hours/month recovered by one team
faster proposal preparation
average time to first working system
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Sound familiar?
Your Team Is Working Hard.
The Information They
Need Isn’t Keeping Up.
01
Your knowledge is trapped
The answers your team needs are in there somewhere. Past proposals. Old project files. Decisions that worked. But finding them means searching drives, asking the right person, or starting from scratch. Every time.
02
Your people are doing the system’s job
Someone is copying data between tools. Someone else is chasing approvals by email. Another person is re-keying numbers that already exist somewhere else. Your team isn’t slow. They’re wasting time bridging the gaps.
03
Growth means more cost, not more margin
Every new contract, new region, or new client line requires more people to service it. Revenue grows. So does the headcount. The margin doesn’t.
04
Key-person risk is a real number on your risk register
When the person who knows where everything is goes on leave — or leaves the organization — the whole operation slows down. That’s not a people problem. It’s a systems problem.
05
You’ve seen the AI pitches. Nothing has shipped.
The demos looked good. The pilots ran. The consultants presented. And you’re still waiting for something that actually works in your environment, on your data, connected to your systems.
What gets built
Not every data problem looks the same. But across construction, experiential services, financial services, and healthcare, the same three gaps come up — in different combinations, at different scales.
01
Your Institutional Knowledge Is Inaccessible
Decades of proposals, project records, case studies, and client history exist inside your organization. The problem is that it lives in folders, inboxes, and people’s heads — not somewhere the whole team can reach it when it matters.
When the right knowledge is hard to find, teams start from blank pages instead of standing on what already worked. Bids take longer. Onboarding takes longer. The people who know where to look become the busiest people in the building.
What changes
One team went from 30-day proposal cycles to 1–2 days — without adding headcount.
02
Your Systems Don’t Talk to Each Other
Your ERP knows one thing. Your CRM knows another. Your field tools, project management software, and reporting dashboards each hold a piece of the picture — but nobody holds all the pieces at once.
The people who need to make decisions end up waiting for someone to pull a report, consolidate a spreadsheet, or chase down a number. The data exists. It just isn’t connected.
What changes
Real-time operational visibility — without rebuilding the platforms you already depend on.
03
Your Operations Don’t Scale Without Adding People
Every new project, client, or region costs roughly the same to service as the last one. The workflows that work at your current size start creaking under the next level of volume. And hiring ahead of revenue is a bet most businesses can’t afford to make.
AI-powered workflow automation changes the equation — not by replacing your team, but by eliminating the work that shouldn’t require human judgment in the first place.
What changes
More capacity from the team you already have.
Industry verticals
These industries share a common challenge: large amounts of institutional knowledge, operational systems that grew faster than they were designed to, and pressure to do more with what already exists.
What it looks like in practice
Events & Experiential Services
30 Days Down to Two. 320 Hours a Month Back.
The Challenge
Every RFP felt like starting from scratch. The team was talented. The portfolio was strong. Decades of winning proposals, proven case studies, and deep client knowledge existed inside the organization.
The problem was they couldn’t access it.
Past proposals lived in shared drives. Relevant case studies were buried in job folders. The people who knew where to find things had been there for years — and they were already stretched thin. Every new bid meant starting a search, not starting a draft. Proposals took 30 or more days. Quality varied based on who was in the room. And marketing capacity was almost entirely consumed by RFP production instead of growth work.
The fix wasn’t a new process. It was making the existing knowledge retrievable.
Once decades of proposals, project documents, and case studies were indexed and structured, an AI system could surface the right historical work for any incoming RFP — and assemble a first draft that was 80% complete before anyone sat down to write.
The compounding value
Once the data was structured for proposals, it started solving problems nobody had asked about yet — project lifecycle visibility, onboarding, cross-team knowledge sharing. Getting your data in order has downstream value that compounds.
The approach
Start with what’s already there
The knowledge your team needs to move faster almost certainly exists inside your organization already. The starting point isn’t building something new — it’s understanding where the friction lives and what a working system needs to do. The build follows the diagnosis, not the other way around.
No rip-and-replace
Your ERP, your CRM, your shared drives, your legacy platforms — they stay. AI layers on top of the infrastructure you already depend on, connecting the pieces that aren’t talking to each other. No migration. No retraining your team on a new platform. No betting the operation on a switch.
Something working in 90 days
The first milestone is a live system delivering measurable results — not a strategy deck and a phase-two proposal. Start with the highest-value problem. Prove it works. Then scale what’s earning its keep.
Trusted by
Client perspective
Trish Do
Head of People, Trellis Group
John Briggs
Senior Manager Software Development, Greenix
John Doheny
CTO, Augustine Institute
Testimonial
Katherine Cook
Principal Product Marketing Manager, Adobe
Jason Rupp
Sr Director Product Management, SXM
Mayuresh Ektare
VP of Product Management, Zingbox – Palo Alto Networks
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Before the call
Is this relevant if our data is a mess
That’s usually the starting point, not a blocker. Most organizations that need this kind of work have data that’s inconsistent, scattered, or only accessible to the people who’ve been around long enough to find it. The first step is understanding what exists and what’s worth using — not assuming the archive is clean.