01How fast do we see something working?
2 weeks
from kickoff to a working proof of concept, not a slide about one
Case study · Healthcare
A two-week discovery found $1 million a year in savings.
A Brazilian healthcare company, a real operation, measured outcomes and a first automation already running.
The question
What can the new generation of AI actually do for us? A Brazilian healthcare company asked, and gave us two weeks to answer.
What we did
We mapped how the business actually runs, from the first document a client sends to the invoice that goes back, put a number on every AI opportunity we found, and shipped one automation instead of a slide about one.
What we found
The portfolio came to about $1 million a year, most of it in documents the team still writes by hand, thousands of times a year. The first automation drafts one of those documents from the client's own history. On its own it is worth around $14 thousand a year and about an hour a day per person.
Results
The three questions owners ask first, and what this client got.
02Is it worth the money?
$1M/yr
of opportunity found and priced, most of it in documents still made by hand
03Does it actually reach day-to-day work?
$14k/yr
from the first automation, now live and saving each person about an hour a day
How it went
Two weeks, three moves.
01 · Week one
Identify and quantify
Interviews with frontline teams and leadership, a value tree that split revenue levers from cost levers, and a dollar figure on every opportunity we found. The week closed with a checkpoint with the sponsor to confirm which one to build.
02 · Week two
Build and test
We took the highest-impact task and built it against the client's real files: several thousand historical documents, with the technical team's own rules encoded and every exclusion reported. A working proof of concept, not a prototype sketch.
03 · The close
Measure and prove
Output measured against what the team had actually signed, value confirmed, and a portfolio of opportunities prioritized by impact and feasibility, with a recommended first build and what it takes to run it in production.
Other sectors
We work with companies that see what AI can do and want it running in their day-to-day work.
We are a fit if you want to
- Speed up the manual processes that still eat the team's day
- Turn documents, email and spreadsheets into work that runs on its own
- Know which AI opportunities pay back before you invest
- Build the capability to keep going without depending on us
What it could look like in your sector
Healthcare
Clinical and administrative documents still written by hand, scheduling spread across channels, reports stitched together from several systems. We start where the hours go and put AI inside the systems the team works in.
Where we would start
- Reports and clinical documents drafted automatically
- Scheduling and patient contact on WhatsApp
- Claim denial review with health plans
Insurance
Claims and underwriting run on documents, email and judgment calls that repeat every day. We help the team read, sort and route them faster, and the decision stays with the people who own it.
Where we would start
- Claims intake and document reading
- A first analysis to support underwriting
- Broker and customer service on WhatsApp
Financial services
Onboarding, credit and reconciliation mix documents, spreadsheets and rules. We automate the reading and the checking, with the access control and audit trail a regulated area needs.
Where we would start
- Onboarding and KYC documents
- Credit analysis with a drafted memo
- Reconciliation and the monthly close
Private equity portfolios
A fund wants the same lift across several companies. We run a discovery in each one, price the opportunities and turn what works in one company into a pattern the others can adopt.
Where we would start
- A priced AI portfolio for each company
- One playbook shared across the portfolio
- Board reporting on what shipped
Retail and distribution
Orders, prices and customer questions arrive through many channels and end up in spreadsheets. We connect those channels and let AI take the repetitive part off the team.
Where we would start
- Order intake from WhatsApp and email
- Pricing and assortment analysis
- Demand and stock forecasting
Industry and logistics
Maintenance, quality and freight run on reports, forms and phone calls. We turn that into data the team can act on, inside the ERP it runs on.
Where we would start
- Maintenance and quality reports
- Freight quotes and tracking updates
- Purchasing and supplier follow-up
Contact
Tell us where the hours go. We'll come back with a first step.
The first conversation is about your business, not about AI.