Project & production intelligence
Bring project status, handoffs, production constraints, and emerging delivery risk into a view people can act on.
Project operations / Production / Risk
Our first commercial focus / AEC
AI for AEC without the enterprise AI department.
A practical path into AI for growing architecture, engineering, and construction firms. Start with a real operational problem and the systems your team already uses.
Intelligence for the work of the firm
Where is complexity consuming your capacity?
These are starting points to evaluate with your firm. The right first system depends on your data, workflow, risk, and potential return.
Executive clarity
Connect backlog, revenue visibility, utilization, and capacity to the decisions leadership makes each week.
Backlog / Revenue / Utilization
Proposal & workflow automation
Reduce repetitive research, drafting, email coordination, and follow-up with bounded workflows and supervised agents.
Proposals / Coordination / Agents
Organizational knowledge
Make approved institutional knowledge easier to find and apply, with data boundaries and human review appropriate to the work.
Context / Knowledge / Private AI
An adoption method, not a transformation program
A small start. A measurable difference.
Blueprint
Find the highest-value starting point. Establish the baseline, data requirements, approval boundaries, and a clear measure of success.
Build
Deploy the system, integration, agent, or intelligence workflow in a bounded scope that fits the way your team works.
Refine
Measure cost, adoption, and useful capacity returned. Improve and expand only where the economics justify it.
A foundation in real operations
Built from operating experience.
The internal Sunrise Geo deployment connected reporting, KPI visibility, and operating workflows. Existing project records describe reporting effort moving from approximately 100 to 20 hours per month.
Read the internal deployment case studyMonthly reporting effort
~100 → ~20
HOURS / INTERNAL SUNRISE GEO DEPLOYMENT
An approximate internal reporting result, not an AI-agent benchmark or a promise of future savings.
Your systems. Your judgment. Clear boundaries.
The starting point is the software and information you already have. We assess which connections matter, what an agent may do, and where a person must review. Cloud, local, and private AI choices follow the workload, data sensitivity, and economics; private deployment is a developing direction, not a universal off-the-shelf offering.
Our approach to human controlPut intelligence to work
You don’t need to transform everything at once.
Find one expensive operational problem. Solve it. Measure it. Expand from there.