Predictions, Company-Specific LLMs & Machine Learning
Governed, secure AI systems that work with your info while maintaining audit fit and auditability.
Company-Specific AI
- Your knowledge accessible — Docs, policies, steps become searchable through tight retrieval.
- Your data stays governed — Labeling, keep, and access control maintained.
- Outputs are audit-fit — All AI interactions logged and traceable.
- Matched with risk posture — Controls match your team's risk tolerance.
The split between a stock public AI and a firm-grade system is not just about input privacy. It is about fit and trust. A stock model gives broad answers. A firm-grade system gives answers tied to your real steps, vendor base, asset setups, and ops past. For rule-bound teams, that fit also means outputs can be traced to a known, audit-fit source. The source takes the place of a black-box training set.
Predictive Systems for Energy Infrastructure
We design prediction systems that reduce unplanned downtime. They forecast failures and find early risk signals. This lets ahead-of-time upkeep and smarter spare-part planning.
Typical Inputs
- Asset telemetry / sensor signals (SCADA, tracking, logs)
- Weather and eco signals.
- Upkeep history + work orders.
- Quality and service KPIs (failures, MTTR, response times)
Typical Outputs
- Failure probability and time-to-failure estimates.
- Upkeep recommendations and prioritized work orders.
- Outage risk predictions and "where to look first" guidance.
- Explainable dashboards for ops, engineering, and control.
Project Deliverables
- Data readiness assessment (quality, completeness, safety)
- Model starting point + review plan (accuracy, false positives, biz impact)
- MLOps plan: tracking, drift finding, retraining cadence.
- Ops link-up: alerts, dashboards, ticket auto-work.
Business Impact
- Reduced unplanned downtime.
- Optimized spare-part planning.
- Ahead-of-time upkeep scheduling.
- Proof-based resource allocation.
Company-Internal LLMs + Procurement Predictions
A tight in-house assistant that answers "how do we do X?" from policies and docs. It can also support procurement with proof-based insights (demand signals, supplier risk, purchase planning).
Two-Layer Architecture
- Knowledge Layer (RAG) — for unstructured docs (steps, PDFs, contracts, e-mails)
- Prediction Layer (ML) — for forecasting (demand, lead times, risk signals) where set data exists.
Knowledge Layer Deliverables
- Data labeling 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 signals.
- Forecasting model plan (features, review, ops KPIs)
- Link-up with procurement workflows.
What We've Built
Predictive Maintenance for Solar Parks
ML-based predictive upkeep system developed in a research cooperation (2020). Pattern finding and failure forecasting for asset ops.
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-matched data handling with purpose limitation. Only needed data used for model training and inference.
Model & Prompt Risk Controls
Risk assessment for model selection, prompt engineering guardrails, and output checks.
Logging & Monitoring
Full logging of all AI interactions, speed tracking, and anomaly finding.
Clear Responsibility
RACI matrix for AI ops, incident response steps, and escalation paths.
When this is the right fit: Firm-grade LLMs and forecast ML fit when a team has built up set ops data. Sensor logs. Upkeep history. Buying records. Deal logs. The goal is to move from look-back reports to ahead-of-time data-driven calls. They also fit when teams spend big time searching in-house docs, steps, or rules. A tight in-house knowledge bot cuts that load. Data rules stay in force.
What this doesn't replace: Forecast ML and in-house LLMs do not replace the team's source data quality work. Nor master data control. Nor ops steps. A model is only as good as the data it learns from. Poor, partial, or odd past data will cap forecast skill, no matter how slick the model. So a data-readiness check is a must-have, not an option. This work covers model design, build-out, and rules. Day-to-day MLOps, model hosting, and live support are split ops duties.
Interested in predictive AI or custom LLMs?
Book a call to explore how AI can work with your specific data and needs.