Case Study: LOOM
A strategic rapid response, AI-enabled, team blueprint generation platform.
Overview
New business teams needed a faster, more scalable way to respond to early-stage briefs and exploratory client conversations, without sacrificing strategic depth or institutional knowledge. LOOM was conceived as a blueprint-generation platform that combines structured inputs with curated team and case study data, enabling new business leads to quickly assemble teams and draft personalized, pitch-ready recommendations.
The problem
Client opportunities often begin with incomplete briefs: a business challenge, a few service needs, and urgency. Creating a compelling response and matching services, case studies, and team experts required manual coordination across strategy, creative, and operations. This slowed momentum and created inconsistencies in how past work and expertise were surfaced.
The solution
LOOM was designed to streamline early-stage team formation and idea development by turning loose briefs into structured, actionable inputs. Through a short series of guided questions, LOOM links sales leads teams with the right internal expertise, introduces diverse specialists, and sparks new thinking through curated thought starters. The tool helps teams originate, optimize, and merge ideas and talent, accelerating alignment without losing nuance. By making agency knowledge more accessible and repeatable, LOOM closes the gap between intent and execution.
Discovery interviews
With the concept in hand, I conducted extensive interviews with:
- Executive leadership
- Sales and new business leads
- Account leads
- HR leads
- Technology leads
Design & build execution
Built with AI, guided by UX. To accelerate delivery, I used GhatGPT as a developer partner to co-develop the LOOM MVP. This included:
- Using ChatGPT to generate working code snippets for the site and backend
- Hosting the initial version via Vercel, enabling rapid iteration and internal testing
- Automating structural tasks so I could stay focused on UX, logic, and creative framing
The results
- A working MVP delivered in days, not weeks, fully tested, structured, and demo-ready.
- <5 minutes to generate a blueprint from scratch
- Zero-code updates to cases and teams via Airtable
- More strategic alignment in early-stage conversations
- High-quality AI outputs tailored by service category
- Fully self-serve, with no setup needed beyond Airtable updates.
Tools & technologies
- Figma for UX and userflows
- ChatGPT to aid in the development of the codebase
- GitHub as the code repository
- Vercel for deployments and API Integration
- Airtable as the data repository for tagged, cases and teams data
- Frontend: React, Tailwind CSS, Framer Motion
- Backend: Next.js API routes
- AI Integration: OpenAI API (Thought starters)
Project gallery
Overview
UX
MVP Flow: Lightweight and Simple
Users enter the client name, industry, client description, and a short-form “ask.”
They then select relevant service areas from a categorized interface.
LOOM then provides a single all in all-in-one blueprint that presents:
- A strategic framing of the ask
- LOOM dynamically filters team members and case studies based on overlapping tag relationships to the selected services via Airtable API integration.
- Identifies team availability based on capacity.
- An extra brainstorming 'voice in the room' by providing optional AI generated thought starters by way of OpenAI integration.
Vercel Deployments
Airtable Data
