AI · 8 min read
AI Strategy for Mid-Size Companies: From Hype to Practice
How mid-size companies can build an AI strategy that delivers results. A field-tested 5-step approach with concrete use cases and timelines.
sergej Read More →
AI Consulting for the Mid-Market: Strategy & Automation
AI agents, automation and LLM integration for the mid-market: from first use case to production. EverBright IT guides SMEs and mid-size companies through structured AI adoption.
Your Benefits
Fast Time-to-Value
Quick wins from the workshop, PoC results in 4–6 weeks: progress in weeks, not quarters.
Human-in-the-Loop
Automation with checkpoints and approvals: AI assists, humans decide.
Data Sovereignty & Compliance
On-prem or private cloud, EU AI Act in mind: AI that fits your privacy and regulatory needs.
Measurable ROI
Use cases are prioritized by business impact, not by hype.
Our Services
AI Strategy & Roadmap
Tailored AI strategy for your business. From use case identification to implementation roadmap.
Agent Development
Autonomous AI agents that independently solve complex tasks: research, data analysis, code generation, and more.
LLM Integration
Large Language Models integrated into existing systems. API integration, prompt engineering, fine-tuning.
Process Automation
Automate repetitive processes with AI-powered workflows and decision systems.
Packages & Pricing
AI Strategy Workshop
from €2,500
1–2 days + evaluation
- Use case identification & prioritization
- Feasibility and data check
- Concrete roadmap with next steps
Proof of Concept
€15,000–40,000
4–6 weeks
- Prototype with real data
- Measurable success criteria
- Security & privacy by design
- Decision brief for the rollout
Production Rollout
on request Priced by scope
- Integration into existing systems
- MLOps, CI/CD and monitoring
- Governance & EU AI Act compliance
- Enablement of your team
Typically 1.5–3× the PoC budget, depending on integration scope.
Discuss your projectEvery project starts with a free intro call.
Our Approach
From use case identification through training and deployment to continuous monitoring: a structured lifecycle.
01
Discovery
Analysis of existing processes and identification of AI potential.
02
Proof of Concept
Quick prototype with real data. Results in 2–4 weeks.
03
Pilot
Integration into existing systems, feedback loops with stakeholders.
04
Rollout
Scaling, monitoring, governance, and continuous improvement.
Technologies
LLMs & Agents
ClaudeOpenAI GPTGeminiLlamaOllamaRAG PipelinesLangChainMCP
AI & Data Platforms
PyTorchHugging Facescikit-learnPineconeWeaviateChromaDB
MLOps & Infrastructure
MLflowDockerKubernetesAirflowWeights & BiasesCI/CD
Typical Scenarios
Process Automation
Goal:
Reduce manual, repetitive tasks without destabilizing existing systems.
Challenge:
Teams spend hours on routine work. Scattered tools and lack of standardization make AI automation risky.
Our approach:
We identify quick wins, build secure automations with human-in-the-loop, and integrate them step by step.
Private AI on Your Infrastructure
Goal:
Use AI on sensitive data while maintaining compliance and control.
Challenge:
Public SaaS AI tools often fail to meet data privacy and residency requirements.
Our approach:
Models deployed in your cloud or on-prem. Encryption, audit trails, full data sovereignty.
From PoC to Production
Goal:
Move AI projects from experimentation to scalable products.
Challenge:
Prototypes stay prototypes. Missing MLOps, unclear ownership, and ad-hoc models block the path to production.
Our approach:
Structured MLOps pipelines, reproducible training, and CI/CD for models. AI that runs reliably in production.
AI consulting for the mid-market: the guide
When is AI consulting worth it?
AI consulting pays off as soon as recurring knowledge work measurably consumes time: writing proposals, reviewing documents, triaging support requests, summarizing reports. For mid-size companies the threshold is lower than many expect. A team of ten spending an hour a day on routine text work already justifies a structured look at automation. What matters is not company size but whether a process can be clearly described and the required data exists in digital form. That is exactly what the workshop verifies before any budget flows into technology: use cases with business impact first, models and tools second.
How an AI project runs with us
Every project starts with a free intro call and, where it fits, an AI strategy workshop. It produces prioritized use cases, a feasibility check on your real data, and a roadmap with next steps. The subsequent proof of concept delivers a reliable result within four to six weeks that lets you measure the benefit. Only then comes the decision about a production rollout, including integration into existing systems, MLOps, and enablement of your team. This staged approach keeps risk small: no step commits you to the next, and each stage has a defined outcome.
What does AI consulting cost in the mid-market?
Entry is deliberately low-threshold: the AI strategy workshop starts at 2,500 euros, a proof of concept ranges from 15,000 to 40,000 euros depending on scope. The production rollout depends on integration effort and typically lands at 1.5 to 3 times the PoC budget. More important than the absolute number is the sequence: money flows into the rollout only after the PoC has proven the benefit on real data. That keeps the investment risk manageable and predictable at every stage.
GDPR and the EU AI Act from day one
For mid-size companies in Germany, data protection and regulation are not a footnote. On request we deploy models on-premises or in your private cloud, with encryption, audit trails, and clear data flows. We factor in the EU AI Act from the start: from classifying your use cases through transparency obligations to AI literacy in the team. The result is AI adoption that business units, IT, and data protection officers support together instead of having to retrofit safeguards later.
Frequently Asked Questions
What does AI consulting cost for a mid-size company?
The AI strategy workshop starts from €2,500; a proof of concept ranges from €15,000–40,000 depending on scope. Every project starts with a free intro call in which we assess effort and value realistically.
How quickly will we see results?
A proof of concept delivers solid results with real data in 4–6 weeks. Quick wins from the workshop can often be implemented within the first weeks.
Can we use AI without a public cloud?
Yes. We deploy models on-premises or in your private cloud: with full data sovereignty, encryption, and audit trails.
What happens after the PoC?
You receive a decision brief with architecture, costs, and risks for the rollout. Production build-out typically runs 1.5–3× the PoC budget.
The People Behind It
Sergej Bardin
CEO · AI Strategy & IT Consulting
AI StrategyMCPRAGMulti-Cloud
Martin-Jan Sklorz
CTO · Software Architecture, Cloud & AI Engineering
Software ArchitectureAPI DesignBackend DevelopmentMicroservices
Independent Profiles
Further Reading
AI · 6 min read
Why AI Projects Disappoint: What Comes After the Pilot
Many AI pilots stall before reaching production. Five questions that reveal whether an AI initiative is operationally ready or still in experiment mode.
sergej Read More →
AI · 4 min read
AI Agents in Enterprise: A Practical Guide
How enterprises put AI agents to productive use: from use case evaluation through proof of concept to scaled rollout. A practical guide for enterprise teams.
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AI · 7 min read
RAG in the Enterprise: AI-Powered Knowledge Management
Retrieval Augmented Generation makes internal company knowledge accessible to LLMs. Architectures, chunking strategies, and a hands-on Python example.
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Discuss your project?
Let's find out how we can help in a free initial consultation.