AI Shopping Assistant

Note: The client, a well-established player in the European product comparison space, has requested anonymity as this product is currently in the Pre-Seed / Validation Phase.

This platform addresses a critical gap in the European product comparison market: Search Abandonment. Traditional keyword-based search fails when users express complex intent ("find a quiet mechanical keyboard under €100 for mac"), leading to high bounce rates and lost revenue.

The challenge was not just technical ("can we build it?") but existential ("can we build it fast, cheap, and good?"). As the Principal Engineer, I owned the entire product lifecycle - from Pre-Seed strategy to Staff-level architecture and high-fidelity UX.

The result was a high-quality, multi-platform PoC delivered in just 5 weeks. It was fully deployed, enabling the client to successfully demo to investors and validate the core concept.

💼 Venture Strategy: Lean Execution & De-risking

In a pre-seed environment, speed is currency and risk is the enemy. My strategy prioritized de-risking the product delivery and optimizing unit economics from Day 1.

1. Strategic De-Coupling

Facing uncertain App Store review timelines (20+ days), I architected the core logic as a platform-agnostic API. This allowed me to spin up a high-fidelity Next.js web client in parallel with the iOS build.

• Result: We delivered a fully functional web demo to stakeholders weeks before Apple approval, ensuring the funding milestone was met regardless of external gatekeepers.

2. Unit Economics (SOTA & Cost-Effective)

Standard Agent implementations are token-heavy and expensive. I designed a pipeline that respects startup constraints:

• Batch Embedding: We utilized batch processing for vector ingestion, reducing ingestion costs by over 50% compared to standard synchronous usage.
• Code Writer Pattern: By moving logic out of the LLM context and into a Python sandbox (see below), we reduced per-query token usage by over 90%, making the unit economics viable for a free-tier B2C product.

🏗️ System Architecture: Control & Predictability

"Agentic AI" often implies unpredictable black boxes. I rejected this for a Pattern-Matching Architecture that prioritizes control, testability, and deterministic outcomes.

The "Code Writer" Pattern vs. Tool Calling

Standard Tool-Calling Agents (using MCP or OpenAI Functions) suffer from Context Bloat and Linear Chaining. Each step (filter → sort → augment) requires a "ping-pong" roundtrip to the LLM.

I adopted a Code Generation & Execution architecture:

1. Composition over Chaining: The agent writes a single Python script to perform complex logic (loops, conditionals, joins) in one turn.
2. The Sandbox Wall: Heavy data processing happens inside a secure, dockerized runtime. The LLM acts as the Architect, defining the logic, but never sees the raw rows.
3. Resilience: I utilized a "Two-Variable Resilience Pattern", ensuring the agent preserves both filtered and unfiltered datasets in memory. If a strict filter returns zero results, the runtime automatically falls back to the broader set, gracefully handling ambiguity ("tight space" vs "folding bike").

This allows deterministic control flow for business logic while leveraging the LLM solely for what it does best: intent understanding.

🚀 Product Engineering: The Trust Equation

In AI products, Perceived Latency is more important than Actual Latency. A 10-second black-box wait feels like a broken app; a 10-second transparent process feels like "hard work".

1. Server-Sent Events (SSE) Streaming

We architected the backend graph to emit granular "lifecycle events" (Routing, Searching, Synthesizing) via SSE.

• UX Impact: We transform a "waiting" experience into an "observing" experience. Users see the agent "Checking store inventory..." or "Reading reviews...", building trust in the final answer.

2. Native Fidelity (iOS)

We chose SwiftUI over cross-platform frameworks to guarantee 60fps animations and haptic feedback. In the "Quality" era of AI apps, the tactile experience is a competitive differentiator.

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This case study demonstrates that you don't need a team of 50 to build SOTA AI products. You need experienced engineers with an execution-focused mindset and AI-First "Product Engineering" skills.