The repetitive work your team grinds through daily, reading, re-keying, chasing, handed to an AI system built into how they already work.
Start free: tell us where your operation loses hours, the work that recurs every day, and we send back the exact AI system we would build for it first, scored in hours saved and the share of the busywork automated. The plan is yours to keep, with or without a call.
Prepared for: a staffing firm
Soon, running an operation without AI will read the way running one without the internet reads today. The work is the same. What carries it changes.
Reads every inbound resume against the open order the moment it lands, then hands the desk a ranked shortlist, key fields already dropped into the ATS.
Reads inbound purchase orders out of email and PDF, checks each line against the catalog and price list, and drops clean orders straight into the ERP.
Turns a short brief and your history of won work into a first-draft proposal in your own format and voice, so the team edits instead of starting from blank.
Matches every incoming invoice to its purchase order and goods receipt, clears the ones that agree to the cent, and surfaces only the real exceptions to judge.
Reads new-patient forms, referrals and records as they arrive, pulls the details into the right fields, and flags what is missing before the visit.
Reads incoming RFQs, drawings and spec sheets, pulls the dimensions and tolerances that drive price, and hands estimators a first-pass quote to check instead of build.
Matches each inbound delivery against its advance ship notice and PO the moment it lands, clears the ones that agree, and flags short, over or damaged counts for a person.
Tracks every active shipment across carriers, drafts the status updates customers keep emailing to ask for, and escalates only the exceptions that are genuinely late.
These are real, running systems companies pay for every month, alongside the automation engine that runs our own business. The proof is not a demo. It is that people renew.
A working AI system with more than 1,000 users, and thirty-plus businesses paying every month to keep using it. It does the marketing and customer-facing busywork a small team would otherwise grind through by hand. The proof here is not a demo. It is that people renew, and the invoices clear every month.
Under the hood · Each customer gets their own private workspace, the AI answers from that customer's own content rather than guessing, and we run the whole thing on our own infrastructure.
An AI system that sits in for the slow, expensive part of technical hiring: running a structured interview and scoring the answers consistently. The point for you is not the hiring use case. It is that we can take a judgment-heavy human workflow, hand the repetitive part to AI, keep a person in control of the call, and get it in front of real users who keep showing up.
Under the hood · Twenty-two live interview scenarios, each scored two ways: a fixed pass-fail check on the things that are either right or wrong, plus an AI read on the judgment calls, with a hard ceiling on the compute each run can use.
If an email from us brought you to this page, it came out of Ksenda. We did not just build it, we lived on it. It finds companies worth talking to and reaches them one at a time, at a scale a person could never do by hand. We pointed it at our own business and it is how we filled the paying side of BrandVox.
Under the hood · One screen, one click to send, with the AI drafting each message and a required human review step before anything goes out the door.
Done by hand, cold outbound is roughly a full-time job for a junior hire, and most of that week goes to copy-paste and list-building, not thinking. The usual payoff is one to five replies per hundred emails. We built Ksenda to carry that load, ran our own outbound on it, and counted the replies by hand. About fifteen out of every hundred people replied. We removed the manual hours and lifted the response rate several times over. That is the strongest proof we can offer: the result we are selling you is the result it produced for us.
A few lines: where the hours go, the work your team repeats every day, where you want more efficiency. No long forms, no access to anything.
We find the one workflow where AI pays off first and write it up, scored in the hours it saves and the percent of the work automated, on numbers you already track. Free.
We read the plan together and you tell us where we read your operation wrong. You keep the written plan either way.
If the number is worth it, we build the AI system into your real workflow and hand it over. Only then, and only if you want it.
Through step three you have spent one link and thirty minutes. Nothing else.
Before we build anything, you get this: the one workflow we would automate first, the AI system we would build for it, and what it saves, in hours and the share of the work it takes off the desk. Here is a sample. It is built on public benchmarks, not a real client, so the numbers are held to the low end.
SAMPLE PLAN · Illustrative example, not a real client · Figures from public 2025-2026 staffing benchmarks
A 90-person regional light-industrial staffing firm. 6 recruiters on the desk, no AI in use today.
recruiter-hrs / wk
given back across the 6-person desk
less screening time
per recruiter, ~12 hrs down to ~4
of lost orders recovered
first submittals that beat a slower rival
Every weekday, inbound resumes land in a shared inbox and the job-board queue. Each recruiter opens them one by one, reads each resume, decides if the person clears the basics for the open order, then re-types the useful details into the ATS by hand before anyone can be submitted. The first agency to put a qualified person in front of the client usually wins the placement, so the hours lost reading and re-keying are not just admin. They are placements that walk to a faster competitor while the resume sits unread.
First-Submittal Shortlister
A quiet helper that watches the same inbox the team already uses. The moment resumes arrive, it reads each against the open order's real requirements and hands the recruiter a ranked shortlist worth a call, each with a one-line reason and the key fields already dropped into the ATS draft. The recruiter still makes every call. The AI system just removes the reading and re-typing.
Screening drops from about 12 hours a recruiter each week to roughly 4, so close to two thirds of the reading and re-keying disappears. Across the six-person desk that is about 48 recruiter-hours back every week, and the faster first submittals win back roughly half of the placements that were slipping to a quicker rival. The hours come back the first week the system is live.
These figures are illustrative, not a real client's books, and held to the conservative end on purpose. Built from public 2025-2026 staffing benchmarks: recruiters spend ~52% of the week on admin; 8 to 10 hours to hand-screen ~200 resumes; teams report 10 to 15 hours per week saved per recruiter from screening automation, and this sample claims only ~8. Your real numbers replace every figure here on the call.
We build on the standard infrastructure your engineers already trust, set up in your own accounts, and we connect to the business systems your team already runs, so the AI lives where the work already happens.
The work does not care what you make or sell. It cares whether your team repeats a task every day that eats hours: chasing the same information, re-keying the same data, drafting the same replies, catching the same errors after the fact. That pattern is in every operation below.
Screening, submittals, candidate matching.
Inbound, inventory, exception handling.
Scheduling, documents, status chasing.
Quoting, specs, quality notes.
Intake, records, back-office work.
Proposals, reporting, client comms.
Fair question, and you are right to be tired of it. We are not going to pitch you a future. We pick one workflow you already run, where the work is repetitive and slow, and we build the thing that does it. You see it running on your data before you decide anything. The free build plan names that one workflow and what it would save, in hours and the share of the work automated, not in adjectives. If the number is not worth your time, you say so and we are done.
Tell us where your operation loses hours. We read how it runs, write the plan, and bring it to a 30-minute call, peer to peer, no slides. If you would rather just read it first, email us and we will send it over, no call needed.
hakan@rehoz.com · Istanbul, TR
Rehoz is an AI engineering firm founded by Ege Hakan Karaagac.
He spent two years at Amazon building the systems that ran checkout and tax for millions of orders, then backend at Dream Games, before that Accenture. We build AI products of our own: BrandVox, with more than 1,000 users and 30-plus paying every month, and Kodwai, an AI interview platform more than 50 developers use. We built Ksenda, the outbound system that brought you here, and ran it on our own pipeline: more than 5,000 emails, about 15 percent reply against an industry norm of 1 to 5 percent. We do one thing for clients: we find the first place AI earns its keep in your operation, and we build it.
