CASE STUDY - 0-TO-1 SaaS PRODUCT DESIGN LEADERSHIP & STRATEGY
Defining Docufy AI from zero
For startups in the pre-seed stage, speed and relevance isn't just a metric, it is survival. With no defined product and no confirmed user, I shaped the product into a sellable MVP.
Docufyai is an AI platform designed to transform how enterprises interact with their vast document repositories.
My Role
Founding Product Designer & Design Lead
Tools
Figma, Jotform, Miro, Framer, Perplexity, Claude
Timeline
Jun 2025 – present
0-to-1 product definition
Competitive analysis
Field research
Personas & user definition
Interaction design
Team leadership & mentorship
Scope negotiation
01 -
Context: a product with no edges
No defined product, no confirmed user, and no runway to get it wrong slowly.
Docufy AI was conceived to let enterprises "talk" to their documents. Contracts, disclosures, technical specs, claims files are some that people analyze manually, often cross-checking to reduce human error.
The challenge wasn't only technical, it was psychological. Enterprises are rightly skeptical of AI hallucinations, and these users handle documents where a wrong answer has consequences
4 pilot companies signed
Enterprise clients in sensitive, document-heavy industries evaluated and adopted the MVP, with a fifth company in talks.
MVP to Beta in three months
MVP launched February 2026, Beta launched May 2026 with trust features designed from pilot feedback.
Raised credits over $400K
From investors including the Government of Ontario, Google, and AWS
02 -
RESEARCH: GROUNDING the product in the market
The market sizing of $16.91B total addressable, $5.4B serviceable, $500M share-of-market target, produced by the CEO with the company's subject-matter advisors, was my only way to find an opening.
I ran a competitive analysis across six platforms. Four underserved gaps became Docufy's positioning
Extraction from highly sensitive documents
Output manipulation
Organization-level configuration
Multi-document summarisation
Defining the user
To validate the need, the CEO and I surveyed our professional network. The signal was clear: the strongest need was among corporate employees in sensitive, document-heavy industries. That turned an open-ended brief into a focused enterprise product, and I translated it into two personas that anchored every decision thereafter.
Michael
Insurance underwriter · Toronto
He reviews commercial policies, claim records, client details, and claims history while balancing speed, accuracy, and compliance across a high submission volume.
"I can't imagine a day without catching claims mistakes or fraud attempts."
WANTS
Accurate risk assessment on every submission.
NEEDS
Less time spent on manual reviews.
NICE TO HAVE
Strong compliance and audit readiness.
WHAT THIS MEANS FOR DOCUFY AI
AUTONOMY
Flags claims for violations, inconsistencies, risk-patterns, and fraud.
SPEED
Extracts and summarizes key information while maintaining integrity.
COMPLIANCE
Traces audit-ready checks and flags violations.
how might we
How might we give enterprise users enough confidence in an AI's answers on high-stakes documents to act on them without the engineering cost of building trust features that a pre-seed MVP cannot yet afford?
03 -
Strategy: familiarity as the fastest path to trust
Natural language interface (NLI) was the lowest-friction way to validate the tool with skeptical users.
In a 0-to-1 MVP, familiarity reduces cognitive load. I anchored the product on a conversational interface so we could quickly validate the tool against real enterprise needs.
04 -
Designing the MVP
I'd love to share my design process but the details of this project is private. To view the complete project, please use the password I provided or feel free to contact me.
What you will unlock -
The four core flows
Prioritization under pressure
MVP to BETA
Unlock
07 -
Leading the team
As the only senior designer, my output ceiling was the team's. So I built a cadence around it.
During the summers, I had the help of seasoned interns. They designed the UI, marketing emails, marketing & product videos, and UAT testing, while I ran weekly reviews and feedback along with a separate mentorship sessions covering UX, systems thinking, tooling, industry norms, and emerging technology like AI.
Outside the summer terms I own everything end to end.
Shielding the interns
Managing interns in a pre-seed company means the pressure lands on them by default. Scopes are larger than the time available, delivery timelines set by fundraising rather than by the work, and briefs arrive ambiguous. A large part of my job was absorbing that pressure before it reached them.
ABSORBING PRESSURE
I pushed back, prioritized, and negotiated on demanding scopes and unreasonable delivery timelines rather than passing them down. Protecting the morale by helping the interns or checking in on them timely, is how I like to lead.
TRANSLATING
I broke large or ambiguous scopes into simple, understandable tasks, and explained the business reasoning behind work that didn't look meaningful at first before validated that need so the urgency the team felt was real rather than manufactured.
08 -
Lessons Learned
LESSON 01
Perfection is the enemy of survival
While cutting certain trust features felt like cutting trust itself, the pilot users told us what trust should look like. In pre-seed, protecting the runway meant designing for user needed when the time arrived.
LESSON 02
Trust is designed, not declared
Saying an AI is trustworthy does nothing for a skeptical enterprise user. The Beta features are the ones that earned it.
LESSON 03
Scaling yourself is a design skill