AI Engineering Studio

AI Is a Tool, Not Magic.We Build It That Way.

Our Services

At Particula Tech, we build AI systems that work in production. Custom models, intelligent automation, and systems that improve as they learn from your data.

Most AI projects fail because teams treat them like normal software. We know the difference.

AI Development

We build custom models that learn from your data, not generic APIs wrapped in promises. Systems that handle edge cases, adapt to your domain, and actually ship to production.

AI Research

We implement techniques from recent papers, design novel architectures for problems that don't have off-the-shelf solutions, and validate approaches before you commit resources.

AI Consulting

We tell you what will work, what won't, and why. No vendor lock-in recommendations. No selling you AI for problems that don't need it.

Our Philosophy

AI isn't magic. It's engineering. We build it that way.

Engineering Over Hype

Most AI fails because teams treat it like magic. We treat it like what it is—software that needs architecture, testing, and maintenance.

Honest About Limits

Sometimes the answer is "don't use AI for this." We only build where AI genuinely solves the problem better than alternatives.

Ships and Stays Shipped

We don't do proof-of-concepts that gather dust. When we build something, it handles real traffic and real edge cases.

Selected Work

A showcase of our technology solutions and digital transformations that demonstrate our expertise in creating innovative, future-ready systems.

AI & Data Infrastructure

Intelligent Data Platform with Multi-RAG Architecture

Enterprise data hub with CAG, RAG, and GraphRAG - 14 sources unified, 2.4M daily records, 85% faster reporting.

AI Infrastructure & Cloud Migration

Medical AI Infrastructure Migration

Cloud-to-on-premise migration for Swiss healthcare AI processing 200K monthly scans, reducing costs 95%.

AI & Machine Learning

AI-Powered Quality Control for Manufacturing

Automated quality inspection and predictive maintenance system that transformed production operations.

AI Consulting & Analytics

Face Recognition & Predictive Analytics for Construction

Multi-stage AI system eliminating attendance fraud and enabling predictive project management.

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Latest Insights

Explore our latest thoughts on AI, technology, and building systems that scale. Practical insights from real-world experience.

Apr 2, 2026

3 LangChain CVEs in One Week: How to Audit Your AI Framework Stack

Three critical LangChain and LangGraph CVEs dropped in one week—path traversal, deserialization, and SQL injection affecting 52M+ weekly downloads. Here's how to audit and patch your AI framework stack.

Sebastian Mondragon

Apr 1, 2026

Agentic RAG Explained: How Agent-Controlled Retrieval Beats Fixed Pipelines

Traditional RAG pipelines hit 34% accuracy on complex queries. Agentic RAG's agent-controlled retrieval loop—with routing, grading, and self-correction—pushes that to 78%. Here's the architecture and how to build it.

Sebastian Mondragon

Mar 31, 2026

Google ADK vs AWS Strands Agents: Which Cloud Agent Platform to Build On

We deployed the same agent on both Google ADK and AWS Strands. ADK's explicit orchestration gives you deterministic control; Strands' model-driven autonomy ships in seconds on Lambda. Here's how to choose.

Sebastian Mondragon

Mar 30, 2026

Gemini CLI vs Claude Code vs Codex CLI: Terminal AI Agents Compared

We benchmarked all three terminal coding agents on a real Express.js refactor. Claude Code finished in 1h17m, Codex CLI in 1h41m, Gemini CLI in 2h04m—here's what the numbers miss.

Sebastian Mondragon

Mar 27, 2026

Microsoft ZT4AI Explained: Zero Trust for AI Agents in 2026

Microsoft's ZT4AI framework brings Zero Trust to AI agents with 700+ controls across agent governance, data security, and prompt defense. Here's how it works and when to use it over NemoClaw or DIY approaches.

Sebastian Mondragon

Mar 26, 2026

SGLang vs vLLM in 2026: Benchmarks, Architecture, and When to Use Each

SGLang delivers 29% higher throughput than vLLM on H100s and 3.1x faster DeepSeek V3 inference. Here's the architecture breakdown and decision framework for picking the right engine.

Sebastian Mondragon

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