We select an AI model for your actual tasks, fine-tune it using approved company data, connect it to your systems, and deploy it inside your own infrastructure.
Start with the best-fit foundation model
Architecture illustration. Model selection and training depend on your business requirements.
A generic model does not know your production rules, equipment names, internal procedures or the language your teams use every day. We adapt the technology to your company instead of forcing your company to adapt to a generic AI tool.
Generic AI
Skill Focus approach
Routine assistance, engineering analysis, computer vision and specialist agents can use different models within one private AI environment.
We handle the work between choosing a model and making it useful inside your business. Each stage has a defined technical and operational outcome.
We meet with process owners, engineers and IT teams to understand what employees and AI agents need to do. We map target workflows, required languages, security rules, expected users and available data.
We test suitable open-weight models against representative company tasks. The choice considers answer quality, reasoning, response speed, hardware use, language support, licensing and the ability to adapt the model.
We review the relevant documents, examples, historical records and connected systems with your team. We clean and structure approved training data, remove inappropriate content and separate training examples from live knowledge sources.
Using the approved dataset, we adapt the selected model to your terminology, instructions, procedures and specialist tasks. Our team chooses the training method and checks whether the adapted model performs better on your real use cases.
We connect the model to ERP, MES, maintenance systems, document repositories and other approved sources. Current facts are retrieved when needed, and answers respect the employee's role and permissions.
Before launch, we validate output quality, source references, access restrictions, latency and system stability. We deploy inference inside your infrastructure, integrate it into approved applications and define monitoring and update procedures.
More parameters do not automatically mean better results. We benchmark candidate models against your tasks and select the best balance of intelligence, speed, reliability and infrastructure cost.
A fast model may be the best choice for employee assistance. More complex engineering tasks may need a stronger reasoning model. We design around both needs.
Our AI engineering team prepares approved examples from your operations and fine-tunes the selected model for your terminology, procedures and specialist workflows. We then compare the adapted model with its baseline before it goes live.
Procedures, industrial vocabulary, expert-reviewed examples and structured training records.
Cleaning, formatting, deduplication, sensitive-data review and train/test separation.
Adjusting model behavior for the chosen company tasks without retraining from scratch.
Checking accuracy, instruction following, reliability and performance on held-out examples.
Running the adapted model with controlled access and connected knowledge sources.
Adapt its language, preferred procedures, task formats and specialist behavior using curated examples approved by your company.
Current orders, inventory, sensor readings and changing procedures come from connected systems, not from old training snapshots.
We use fine-tuning where the approved data and evaluation show clear value. Live operational data remains connected through controlled retrieval and integrations.
Training alone is not enough. We connect your chosen model to the information it needs at the moment a question is asked, with the same access rules your company uses today.
Example of a customer-controlled AI deployment
Data permissions are checked for each user and source. Sensitive documents and business records remain in your company-controlled environment.
On-premises architectureWe design your AI deployment around your security requirements, not around sending your business information to third-party AI services.
Model inference and approved fine-tuning can run inside your infrastructure. External AI APIs are not required for a fully private deployment.
Models and connected sources follow your company's roles and permissions, so employees only receive information they are authorized to access.
Company data is not automatically used to train models. Training runs and updates use approved datasets, reviews and release procedures.
AI models improve quickly. We help your company maintain performance, manage changes and adopt newer models when they are suitable for your workloads.
Monitor
Track speed, reliability and system health across real AI workloads.
Evaluate
Review approved test cases, feedback and errors before adjusting a model.
Update
Refresh connected documents and data sources as operations change.
Upgrade
Evaluate newer models and expand capacity when the business needs it.
We normally start with a suitable existing model, evaluate it against your tasks, then adapt it using approved company data. This is typically more practical than training a large model from zero.
No. We work with approved, reviewed training datasets. Changing operational records such as orders, inventory and production readings stay in connected systems and are retrieved when needed.
Yes. We can design an on-premises setup with local inference, company-controlled data access and no dependency on external AI APIs. Network access and update procedures are set according to your security policy.
Yes. Skill Focus is designed to connect private models to your existing software and data systems, including Factory Copilot and specialized industrial AI agents, subject to available interfaces and permissions.
We can test newer models against your original evaluation criteria and plan controlled upgrades when they offer better results and fit the hardware, licensing and security requirements.
From model selection and company-specific fine-tuning to integrations and local deployment, Skill Focus handles the complete journey.
