Question 1

What makes Particula different from other AI companies?

Accepted Answer

Two things. First, we specialize in on-premise and private AI: systems that run on your infrastructure, so sensitive data never leaves your control. Second, we treat AI as engineering, not magic. Most AI projects fail because teams don't understand the difference between a demo and a production system. The AI systems we ship handle real traffic and real edge cases, and we're honest about limits: sometimes the best answer is 'don't use AI for this.' When we build something, it ships and stays shipped.

Question 2

What does 'AI Engineering Studio' mean?

Accepted Answer

We combine development and research, but everything we do is grounded in production outcomes. We're not a generic dev agency that bolts on AI, and we're not a research lab that stops at papers: when we do publish research, it comes from problems we hit in production. We build custom models, implement cutting-edge techniques, and ship systems that work under real conditions. The research serves real use cases. The development is built to scale.

Question 3

What industries do you specialize in?

Accepted Answer

Healthcare, legal, finance, manufacturing, and retail. We've built medical AI processing 200K monthly scans, legal document systems maintaining attorney-client privilege, financial data extraction with 99%+ accuracy, and manufacturing quality control with computer vision. Our vertical-specific models (Particula-Healthcare, Particula-Legal, Particula-Finance) reflect this deep domain expertise.

Question 4

What are Particula Models?

Accepted Answer

Our proprietary AI models, all 7 billion parameters or smaller. They're purpose-built for specific tasks, JSON generation, text classification, code generation, and domain-specific work in healthcare, legal, and finance. Because they're focused rather than general-purpose, they outperform larger models on their specific tasks while being dramatically more efficient to run.

Question 5

Why are your models so much cheaper than OpenAI or Anthropic?

Accepted Answer

Purpose-built versus general-purpose. Our models start at $0.03 per million tokens, compared to $1-75 for comparable API providers. A compact model trained specifically for JSON generation doesn't need 175 billion parameters, it needs the right 7 billion. This means 30-2500x cost savings at scale, plus the option to run on your own infrastructure with no per-token fees at all.

Question 6

Can I run Particula Models on my own infrastructure?

Accepted Answer

Yes. All our models are designed for on-premise deployment. This is essential for healthcare (HIPAA), legal (attorney-client privilege), finance (data sovereignty), and any organization that can't send sensitive data to external APIs. You control the infrastructure, the data never leaves your environment, and there are no per-token costs after deployment.

Question 7

Do you only deploy on-premise, or do you also work with cloud and API models?

Accepted Answer

Self-hosted is our specialty and our default: on-premise, private cloud, air-gapped when required. But cloud optional means exactly that. When a workload has no sensitive data and a managed API is the pragmatic choice, we build on cloud infrastructure and commercial APIs as well. We recommend what the problem needs, not what our preference is, and plenty of good systems mix both.

Question 8

What happens if AI isn't the right solution for my problem?

Accepted Answer

We tell you. During our initial assessment, we evaluate whether AI genuinely solves your problem better than alternatives. Sometimes a well-designed database query or a simple rules engine is the right answer. We only take on projects where we've identified a specific problem AI solves better than traditional approaches. No point building something impressive that doesn't actually help.

Question 9

How long does a typical AI project take?

Accepted Answer

It depends on complexity, but we ship iteratively. Most projects have working components within 2-4 weeks, not months of planning before you see anything. A focused model fine-tuning project might take 4-6 weeks. A full production system with custom models, integration, and monitoring typically runs 2-4 months. We don't do 12-month roadmaps that deliver nothing until the end.

Question 10

Do you build proof-of-concepts or production systems?

Accepted Answer

Production systems. We don't do proof-of-concepts that gather dust or demos that break under real traffic. Every system we build is designed to handle concurrent requests, version control, drift monitoring, and the edge cases that happen when real users interact with it. If you need a quick validation, we can do that, but it's still built properly.

Question 11

Can you work with our existing AI system that's underperforming?

Accepted Answer

Yes, this is common. Many clients come to us with AI systems that worked in development but struggle in production, accuracy drops, latency spikes, costs explode at scale. We diagnose the root causes, optimize or rebuild as needed, and implement proper monitoring. Sometimes it's a model issue, sometimes it's infrastructure, sometimes it's how the system handles edge cases.

Question 12

How do you handle sensitive data and compliance requirements?

Accepted Answer

We deploy on-premise when needed, sign NDAs before any discussion, and comply with GDPR, HIPAA, CCPA, and industry-specific regulations. We never train models on your proprietary data without explicit permission. For healthcare, legal, and financial clients, we've built systems specifically designed to maintain data sovereignty and regulatory compliance.

Question 13

What's the first step to working together?

Accepted Answer

A 30-minute consultation where we assess fit. We'll ask about your problem, your data, your constraints, and your timeline. We'll be direct about whether we can help and what it would take. No commitment required, no sales pitch, just a practical conversation about whether there's a good match. Book directly on this site.

Everything you need to know about working with us.

Everything you need to know about working with us.