Practical AI. Grounded in real work.
Companies are ready to move beyond AI curiosity. What they want is clarity on where it belongs, help bringing it into day-to-day work, and the option to build something custom when the fit matters.
POLR AI works where business need, workflow, and execution meet, so AI becomes something useful, adopted, and shaped around how the work really gets done.
Two ways we put AI to work.
We help teams decide where AI belongs, then make it useful inside the work they already do.
Consulting and implementation are how the sprint gets delivered: consulting to prioritize and train, implementation to build.
Identify where AI belongs. Prioritize what matters.
Clear direction on where AI belongs, grounded in how the work actually flows, paired with a practical plan for moving forward.
- AI readiness & workflow assessments
- Use case identification & prioritization
- Leadership advisory
- Roadmap development
- Governance & team enablement
- Change management & adoption planning
Turn the plan into something real.
Careful rollout, workflow integration, and purpose-built tools, all shaped around how your team actually works.
- Workflow automation
- AI implementation & rollout
- Internal assistants & tools
- Dashboards, portals, knowledge systems
- Custom solution development
- Optimization & ongoing support
Consulting helps you decide what matters. Implementation helps you make it work.
The smallest team can create the largest impact.
Owners and leaders are the smallest, most nimble team in the business, but their decisions shape how everyone works. When they build confidence with AI, align on the right priorities, and act decisively, adoption moves faster and value compounds across the organization.
The 90-Day AI Sprint
Our work leads with one engagement. We sit down with your leadership team, rank where AI can create the most impact against your strategic goals, and choose the first workflow together. Ninety days later an agent is live in the tools you already own, your team is trained to run it, and the result is measured against a baseline captured before we built anything.
Prioritize
Leadership working session, discovery interviews, and a measured baseline of the chosen workflow.
Build
Collaborative build on a weekly cadence, inside your existing tools and permissions.
Prove
Test, roll out, train, and measure against the baseline. Results reviewed with leadership.
At day 90 you have a ranked AI roadmap, one agent in production, a trained workflow owner on your team, and before-and-after numbers.
The cycle repeats each quarter with the next workflow in your ranked backlog, so capability compounds instead of stalling after a pilot.
What this looks like in practice.
AI makes the biggest difference when it shows up inside the work that already matters. These are the patterns we see most often across the teams we work with.
Client paper, bid-day scope, and what actually gets bought out rarely stay one story. Terms drift from your standards, people chase versions across PM tools and Word, and nobody reaches for comparable jobs until a trade scope looks thin or the yard won't tie, then it's late nights, not process.
Support that holds a single thread: redlines checked to the playbook, scope stress-tested against similar work before buyout, trade packages drafted from the leveled abstract, and yard or ERP rolled up to exceptions instead of spreadsheet archaeology, teams in this pattern often reclaim on the order of 38h per PM each month on assembly, chasing, and the emergencies that never have to start.
Illustrative scenario; composited from typical client workflows.
Best fit for teams with real workflow complexity.
We work best with companies that have coordination challenges, workflow friction, and a real need to make AI useful in day-to-day execution.
AEC is our deepest experience, but the broader fit is teams with real workflow complexity and real implementation needs.
Implementation-focused. Grounded in real work.
POLR AI is led by Brennan Gerle and focused on helping companies move beyond AI experimentation into practical adoption.
The work is built for environments where workflows are messy, teams are busy, and off-the-shelf tools do not fully solve the problem. That means helping clients identify the right opportunities, implement carefully, and build custom solutions when needed.
Clear thinking, practical execution, and solutions built around how the business actually works.
Questions worth answering first.
Ready to make AI useful where work actually happens?
If your team is thinking about where AI fits, how to implement it, or what to build, we'd love to hear from you.