AI, deployed where your business runs.
We embed AI-infused senior engineers with more than 20 years of technology experience into enterprise teams to turn AI into secure, efficient production systems - from cloud and private infrastructure to the edge and robotics.
Stop demoing AI. Deploy it.
- 01 Define Mission · workflow · data · constraints
- 02 Build Code · agents · integration
- 03 Evaluate Tests · security · human control
- 04 Deploy Cloud · private · edge · robot
- 05 Operate Observe · learn · transfer
Technology selected by the mission.
Enterprise copilots, coding agents, open agent frameworks, cloud platforms, local runtimes, and open-weight systems can enter the same deployment architecture. We choose by data boundary, quality, portability, and cost—not by vendor quota.
- Azure AI Foundry Enterprise AI platform
- Azure AI Foundry Local On-device AI runtime
- Microsoft Copilot Work copilot
- GitHub Copilot Coding agent
- Replit Build + deployment
- OpenAI Model platform
- Anthropic Model platform
- xAI / Grok Model platform
- Kimi Open model family
- Ollama Local model runtime
- Hermes Agent Open agent framework
- OpenClaw Personal agent runtime
Named for technical transparency; no exclusivity, endorsement, or automatic fit is implied.
One engineering line from model to machine.
The useful question is not which model is fashionable. It is where intelligence belongs, what it may access, how it fails safely, and who can operate it after launch.
Enterprise AI systems
Select, integrate, evaluate, govern, and operate AI around a measurable business outcome.
02Agents and automation
Bounded AI agents and agentic coding with real tools, repository context, approvals, observability, and human control.
03Private and local AI
Keep sensitive models, retrieval, and data paths inside infrastructure you control.
04Cloud and hybrid AI
Use provider scale without surrendering architecture, portability, or cost discipline.
05Robotics and embodied AI
Connect perception, edge inference, orchestration, and enterprise workflows to physical systems.
06Personal Robotics Lab
Prepare for trusted personal robots through research, service design, integration, and responsible pilots.
One simple delivery model. One commercial rule.
First 2 hours Scope setting · no charge
USD 300 Agentic AI usage included per engineering day
3–5× Value ambition versus conventional delivery · not a guaranteed average
Each billed day combines senior engineering with agentic AI execution. The same rate applies across every MH – Applied AI service, and the expected number of days is agreed before delivery begins.
The pilot is rarely the hard part.
Production exposes the real work: identity, data boundaries, evaluation, integration, ownership, adoption, failure handling, and cost. We engineer those conditions with the people who will run the system.
- Operational ownership before hand-off
- Security and governance inside the architecture
- Model and token economics measured in context
- Human adoption designed into the workflow
Senior engineers, deployed into the mission.
A small core team works inside your operational reality. We add only the specialists the mission needs, build with your people, and leave behind a system your organization can own.
ApproachFrame the outcome and the constraints.
Join the team and technical environment.
Build, evaluate, and operate the real path.
Transfer capability and measure value.
Evidence has to survive scrutiny.
We publish client work only when the problem, constraints, delivery role, and outcome can be verified. Until client-approved dossiers are ready, we will not fill the gap with invented percentages or logo walls.
WorkFounder track record is attributed to Marcel Haas; company claims remain company-specific.
The next interface has motors.
Enterprise AI is moving from screens into facilities, devices, and homes. Our robotics practice connects software architecture to perception, edge compute, human supervision, and the realities of physical operation.
Explore this capabilityBuilt by an operator, not a pitch deck.
Marcel Haas created MH – Applied AI as the forward-deployed AI offering of Bear Intelligence GmbH. He is co-founder, CEO, and Solution Architect at Collaboration NEXT GmbH, working with SharePoint since 2006, Microsoft cloud since BPOS in 2009, and applied AI today.
AboutExecutive questions, direct answers.
What does a forward-deployed AI engineer do?
A forward-deployed engineer works inside the client environment with business and technical teams. The role spans discovery, architecture, integration, evaluation, deployment, adoption, and operational transfer rather than stopping at a prototype.
Can you deploy AI on our own infrastructure?
Yes. The architecture can run in public cloud, private cloud, on-premises infrastructure, edge devices, or a hybrid combination. Placement follows data sensitivity, latency, cost, resilience, and operating requirements.
Do you require a specific cloud or model vendor?
No. MH – Applied AI is vendor-independent and open-source-first where that improves control, economics, or portability. Commercial platforms remain valid when they are the best fit for the mission.
How do you reduce AI token usage and operating cost?
We start with task and quality requirements, then reduce unnecessary context, route work to the right model, cache stable results, measure retrieval quality, and use deterministic software where a model adds no value.
What does an MH – Applied AI engagement cost?
The first two hours are free and used to set scope. Billing then starts at USD 2,800 per engineering day. The rate covers People + AI delivery and includes USD 300 of agentic AI usage per billed day.
Is personal robotics a current service?
It is an emerging practice. We currently focus on readiness, research, integration architecture, privacy, service design, and carefully bounded pilots rather than presenting home robots as a mature mass-market offering.
Bring us the mission, not a shopping list.
In the first conversation we map the operational outcome, constraints, deployment environment, and the shortest credible path to evidence.
Book an AI deployment consultation