For CEOs & COOs of PE-Backed Companies
Real AI Transformation, Measured in EBITDA
Your board wants an AI story. Your team is stretched. Vendors are selling copilots that demo well and change nothing. We build AI into the one or two workflows where your company actually bleeds hours and margin — working software in 90 days, with numbers you can put in front of your sponsor.
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You’ve probably lived some version of this
The pilot that went nowhere
A chatbot or copilot pilot launched with fanfare, used for two weeks, quietly abandoned. The problem wasn’t the model — it was that the pilot never touched a workflow with money attached.
Board pressure without a plan
“What’s our AI strategy?” comes up every quarter. What’s wanted isn’t a slide — it’s evidence: costs down, throughput up, error rates cut, attributable to something you shipped.
Your best people do robot work
Estimators, claims handlers, schedulers, quoting teams — skilled staff spending most of their day reading documents, re-keying data, and checking each other’s work.
Every vendor sells the same thing
Generic tools your competitors can buy the same day give you no edge. The AI that changes your company’s trajectory is built on your data, in your workflow — and almost nobody offers to build that at mid-market scale.
Where AI actually moves EBITDA in an operating company
Across industrial and specialized businesses, the pattern repeats. The wins are rarely glamorous — they’re workflows where hours are burned and errors are priced:
Quoting & estimating
AI reads the incoming RFQ, drawing set, or spec; extracts the scope; drafts the quote against your pricing history. Faster bids, more bids, fewer scope misses.
Document-heavy back office
Order entry, invoice matching, claims intake, compliance documentation — AI that reads any format, validates against your systems, and routes only exceptions to humans.
Technical document intelligence
Drawings, specs, inspection reports, maintenance logs — turning unstructured technical documents into structured, decision-ready data. This is the domain where we’ve built our own patent-pending systems.
Scheduling & resource allocation
Crew, fleet, and job scheduling that currently lives in a planner’s head and a spreadsheet — augmented with AI that proposes, explains, and learns from overrides.
We hold ourselves to the same standard
Kamna isn’t an agency reselling AI enthusiasm. We build and operate our own vertical AI ventures — including Aginera, an AI takeoff platform used by construction estimators, where we built RouteNet, our own patent-pending computer-vision model, because off-the-shelf AI failed on the workflow that mattered most. We know from operating experience what a real AI P&L impact looks like, what a doomed pilot looks like, and how to tell them apart early. Read how we built RouteNet →
How an engagement runs
Operations assessment (2–3 weeks)
We sit with the people who run your workflows — not just the org chart — and rank automation candidates by hours burned, error cost, data readiness, and integration reality. You get a ranked roadmap with honest build-vs-buy calls and the business case for the top item.
90-day build on the #1 workflow
Working software, integrated with the ERP/CRM/document systems you already run. Frontier AI models where they’re sufficient; custom capability only where it’s justified. Baseline measured before, impact measured after — in hours, error rates, and margin.
Scale what works
Extend across departments and sites, harden the system, train your team to own it. Where your data supports it, we help convert the capability into durable IP your company — and your sponsor’s exit story — actually owns.
Frequently asked questions
We’re mid-market — is custom AI realistic for a company our size?
Yes, because “custom” doesn’t mean building models from scratch. Most of the value comes from frontier AI models harnessed deeply into your specific workflow and systems. Genuinely proprietary models are reserved for the rare workflow where they’re economically justified — and that discipline is exactly what keeps this affordable at mid-market scale.
How fast do we see results?
The first build cycle is 90 days from assessment sign-off to working software on one workflow, with before/after operating metrics. We deliberately pick a first workflow that can prove value inside one quarter.
Will this disrupt our ERP or existing systems?
No rip-and-replace. We build on top of what you run — SAP, Dynamics, Sage, Epicor, industry-specific systems — with AI handling the reading, matching, and routine decisions around them.
What does our sponsor see?
Operating metrics: hours removed, error rates cut, cycle times shortened, margin recovered — measured against a pre-build baseline. The point is a value-creation story that survives diligence at exit, not a technology showcase.
What happens when the engagement ends?
You own the systems, the data assets, and the documentation. We structure handover so your team can operate and extend what was built — or we stay on in a fractional capacity if you’d rather not build that muscle internally.
Start with the workflow that hurts most
Bring us the process your team complains about — the quoting backlog, the order-entry grind, the document pile. We’ll tell you within weeks whether AI genuinely moves the needle there, and prove it within a quarter.
Book an Operations AI Assessment For your sponsor: the PE view →