
Ask Your Docs
A Labs demo of the Knowledge Agent pattern: upload a document set, ask questions, and get answers cited back to the exact source passage.
- Next.js
- TypeScript
- Python
- PostgreSQL
The audit fee comes off your buildHow it works
AI agent studio
Evoxatech maps the process you run by hand, then builds the agent system that runs it — with a human approving every action, deployed on infrastructure you control.
Built by the engineers behind Optivizio and Kreova

100%
human-approved at launch
Every agent proposes. A person approves. Autonomy is a dial you turn up when the numbers earn it.
What we do
Evoxatech is a engineering studio that builds AI agents for small and mid-sized businesses. We start by watching how the work is done by hand, then build the system that does it — with a human in the loop and deployed on infrastructure you control.
The reason most companies stall on AI is not cost, it is trust. Every agent we build proposes an action and a person approves it. You start at 100% review and turn autonomy up when your own numbers justify it.
Your cloud, your VPS, or a server we manage for you. Open-weight models on vLLM when privacy, data residency or token volume demands it. Your data never has to leave your perimeter.
Eight years of full-stack work and two AI products we built and still operate. You get auth, audit logs, monitoring, rollback and an interface a non-technical operator will actually use.
We do not sell AI transformation. We sit with the person doing the job, document exactly what they do including the edge cases, and automate that. It is unglamorous, and it is why our builds work where generic tools fail.
The offers
The arithmetic
15 hrs
per week
The time a single person loses to one mechanical workflow — reading, deciding, retyping.
$21,000
per year
What that costs you in loaded salary, spread invisibly across the payroll so nobody sees it.
11 months
to payback
On a $15,000 build removing 80% of it. After that, it keeps paying every year.
Selected work

A Labs demo of the Knowledge Agent pattern: upload a document set, ask questions, and get answers cited back to the exact source passage.

A Labs demo of the Workflow Agent pattern: messy customer emails in, structured data out, and a human approval queue in the middle that you can drive yourself.

A Labs demo you can talk to. It answers questions about Evoxatech and books a discovery call straight into the calendar.
How we work
“A person spends fifteen hours a week reading things, deciding, and typing the result somewhere else.”
80%
of the manual workload removed on a typical build
5 days · $2,000
We map the work, put a number on what it costs, and build a working proof-of-concept on your real data.
3–8 weeks · fixed price
Fixed scope, fixed price, no hourly surprises. You see the approval queue and the audit trail before anything goes live.
from $3,000/month
Someone owns it after launch. Monitoring, tuning as your data drifts, cost optimisation, and a monthly report you can read.
Objections
From the blog
August 2026
Most AI automation projects fail on trust, not capability. The interface where a human reviews what the agent proposes is the part that decides whether anyone uses it.
Read moreJuly 2026
There is a token volume above which running your own model on vLLM is cheaper than an API, and a compliance threshold below which it is the only option. Here is how to work out both.
Read moreTell us what work is eating your team’s time. If it is a fit, we will map it, cost it, and show you a working prototype on your own data — and the audit fee comes off the build.