Ideal Client Posse: Where Parallel Processing Meets Business Development
Drop in information about your company and a case study, hit the button, and watch as the system generates multiple distinct client personas: early-stage startups, large e-commerce companies, digital agencies, whatever fits your business.
Instead of reviewing your content one persona at a time, you can select five or six profiles and have them all analyze your case study simultaneously. Each review runs independently in the background. Results slide in as they complete.
You can also go into “Miracle Mode” to let the app pick the best ICPs for you and rewrite your case study to be uniquely tailored to them.
Notes from Building
AI personas need consistent traits to be useful. Our profiles follow very specific characteristics:
Judicious (smart decisions)
Upmarket (values quality)
Growth-aligned (seeks scale)
Goal-getting (execution-focused)
Ambitious (pushes for impact)
Logical (loves clarity) and
Operator (understands business).
The right prompt matters—a lot. So we built a consistent base that works both for reviewing content and understanding the broader context of the app.
Pressure test infrastructure (and assumptions) with real use cases. The big idea here isn’t just about generating client personas, it was about proving our system could handle multiple concurrent LLM calls without breaking. We've had several client projects recently that needed similar parallel processing capabilities.
Parallel processing speeds things up (in two ways). Instead of waiting two minutes for six sequential reviews, you get all six in about 30 seconds. But more importantly, users can keep working while jobs run in the background. No more staring at loading spinners.
The other kind of parallelism: breaking big jobs into smaller ones. When a task involves multiple steps—like generating reactions from three different clients—we split it up and run them all at once. Same result, way faster, and no single job slows everything down.
Background job orchestration is worth the complexity. Ah, the fun part—we got to explore some tools that actually handle it well. In this case we landed on Inngest for background job orchestration. It plays nicely with our Next.js/Vercel setup and takes care of the heavy lifting without custom queues or fragile workarounds.
Diversify! Getting feedback from one ICP (you know, "ideal client") is useful. Getting six different takes on the same content reveals patterns you'd miss with a single review. The early-stage startup sees different problems than the enterprise client.
PS: We’re still working on generating an actual Insane Clown Posse review of our case studies. The technology isn't quite there yet, but we remain optimistic about the future of Juggalo-powered business development.
