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Check it out How AI Replaced 8 Improv Actors Per Venue Per Night (Without Breaking Immersion)
A 2-second delay in a haunted house doesn't just feel slow—it completely destroys the spell.
When a client in live entertainment came to me, they were facing a massive scaling bottleneck: hiring, training, and retaining 8+ specialized improv actors per venue every single night. When an actor called in sick, that entire room went dark or ran on static scripts that guests saw through immediately.
They needed the immersion of live, unscripted performance with the reliability and scalability of an automated system.
To solve this, I engineered AI Possession Kits—modular character engines deployed directly to animatronics and environmental trigger points.
🧠 The Technical Architecture
RAG-Powered Character Engine: Fed lore bibles and backstory docs into custom retrieval pipelines so characters maintain 95%+ lore accuracy without hallucinating outside the universe.
Real-Time Audio Loop: Guest speech is captured, processed through the AI inference layer, and returned as character-voiced audio through animatronic speakers.
Persona & Mood Parameters: Utilized Claude and OpenAI models configured with emotional range parameters, strict persona prompts, and scene-aware context windows.
Orchestration Layer: Built with Make to handle real-time routing between audio input, AI processing, and output delivery.
Failsafe Latency System: If network latency spikes beyond the immersion threshold, the system gracefully falls back to pre-recorded contextual voice lines. The guest never hears silence. The spell never breaks.
📈 The Results & Business Impact
8+ specialized improv performers eliminated per venue per night
3x increase in guest engagement time compared to static animatronics
95%+ lore accuracy maintained across thousands of live guest interactions
Hours vs. Weeks: New interactive characters spin up in hours by feeding in lore docs, compared to weeks of human actor rehearsals
💡 Key Takeaway
The hardest engineering problem wasn't making the AI sound smart—it was managing latency and failsafes under live performance constraints. In AI engineering, the tech is only as good as its fallback when conditions drop below the threshold.
What’s the tightest latency constraint or real-time challenge you’ve had to engineer an AI pipeline around? Drop your stack below 👇
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Hello everyone I'm Prem, and this is ORBIT, my entry for the Flowstep.ai Challenge.
We all lose touch with friends we loved from school, from college. Nobody's fault. Life gets busy, and by the time you notice, it's been years.
Losing a friend isn't a decision you make; it's something you find out about, long after it happened. Everyone means to react out . Nothing reminds you that you haven't.
So I built the thing that keeps track. In Orbit you're the sun, and your friends are planets orbiting you. How far out a planet sits is how long you've been quiet. Tanmay hasn't heard from me in 212 days, so he's stopped moving he's asleep, with little z's floating off him.
The distance isn't decoration, the picture is the data. Your glance at your galaxy.
And it doesn't nudge you to "check in." It gives you a reason: her show opens Friday, and you saved her flyer in March. You send a Shooting Star, she replies, her orbit pulls back in.
It never reads what you wrote. It only notices that you talked.
I connected Flowstep to Claude Code over MCP and drove the whole build from my terminal. Loading my design system in as Flowstep's design guidelines first is what made 21 screens feel like one product. Then I put it live, so it's a real app you can use.
Send someone a shooting star and see what comes back 😊 .
#FlowstepChallenge #Flowstep #ClaudeCode #MCP #ProductDesign
Built this landing page using Figma + Claude Fable 5.
https://novaabank.netlify.app
Wow , i mean now words still cannot belive ai did this
Portfolio update day 4/30:
Today I started migrating the scene to Three.js with Claude.
What I've accomplished:
WebGPU scene with soft shadows and reflections, transfer of models, materials, and environments, lighting setup.
As expected, I transferred all the routine work to Claude and didn't bother with manual export.
This will require review and possible optimization in the final stage, but for now the result is amazing.
Next up are the bot animation, the Rive interface, and finally, the creation of the website layout into which the scene will be integrated.
I keep building!
WebGPU still has spotty browser support so that review stage probably needs a fallback path not just optimization. worth locking in early since it is easy to design around it later than to patch it after the scene is built