What is Hyperion Consulting?

Hyperion Consulting is a boutique AI consulting firm 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.

Who is Mohammed Cherifi?

Mohammed Cherifi is the founder of Hyperion Consulting with 15+ years of enterprise AI experience at Cisco Systems (100M+ users), Renault-Nissan-Mitsubishi Alliance (€75M budgets, Deputy GM), 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 302+ microservices and 31 AI models—in just 2 months.

Where is Hyperion Consulting located?

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.

What services does Hyperion Consulting offer?

Hyperion Consulting offers four main service categories: (1) AI Strategy Sprint - vendor-agnostic AI roadmap and strategy in weeks, (2) Pilot-to-Production Sprint - 90-day engagement to ship stuck AI projects, (3) Production AI Systems - RAG optimization, fine-tuning, and LLMOps infrastructure, (4) AI Development Training - the 10x methodology behind building 1M+ lines of production code in 2 months.

What is the DEPLOY Method?

The DEPLOY Method™ is Hyperion's proven framework for shipping AI: Diagnose the real problem, Engineer for production (not demos), Pilot with clear graduation criteria, Launch to real users, Optimize continuously, and Yield measurable business impact with capability transfer. Built on 15 years of shipping AI at Cisco, Renault-Nissan-Mitsubishi, and ABB.

What is a RAG system and how can Hyperion help?

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.

Why do 70% of AI pilots never reach production?

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's DEPLOY Method addresses each of these failure modes.

How can I fix my stuck AI project?

Hyperion's Pilot-to-Production Sprint is designed exactly for this. In 90 days, we diagnose why your project is stuck, define clear graduation criteria, address production requirements, and ship to real users—or pivot with purpose. The key is embedded AI leadership with one mandate: ship, not advise indefinitely.

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

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.

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

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) Vendor-agnostic with no partnerships or kickbacks—recommendations are based on what's right for you. Plus, our goal is to transfer capability and work ourselves out of a job.

What industries does Hyperion Consulting serve?

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 to 30+ startups).

How do I book a consultation with Hyperion Consulting?

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.

What languages does Hyperion Consulting support?

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.

How much does AI consulting cost?

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.

What is the ROI of AI consulting?

ROI varies by engagement type: AI Strategy Sprints typically save 6-12 months of wasted effort by avoiding wrong technology bets. Pilot-to-Production Sprints save €500K+ on average by ending pilot purgatory. Production AI Systems engagements deliver measurable improvements—one client reduced support costs 30% with a RAG-based customer service system. We define success metrics before writing code, so ROI is measurable, not theoretical.

What is the difference between RAG and fine-tuning?

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.

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

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's DEPLOY Method™ compresses these timelines by addressing production requirements from day one.

Should we use open-source or proprietary AI models?

It depends on volume, data sensitivity, and customization needs. Proprietary APIs (GPT-4, Claude) 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 provides vendor-agnostic guidance to find the right fit.

What does an AI consulting engagement look like?

A typical engagement starts with a free 30-minute discovery 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.

Do you help with EU AI Act compliance?

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.

Can you help if we have no AI experience?

Absolutely. Many clients come to us at the beginning of their AI journey. Our AI Strategy Sprint is designed for exactly this—cutting through vendor noise to identify where AI creates real value for your specific business. We assess your data readiness, team capabilities, and organizational context, then deliver a practical roadmap with clear ROI estimates. No prior AI experience required. We also offer a risk-free AI Diagnostic: if we don't identify at least 3 high-ROI AI opportunities, you pay nothing.

What makes a good AI use case for enterprise?

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.

Zurück zu FallstudienAutomobil & EV

Auralink: Enterprise-Grade EV-Ladeplattform

319 Microservices, ~20 KI-Agenten, von 1 Entwickler in 2 Monaten gebaut

Internes Hyperion-Projekt

Zeitrahmen: 2 Monate

Dezember 2024

319

Cloud-Microservices

KI/ML-Services

200+

Partnerintegrationen

10-20x

Produktivitätsmultiplikator

Über den Kunden

Ein internes Flaggschiff-Projekt, das die KI-erweiterte Entwicklungsmethodik demonstriert—aufbauend was traditionell ein 25-Personen-Team und ein 12M€-Budget erfordern würde.

Größe: 1 Entwickler

Die Herausforderung

Aufbau einer autonomen EV-Ladeinfrastruktur-Plattform auf Enterprise-Ebene von Grund auf.

319 Cloud-Microservices zur Orchestrierung von Ladevorgängen über verteilte Netzwerke

56 Edge-Computing-Services für lokale Echtzeit-Intelligenz mit <50ms Latenz

31 KI/ML-Services einschließlich Multi-Agent Reinforcement Learning und physik-informierter neuronaler Netzwerke

Vollständiges Ladegerät-Betriebssystem in Rust mit sicherheitskritischen Zertifizierungen (ISO 15118-20, OCPP 2.0.1)

200+ Partnerintegrationen über Energienetze, Zahlungsanbieter und Flottenmanagement-Systeme

Traditionelle Entwicklung würde 25-30 Ingenieure, 2-3 Jahre und ein Budget von 10-15M€ erfordern

Unsere Lösung

Nutzung von KI-erweiterter Entwicklung zum Aufbau einer Enterprise-Plattform mit einem 10-20x Produktivitätsmultiplikator.

Eine vierstufige verteilte Architektur nach Mustern der Technologiegiganten: Cloud (319 Microservices) → Edge (56 Services) → Agent (7 spezialisierte KI-Agenten) → ChargerOS (18 Rust Crates). Jede Komponente produktionsbereit mit umfassenden Tests, Sicherheitshärtung und Compliance-Zertifizierungen.

Implementierungsphasen

Architektur & Design

Entwurf einer produktionsreifen vierstufigen verteilten Architektur mit klaren Service-Grenzen, API-Verträgen (OpenAPI, AsyncAPI) und Infrastructure-as-Code (Terraform, Kubernetes).

1 Woche

Cloud-Plattform (AuralinkCloud)

Aufbau von 319 Go-Microservices: Ladevorgänge, Abrechnung, Flottenmanagement, Netzintegration, Analytics und Enterprise-Sicherheitsplattform mit SIEM (100K+ Events/Sek).

3 Wochen

KI/ML-Plattform (AuralinkAI)

Implementierung von 31 KI-Services: CAMAC-DRA Multi-Agent RL (92% Koordinationserfolg), hybride Bedarfsprognose (<5% MAE), physik-informierte neuronale Netzwerke für Batterie-SoC/SoH (±1,5% Genauigkeit) und XAI für EU AI Act Compliance.

2 Wochen

Edge & ChargerOS

Entwicklung von 56 Edge-Services auf Embedded Linux mit 72+ Stunden Offline-Betrieb, plus vollständiges Rust-Ladegerät-OS (18 Crates) mit Unterstützung für CCS, CHAdeMO, NACS und V2G bidirektionales Laden.

2 Wochen

Technologien & Ansätze

Go 1.23+RustPythonTypeScriptFlutterPostgreSQL (14 Instanzen)TimescaleDBNATS JetStreamKafkaKong GatewayKubernetesTerraformOCPP 2.0.1ISO 15118-20Hyperledger Fabric

Ergebnisse & Wirkung

Lieferung einer produktionsbereiten Enterprise-EV-Ladeplattform in 2 Monaten—entspricht dem, was traditionell ein 25-Personen-Team, 2-3 Jahre und ein 10-15M€-Budget erfordern würde.

319

Cloud-Microservices

Komplette Enterprise-Plattform in Go

KI/ML-Services

Inkl. Multi-Agent RL und PINNs

200+

Partnerintegrationen

Energie-, Zahlungs-, Flotten-Ökosysteme

10-20x

Produktivitätsmultiplikator

vs. traditionelles 25-Personen-Team

“Auralink demonstriert, dass sich die Wirtschaftlichkeit der Softwareentwicklung grundlegend verändert hat. Was einst Millionen an Kapital und Jahre der Entwicklung erforderte, kann jetzt von einem qualifizierten Entwickler mit KI-Unterstützung erreicht werden. Das ist die Methodik, die ich jetzt lehre.”

Mohammed Cherifi

Gründer, Hyperion Consulting

Gelieferte Services

KI-erweiterte Entwicklung

Architektur verteilter Systeme

KI/ML-Engineering

Edge Computing

Sicherheitskritische Systeme

Bereit für ähnliche Ergebnisse?

Lassen Sie uns besprechen, wie wir Ihnen helfen können, Ihre spezifischen Herausforderungen zu bewältigen und messbare Geschäftsergebnisse zu erzielen.

Zurück zu FallstudienAutomobil & EV

Auralink: Enterprise-Grade EV-Ladeplattform

319 Microservices, ~20 KI-Agenten, von 1 Entwickler in 2 Monaten gebaut

Internes Hyperion-Projekt

Zeitrahmen: 2 Monate

Dezember 2024

319

Cloud-Microservices

KI/ML-Services

200+

Partnerintegrationen

10-20x

Produktivitätsmultiplikator

Über den Kunden

Ein internes Flaggschiff-Projekt, das die KI-erweiterte Entwicklungsmethodik demonstriert—aufbauend was traditionell ein 25-Personen-Team und ein 12M€-Budget erfordern würde.

Größe: 1 Entwickler

Die Herausforderung

Aufbau einer autonomen EV-Ladeinfrastruktur-Plattform auf Enterprise-Ebene von Grund auf.

319 Cloud-Microservices zur Orchestrierung von Ladevorgängen über verteilte Netzwerke

56 Edge-Computing-Services für lokale Echtzeit-Intelligenz mit <50ms Latenz

31 KI/ML-Services einschließlich Multi-Agent Reinforcement Learning und physik-informierter neuronaler Netzwerke

Vollständiges Ladegerät-Betriebssystem in Rust mit sicherheitskritischen Zertifizierungen (ISO 15118-20, OCPP 2.0.1)

200+ Partnerintegrationen über Energienetze, Zahlungsanbieter und Flottenmanagement-Systeme

Traditionelle Entwicklung würde 25-30 Ingenieure, 2-3 Jahre und ein Budget von 10-15M€ erfordern

Unsere Lösung

Nutzung von KI-erweiterter Entwicklung zum Aufbau einer Enterprise-Plattform mit einem 10-20x Produktivitätsmultiplikator.

Implementierungsphasen

Architektur & Design

Entwurf einer produktionsreifen vierstufigen verteilten Architektur mit klaren Service-Grenzen, API-Verträgen (OpenAPI, AsyncAPI) und Infrastructure-as-Code (Terraform, Kubernetes).

1 Woche

Cloud-Plattform (AuralinkCloud)

Aufbau von 319 Go-Microservices: Ladevorgänge, Abrechnung, Flottenmanagement, Netzintegration, Analytics und Enterprise-Sicherheitsplattform mit SIEM (100K+ Events/Sek).

3 Wochen

KI/ML-Plattform (AuralinkAI)

2 Wochen

Edge & ChargerOS

2 Wochen

Technologien & Ansätze

Go 1.23+RustPythonTypeScriptFlutterPostgreSQL (14 Instanzen)TimescaleDBNATS JetStreamKafkaKong GatewayKubernetesTerraformOCPP 2.0.1ISO 15118-20Hyperledger Fabric

Ergebnisse & Wirkung

Lieferung einer produktionsbereiten Enterprise-EV-Ladeplattform in 2 Monaten—entspricht dem, was traditionell ein 25-Personen-Team, 2-3 Jahre und ein 10-15M€-Budget erfordern würde.

319

Cloud-Microservices

Komplette Enterprise-Plattform in Go

KI/ML-Services

Inkl. Multi-Agent RL und PINNs

200+

Partnerintegrationen

Energie-, Zahlungs-, Flotten-Ökosysteme

10-20x

Produktivitätsmultiplikator

vs. traditionelles 25-Personen-Team

“Auralink demonstriert, dass sich die Wirtschaftlichkeit der Softwareentwicklung grundlegend verändert hat. Was einst Millionen an Kapital und Jahre der Entwicklung erforderte, kann jetzt von einem qualifizierten Entwickler mit KI-Unterstützung erreicht werden. Das ist die Methodik, die ich jetzt lehre.”

Mohammed Cherifi

Gründer, Hyperion Consulting

Gelieferte Services

KI-erweiterte Entwicklung

Architektur verteilter Systeme

KI/ML-Engineering

Edge Computing

Sicherheitskritische Systeme

Bereit für ähnliche Ergebnisse?

Lassen Sie uns besprechen, wie wir Ihnen helfen können, Ihre spezifischen Herausforderungen zu bewältigen und messbare Geschäftsergebnisse zu erzielen.