Bright Decisions
Lighting asset survey and decision support. Compare retaining, repairing, remanufacturing and replacing an asset side by side, with the evidence behind every figure.
Explore Bright Decisions →Our work
Selected systems and applied work.
Two are products you can explore today. The rest are live services and pilots running inside our own operation — we label each by its real stage so a working prototype is never mistaken for a finished platform.
Products
Try these yourself.
AI-assisted tenant repairs agent
An AI phone agent takes the repair call and logs the job. A staff dashboard reviews every safety flag and repeat fault before a contractor sees it.
See how it works →Applied systems & pilots
Live services and work in progress inside EGG.
Emergency-lighting compliance workflow
- Problem
- Recurring test data, faults, remedial actions and certification needed to become one continuous compliance record.
- System
- Scheduled extraction and reporting, fault identification, remediation logging, annual certification and technical escalation.
- What it demonstrates
- Operational compliance automation, recurring reporting, exception surfacing and evidence continuity.
Compliance evidence engine
- Problem
- Compliance evidence is distributed across storage systems and difficult to monitor consistently.
- System
- The prototype ingests selected SharePoint and local folders and maintains an audit log. The wider design adds classification, evidence links, gap and freshness monitoring, alerts, reporting and access control.
- What it demonstrates
- Evidence-system architecture, a disciplined AI/rules split and auditability as a core requirement.
AI-assisted sales agent
- Problem
- Prospect research and relevant outreach required significant founder time and careful control of sending quality.
- System
- An internal agent identifies prospects, prepares evidence-based drafts and tracks responses. Every message is reviewed and sending volume is deliberately constrained.
- What it demonstrates
- Research agents, evidence-grounded drafting, human approval and rate limits.
How we present a project
Every project write-up follows the same structure: stage tag, challenge, users, information sources, workflow, AI tasks, deterministic controls, human review, current result, limitations and next step. Client names, sites, volumes and screenshots appear only with written permission and redaction review.
Recognise one of these problems in your own operation?
Bring us the process. We will tell you where AI is useful, what should remain rule-based and what a practical first build would look like.
Discuss a workflow
No transformation programme required. Start with one process.