. Examples include reconciling tiny transactions humans ignore and improving forecasting beyond historical averages.
- •Value spans nearly every P&L line item
- •Automates manual work and reduces required headcount
- •Recovers “free money” by chasing transactions humans skip
- •Planning upgrade: better forecasting than simple historical averages
- 7:13 – 7:35
The end-state vision: Confido touching every step from ingredients to shelf
Justin describes success as end-to-end influence over how products are made, priced, stocked, and forecasted. The retail shelf becomes the proof point that Confido improved the entire operational chain behind each brand.
- •Measure impact by seeing brands on shelves touched by Confido
- •Workflows include procurement, pricing, and shelf forecasting
- •Goal: end-to-end operational automation for consumer brands
- •Vision of pervasive behind-the-scenes infrastructure
- 7:35 – 8:53
Building without product managers: engineer-owned modules and customer-facing execution
Diana closes by exploring Confido’s org design: no dedicated product managers. Kara describes engineers who own ambiguous problems end-to-end, including roadmap, expert discovery, onboarding, and even sales enablement.
- •No PMs; engineers drive product ownership
- •Engineers handle roadmap + customer discovery + onboarding
- •Example: supply planning module led by a long-tenured engineer
- •Hiring profile: curious, highly technical, thrives in ambiguity
