Predictions, Company-Specific LLMs & Machine Learning
Governed, secure AI systems that work with your data while maintaining compliance and auditability.
Company-Specific AI
- Your knowledge accessible — Documents, policies, procedures become searchable through controlled retrieval
- Your data stays governed — Classification, retention, and access control maintained
- Outputs are auditable — All AI interactions logged and traceable
- Aligned with risk posture — Controls match your organization's risk tolerance
The distinction between a generic public AI and a company-specific system is not just about data privacy — it is about relevance and reliability. A generic model answers general questions; a company-specific system answers questions grounded in your actual procedures, your supplier base, your asset configurations, and your operational history. For regulated organizations, that specificity also means outputs can be traced back to a defined, auditable knowledge source rather than a black-box training corpus.
Predictive Systems for Energy Infrastructure
We design prediction systems that reduce unplanned downtime by forecasting failures and identifying early risk signals — enabling proactive maintenance and smarter spare-part planning.
Typical Inputs
- Asset telemetry / sensor data (SCADA, monitoring, logs)
- Weather and environmental signals
- Maintenance history + work orders
- Quality and service KPIs (failures, MTTR, response times)
Typical Outputs
- Failure probability and time-to-failure estimates
- Maintenance recommendations and prioritized work orders
- Outage risk predictions and "where to look first" guidance
- Explainable dashboards for operations, engineering, and management
Project Deliverables
- Data readiness assessment (quality, completeness, security)
- Model baseline + evaluation plan (accuracy, false positives, business impact)
- MLOps plan: monitoring, drift detection, retraining cadence
- Operational integration: alerts, dashboards, ticket automation
Business Impact
- Reduced unplanned downtime
- Optimized spare-part planning
- Proactive maintenance scheduling
- Data-driven resource allocation
Company-Internal LLMs + Procurement Predictions
A controlled internal assistant that answers "how do we do X?" from policies and documentation, and can support procurement with data-driven insights (demand signals, supplier risk, purchase planning).
Two-Layer Architecture
- Knowledge Layer (RAG) — for unstructured documents (procedures, PDFs, contracts, e-mails)
- Prediction Layer (ML) — for forecasting (demand, lead times, risk signals) where structured data exists
Knowledge Layer Deliverables
- Data classification and access model (who can see what, by default)
- Knowledge ingestion pipeline (ownership, update cadence, audit trail)
- Prompt + output standards (format, citations, escalation rules)
Prediction Layer Deliverables
- Feature engineering from historical data
- Forecasting model plan (features, evaluation, operational KPIs)
- Integration with procurement workflows
What We've Built
Predictive Maintenance for Solar Parks
ML-based predictive maintenance system developed in a research cooperation (2020). Pattern detection and failure forecasting for asset operations.
AI-Supported Decision Models
Decision support systems for engineering decision-making with explainable AI outputs and audit trails.
Governance-First ML/LLM Approach
Data Minimization
GDPR-aligned data handling with purpose limitation. Only necessary data used for model training and inference.
Model & Prompt Risk Controls
Risk assessment for model selection, prompt engineering guardrails, and output validation.
Logging & Monitoring
Comprehensive logging of all AI interactions, performance monitoring, and anomaly detection.
Clear Responsibility
RACI matrix for AI operations, incident response procedures, and escalation paths.
When this is the right fit: Company-specific LLMs and predictive ML systems are the right fit when an organization has accumulated structured operational data — sensor telemetry, maintenance histories, procurement records, or transaction logs — and wants to move from reactive reporting to proactive, data-driven decision support. They are also appropriate when teams spend significant time searching internal documentation, procedures, or policies, and a controlled internal knowledge assistant would reduce that overhead while maintaining data governance.
What this doesn't replace: Predictive ML and company-internal LLM systems do not replace the organization's source data quality practices, master data management, or operational processes. A model is only as reliable as the data it is trained on — poor-quality, incomplete, or inconsistent historical data will limit predictive accuracy regardless of model sophistication. Data readiness assessment is therefore a prerequisite, not an optional step. Additionally, this work focuses on model design, architecture, and governance; ongoing MLOps, model hosting, and production support are separate operational responsibilities.
Interested in predictive AI or custom LLMs?
Book a consultation to explore how AI can work with your specific data and requirements.