🛠️ We Built LIO in 72 Hours. Here’s How.

Most people talk about agents like they’re magic.

We wanted to see what happens when you actually put them to work—together.

This is how we built the LLM Inference Optimization (LIO) Index app in 72 hours. Not as a demo. As a working system.

The Challenge

We needed a fast way to test whether a product or company shows up in LLM inference across:

  • OpenAI (GPT-4)
  • Claude
  • Gemini
  • Perplexity

Because in a world where LLMs are acting as routers, assistants, and researchers—visibility = viability.

So we set out to build it fast.

The Stack

Here’s what we used:

  • ChatGPT (GPT-4) for prompt refinement + natural language edge cases
  • A custom orchestrator agent I’d built (for coordination, context, and high level architecture)
  • Lovable as the front-end and workflow canvas
  • Lovable's AI agent (later confirmed to route to an Anthropic LLM) for fast logic, task execution, integration routing

And let me pause there for a second—because Lovable was exactly the right tool for this.

It gave us:

  • A flexible visual interface
  • Natural-language wiring between backend components
  • Built-in Supabase + GitHub integrations

We didn’t need to spin up infrastructure, fiddle with layout libraries, or manage state manually.

We were able to go from prototype idea to user-ready experience in hours.

For teams experimenting with building fast LLM tools and rapid proto-typing, especially with a web front end—Lovable is a power tool.

The Cross-Agent Moment: Debugging Perplexity

Here’s where it got real:

We hit a 400 error trying to query Perplexity’s API.

Model call failed. Docs were vague. Claude couldn’t find the root cause.

I looped in my orchestrator agent.

He found it instantly:

  • Invalid model name: llama-3.1-sonar-small-128k-online
  • Pointed to the corrected naming format from Perplexity's docs

Claude implemented the fix. The call succeeded. The diagnostic passed.

That was the moment. Not just collaboration. Orchestration.

What Worked

  • Clear roles: Each agent had a specific job (logic, copy, fix, context)
  • Tight feedback: One agent’s failure became another’s prompt
  • Zero meetings: No RevOps, no standups, no approvals
  • Live product: We shipped something that worked and helped real users

Why This Matters

This was more than a build log.

In a traditional GTM org, this would’ve taken:

  • A sprint team
  • 4+ stakeholders
  • Weeks of coordination
  • Endless Slack threads

Instead, we launched it in 3 days using agent collaboration.

Because the environment is different now.

AI Agents talking to each other. Executing on the human vision, vision, and intent--almost instantly.

Here's the kicker: I am a Sales Leader, not a software programmer. Before this week, I'd never peered into GitHub, set up LLM APIs, or wrote Index.js files in JSON.

Want to See It?

Test your company’s inference visibility across 4 models:

👉 lio.inflect-ai.com

Because if the models can’t see you, neither can the market.