SonicFilter AI: Using Fast Retrieval to Tune Acoustic Water Filtration

What I Built

Water filtration systems enhanced with vibrational frequencies—like those using D80 filters—can dramatically reduce turbidity, but tuning them is slow, experimental, and prone to error.

SonicFilter AI is a consumer-facing, non-conversational AI agent that eliminates guesswork by automatically recommending optimal acoustic parameters based on real-world experimental data.

Rather than waiting for user prompts or engaging in dialogue, SonicFilter AI works proactively:

• It ingests logged experimental runs and sensor metadata
• Detects inefficiencies in frequency/flow-rate pairings
• Recommends empirically validated vibrational settings (e.g., 20.3 Hz)
• Delivers calibrated, actionable guidance at setup time

The result? Faster deployment, fewer failed trials, and consistently higher filtration performance—without a single chat bubble.

Demo

🔗 GitHub Repository: https://github.com/trizist/sonicfilter-ai
🔗 Live Demo: Coming soon

What the demo will show:

• Indexed experimental runs with full sensor metadata
• Ranked vibrational configurations by filtration efficiency
• Automatic flagging of suboptimal vibration/flow combinations (Screenshots and a video walkthrough will accompany the live demo.)

How I Used Algolia Agent Studio

SonicFilter AI uses Algolia Agent Studio as a real-time retrieval and decision layer, not as a conversational engine. The system is built around fast, contextual lookups—not generative dialogue.

Indexed Data

I structured three core data categories for indexing:

1. Experimental Metadata

   • Vibrational frequency (Hz)
   • Flow rate (L/min)
   • Turbidity reduction (%)

2. Sensor-Derived Performance Embeddings

   • Encoded behavioral patterns of filtration under varying conditions
   • Enables similarity-based retrieval across historical experiments

3. User-Validated Outcome Tags

   • Labels like successful, unstable, or context-dependent
   • Grounds recommendations in real-world validation

Retrieval-Driven Agent Behavior

Instead of generating text responses, the agent uses retrieval to:

• Rank frequency configurations by empirical efficiency gain
• Flag mismatches between vibration and flow conditions
• Personalize suggestions based on local water composition (e.g., hardness, particulate load)
• Surface optimal settings instantly during system setup

Prompt Engineering

Prompts were designed to be task-specific, deterministic, and output-constrained, ensuring the agent always returns:

• A ranked set of configurations
• Confidence-weighted recommendations
• Actionable calibration steps (e.g., “Set to 20.3 Hz at 1.8 L/min”)

This keeps the system predictable, explainable, and scientifically trustworthy—critical for research and field deployment.

Why Fast Retrieval Matters

In water filtration research—and especially in resource-constrained settings—latency equals friction. Scientists and technicians don’t need explanations; they need the right answer now.

Algolia’s ultra-fast, contextual retrieval enables SonicFilter AI to:

• Replace manual trial-and-error with instant, data-backed recommendations
• Scale seamlessly as experimental datasets grow—without performance degradation
• Deliver insights precisely when users configure their systems

Measured impact:

• >70% reduction in setup time
• Higher consistency in turbidity reduction through reliable acoustic tuning

The AI doesn’t interrupt the workflow—it disappears into it, acting as a silent co-pilot for clean water innovation.

Final Thoughts

SonicFilter AI reimagines what an AI agent can be: not a chatbot, but a proactive, retrieval-first decision engine embedded in critical infrastructure.

By leveraging Algolia Agent Studio’s speed and precision, this project demonstrates how non-conversational AI can quietly advance real-world goals—like equitable access to clean water—by making the right choice at the right moment.

Thank you to the Algolia team for this inspiring challenge.

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