Question 1

What is Hyperion Consulting?

Accepted Answer

Hyperion Consulting is an AI-native Physical AI consulting practice founded by Mohammed Cherifi that helps European companies ship AI that actually works. Unlike traditional consultancies, we combine strategic advisory with hands-on implementation—from AI strategy through production deployment. We specialize in RAG systems, LLM implementation, EU AI Act compliance, and AI product management.

Question 2

Who is Mohammed Cherifi?

Accepted Answer

Mohammed Cherifi is the founder of Hyperion Consulting with 17+ years of enterprise AI experience at Cisco Systems (100M+ users), Renault-Nissan-Mitsubishi Alliance (Deputy GM, led major programs), and ABB E-mobility. He's a Forbes Technology Council member since 2021, French Government AI Ambassador for the 'Osez l'IA' initiative, and Berkeley SkyDeck Key Advisor. He built Auralink—a complete EV charging platform with 400+ microservices and ~20 AI agents—in just 2 months.

Question 3

Where is Hyperion Consulting located?

Accepted Answer

Hyperion Consulting is headquartered in Boulogne-Billancourt, France (near Paris), with presence in Athens, Greece and Amsterdam, Netherlands. We serve clients throughout the European Union, with particular expertise in France, Germany, Netherlands, and Greece.

Question 4

What services does Hyperion Consulting offer?

Accepted Answer

Hyperion Consulting delivers Physical AI engineering through three engagements for industrial verticals (robotics, automotive and mobility, aerospace, energy and infrastructure, logistics, maritime, and embedded/edge as a cross-cutting layer): a Production Readiness Review (the entry diagnostic against production-grade architecture, safety, and operations), a Physical AI Deployment program (pilot-to-production hardening of one system), and a Physical AI Operating Partner retainer (ongoing architecture, operations, and governance leadership). EU AI Act alignment throughout; sovereign-first model selection, with Mistral and open-weight models preferred for edge and on-prem deployments.

Question 5

How does Hyperion deliver AI from pilot to production?

Accepted Answer

Hyperion delivers through three engagements. A Production Readiness Review — readiness assessment and EU AI Act gap analysis producing a prioritized blocker map and a clear production decision. A Physical AI Deployment program — resolving the blockers and preparing one system for a controlled, production-grade rollout (open-weight LLM adaptation, agentic systems, edge deployment, and pilot-to-production hardening). And a Physical AI Operating Partner retainer — ongoing production, architecture, and governance leadership, with EU AI Act conformance and capability transfer. Built on 17 years of shipping AI at Cisco, Renault-Nissan-Mitsubishi, and ABB.

Question 6

What is a RAG system and how can Hyperion help?

Accepted Answer

RAG (Retrieval-Augmented Generation) is an AI architecture that combines large language models with your organization's knowledge base to provide accurate, contextual responses. Hyperion helps design, build, and optimize production RAG systems—fixing common issues like hallucinations, latency problems, and cost overruns that plague demo-quality implementations.

Question 7

Why do most AI pilots never reach production?

Accepted Answer

Most AI pilots fail due to: (1) No clear graduation criteria—'pilot' becomes a permanent state, (2) Demo-quality architecture that breaks under real-world load, (3) Gap between technical teams and business stakeholders, (4) Missing production requirements like security, compliance, and monitoring, (5) Consultant dependency without capability transfer. Hyperion addresses each of these failure modes across its three engagement phases — Diagnose & Review, Build & Deploy, and Operate & Scale.

Question 8

How can I fix my stuck AI project?

Accepted Answer

Hyperion's Physical AI Deployment program is designed exactly for this. We diagnose why the system is stuck, define clear graduation criteria against production-grade requirements, resolve the blockers, and ship to real users — or stop with a clear decision. The mandate is embedded engineering leadership: ship a dependable system, not advise indefinitely.

Question 9

What is EU AI Act compliance and do I need it?

Accepted Answer

The EU AI Act is the world's first comprehensive AI regulation, with high-risk requirements taking effect August 2026. Fines reach €35 million or 7% of global turnover. If you operate in the EU and use AI for hiring, credit decisions, law enforcement, education, or critical infrastructure, you likely need compliance. Hyperion helps with system inventory, risk classification, documentation, and governance implementation.

Question 10

How is Hyperion different from big consulting firms like McKinsey or Accenture?

Accepted Answer

Three key differences: (1) You work directly with senior expertise—not junior teams supervised by partners who show up for presentations, (2) We both advise AND build—from strategy presentations to production code, (3) Mistral-specialist with sovereign-first model selection—no hidden partnerships or kickbacks, recommendations based on what's right for you. Plus, our goal is to transfer capability and work ourselves out of a job.

Question 11

What industries does Hyperion Consulting serve?

Accepted Answer

Hyperion has deep expertise in: (1) Automotive & Mobility—connected vehicles, EV charging, software-defined vehicles (experience at Renault-Nissan-Mitsubishi and ABB), (2) Technology & Enterprise—platform strategy, AI infrastructure, production systems (experience at Cisco), (3) European SMEs & Scale-ups—AI strategy, EU AI Act compliance, practical AI adoption (Berkeley SkyDeck advisor since 2022).

Question 12

How do I book a consultation with Hyperion Consulting?

Accepted Answer

Book a free 30-minute call at hyperion-consulting.io/book. No pitch, no pressure—just an honest conversation about your AI challenges and whether we can help. If we're not the right fit, we'll tell you and point you in a better direction. Response within 24 hours.

Question 13

What languages does Hyperion Consulting support?

Accepted Answer

Mohammed Cherifi speaks English, French, and Arabic fluently. The Hyperion website is available in 7 languages: English, French, German, Dutch, Greek, Arabic, and Japanese. Services are primarily delivered in English and French.

Question 14

How much does AI consulting cost?

Accepted Answer

Hyperion offers three engagement models: (1) Sprint engagements (2-4 weeks, fixed-price) for strategy and assessment, (2) Project engagements (4-12 weeks, scoped milestones) for pilot-to-production and implementation, (3) Embedded advisory (monthly retainer) for fractional AI leadership. All engagements are fixed-scope, fixed-price—no ballooning timelines or change orders. Contact us for specific pricing based on your needs.

Question 15

What is the ROI of AI consulting?

Accepted Answer

ROI varies by engagement. A Production Readiness Review saves months of wasted effort by surfacing the real blockers before you commit to a build; a Physical AI Deployment program gets a system past its production stage gate and out of pilot purgatory. We define success metrics before writing code, so ROI is measurable, not theoretical.

Question 16

What is the difference between RAG and fine-tuning?

Accepted Answer

RAG (Retrieval-Augmented Generation) keeps your data separate and retrieves relevant context at query time—best for knowledge bases, documents, and frequently updated information. Fine-tuning modifies the model's weights with your data—best for specialized behavior, consistent formatting, or domain-specific language. Most enterprise use cases benefit from RAG first, with fine-tuning added for specific tasks. Hyperion helps determine the right approach based on your data, latency requirements, and cost constraints.

Question 17

How long does it take to build a production AI system?

Accepted Answer

Timeline depends on complexity: A focused RAG system or chatbot can reach production in 4-8 weeks. Multi-model AI agents with tool use typically take 8-16 weeks. Enterprise-wide AI infrastructure (MLOps, monitoring, governance) takes 3-6 months. The biggest time sink is usually not the AI itself—it's data preparation, integration with existing systems, and organizational alignment. Hyperion compresses these timelines by addressing production requirements from day one.

Question 18

Should we use open-source or proprietary AI models?

Accepted Answer

It depends on volume, data sensitivity, and customization needs. Proprietary frontier APIs are best for experimentation and low-volume use. Open-source models (Llama, Mistral, Qwen) are better for high-volume production (10M+ requests/month), data sovereignty requirements, or heavy customization. The cost crossover point where self-hosting becomes cheaper than APIs is typically 100K-1M monthly requests. Hyperion applies sovereign-first, open-weight-first selection to find the right fit.

Question 19

What does an AI consulting engagement look like?

Accepted Answer

A typical engagement starts with a free 30-minute fit call to understand your challenges. If we're a fit, we propose a scoped engagement: (1) Week 1—deep-dive discovery and stakeholder interviews, (2) Weeks 2-3—analysis, architecture design, and recommendation development, (3) Final week—executive presentation with prioritized roadmap, ROI estimates, and implementation plan. For implementation engagements, we work embedded with your team 2-3 days/week, with weekly progress reviews.

Question 20

Do you help with EU AI Act compliance?

Accepted Answer

Yes. The EU AI Act's high-risk requirements take full effect August 2026, with fines up to €35 million or 7% of global turnover. Hyperion helps with: AI system inventory and risk classification, gap analysis against Articles 9-15 requirements, technical remediation (bias testing, explainability, human oversight), compliance documentation, and ongoing monitoring. We use established frameworks like Fairlearn, SHAP, and MLflow rather than building from scratch.

Question 21

Can you help if we have no AI experience?

Accepted Answer

Absolutely. Many clients come to us early. A Production Readiness Review is designed for exactly this — cutting through vendor noise to identify whether a Physical AI initiative can reach dependable production, and what it takes. We assess your data and system readiness, team capabilities, and operational context, then deliver a practical roadmap with a clear production decision. No prior AI experience required.

Question 22

What makes a good AI use case for enterprise?

Accepted Answer

The best enterprise AI use cases share four characteristics: (1) High volume—the task happens frequently enough to justify automation, (2) Clear success criteria—you can measure if the AI is working, (3) Available data—quality training or retrieval data exists or can be created, (4) Human fallback—errors can be caught and corrected without catastrophic consequences. Common high-ROI starting points include customer service automation, document processing, knowledge management, and sales intelligence.

Physical AI，从试点到可靠的生产。

反复做试点。把系统工程化。

诊断与评审 → 构建与部署 → 运营与扩展

同一套系统，三种配置。

汽车与出行

制造与自动化

能源、电动车充电与基础设施

证据 — 诚实分类。

Reachy Mini + SO-101

Auralink

Mohammed Cherifi

Physical AI，从试点到可靠的生产。

反复做试点。把系统工程化。

诊断与评审 → 构建与部署 → 运营与扩展

同一套系统，三种配置。

汽车与出行

制造与自动化

能源、电动车充电与基础设施

证据 — 诚实分类。

Reachy Mini + SO-101

Auralink

Mohammed Cherifi

The 90-Day Pilot-to-Production Clock for Physical AI

AI Research Decoded: The Reality Check for Embodied AI Deployments

AI Research Decoded: The Reality Gap in Physical AI – Benchmarks, Shortcuts, and Real-World Readiness

从试点到可靠的生产。

Physical AI，从试点到可靠的生产。

反复做试点。 把系统工程化。

诊断与评审 → 构建与部署 → 运营与扩展

同一套系统，三种配置。

汽车与出行

制造与自动化

能源、电动车充电与基础设施

证据 — 诚实分类。

Reachy Mini + SO-101

Auralink

Mohammed Cherifi

Physical AI，从试点到可靠的生产。

反复做试点。 把系统工程化。

诊断与评审 → 构建与部署 → 运营与扩展

同一套系统，三种配置。

汽车与出行

制造与自动化

能源、电动车充电与基础设施

证据 — 诚实分类。

Reachy Mini + SO-101

Auralink

Mohammed Cherifi

The 90-Day Pilot-to-Production Clock for Physical AI

AI Research Decoded: The Reality Check for Embodied AI Deployments

AI Research Decoded: The Reality Gap in Physical AI – Benchmarks, Shortcuts, and Real-World Readiness

从试点到可靠的生产。

反复做试点。把系统工程化。

反复做试点。把系统工程化。