The Problem
Your AI program has a roadmap and no working system.
AI adoption fails because business is messy. Data scattered across spreadsheets, information living in employees' minds, and no standardized process for how things should be done.
We clean up the mess & train you on how to maintain it through our AI transformation process.
AI models have been great since 2025. Companies still cannot turn them into operating capability, because AI strategy, builds, and training require transformation, not just a tool.
what’s included
One engagement that produces a working system, not a maturity assessment.
This is not a roadmap you implement later, and it is not a training program bolted onto software somebody else installed. It is an operating engagement. DayNova maps the strategy to how your company actually runs, builds the automations that produce the largest gains, and coaches your team to run and extend them.
MIT reported in 2025 that roughly 95 percent of enterprise generative AI pilots produced no measurable P&L impact, with the failure concentrated in workflow fit and organizational adoption rather than model quality. The models were never the constraint.
Our team got a ton out of the time spent with Travis. He looked at our business strategy, process, resourcing, and planned initiatives through an objective, expert lens. We walked away with countless learnings about the AI landscape, applications to our business, and a clear roadmap for how to move quickly, but intentionally, in the ever-changing space of AI.
— Jess, Sr. Director @ Large Outdoor Retailer
COMPANIES WE'VE WORKED WITH
Microsoft
LEGO
Tracksmith
Ridge
Capital One
Aspen Snowmass
+3
Production-grade AI automations built within the 1st quarter
what is inside an ai transformation engagement
Six workstreams. Each one makes everything else work better.
Every engagement is scoped to your operating model, tool stack, and team structure. The six workstreams below are the standard shape, not a fixed package.
Workstream
What it is
Operating Model Map
A written map of where AI touches your real workflows, where cognitive load runs highest, and where resistance will surface before you spend a dollar building. Built from stakeholder interviews and observed process, not a questionnaire. Names the decisions your team makes under pressure and who owns them. You start the build knowing which three things matter instead of thirty.
AI Capability Diagnostic
A scored baseline of what your team can actually do with AI, run against Dr. Nicole Gravagna’s research on decision-making under pressure. It measures whether people reach for AI, not how confident they say they feel. Confidence moves on its own and proves nothing. This gives you a figure you can move and report to a board.
Automation Build
The working automations, built inside your stack with your data and your approval paths. DayNova builds them directly or coaches your engineers to build them, depending on what you already have. Every build is pressure-tested against the exceptions your ops team has been absorbing for years. What ships is something your team can run on Monday without a vendor on the call.
Playbooks & Decision Rights
The written standards for how AI gets used, who approves what, and where a human stays in the loop. Covers prompt architecture, output quality benchmarks, and escalation paths. Tool-agnostic by design, so changing platforms does not reset your capability. This is what makes the second automation cheaper to build than the first.
Embedded Coaching
Training built around the live system instead of a generic curriculum. Your team practices running the automations with DayNova alongside them, then keeps extending them through ongoing coaching. Sessions are paced around how habits form, not around a content calendar. Adoption holds past 90 days because the new behavior became the familiar one.
Measurement & Reporting
The quarterly read on capability, adoption, and time recovered, tied back to the diagnostic baseline. Reported in language your board already uses. Names what is working, what stalled, and what the next build should be. You stop debating whether AI is paying off and start deciding where it goes next.
engagement structure
An AI transformation engagement built in 12 weeks. Refined through partnership.
Built for leaders who want to become ai-native
Right fit. Wrong fit.
Best fit if you’re:
Not the right fit if you want:
Running a company above $10M in revenue that has already tried AI and hit a wall.
A slide deck you can present next month with nothing running behind it.
Carrying an AI mandate from the board with no working system to point at.
A dev shop that builds the automations and hands your team a login.
Holding a roadmap from a strategy firm that nobody has been able to build.
A one-day workshop that inspires the room and changes nothing by quarter end.
Willing to give your team real time inside the build, because that is where capability forms.
A platform license sold to you as a transformation strategy.
PROOF
Built by operators, backed by neuroscience.
Founder & CEO
Travis Tallent
Travis Tallent spent 15 years operating inside the organizations most companies benchmark against, including Microsoft, LEGO, adidas, Capital One, and Aspen Snowmass. He ran the operations AI is supposed to change.
Founding Advisor
Dr. Nicole Gravagna, PhD
Dr. Nicole Gravagna, PhD, neuroscientist, data engineer, and product manager. She's built 6 products from scratch and knows the technical know-how to build custom AI solutions for you.
Technical Team
We are supported by a technical team of forward-deployed engineers, specializing in software development, AI/ML engineers, and data engineers.
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