Proof · how we run deverus

We did it to ourselves first. deverus runs AI-first.

deverus is a software platform serving 42 client instances. About a dozen coordinated AI agents now run our releases, cloud, support and integrations. A person approves every production change.

See the use cases Talk to us
181production releases in one week, each approved by a person
100+scheduled automations running for deverus customers
17%off the monthly cloud bill, about $72K a year
70%of error noise gone, about 4.9M events a month

By areaWhat we automated at deverus, and the result

Support. Integrations. Operations. We did all three.

01

Support

Agents look things up across all 42 client instances. The connector grew from 4 tools to 24 in about two days, and an urgent order problem was solved in about 11 minutes.

See the result 02

Integrations

A new, AI-built integration engine is shadow testing beside the old one. Nothing switches until the outputs match and a person signs off.

See the status 03

Operations

181 production releases in one week, each approved by a person. 100+ scheduled automations. The cloud bill down about 17%, and about 70% of error noise gone.

See the results

Use casesReal numbers from deverus, every status stated plainly

What we automated, and how it is going.

01 · Workflow AS · built by our teamRunning · 8 months

Teach it once. It runs every day.

You teach Workflow AS a process in chat. It writes a playbook you can read, turns it into a locked automation, runs it live while you watch, then runs it on a schedule. For our customers it runs 100+ automations across about 24 environments. For one firm it handled 5,500+ orders and reviews in 45 days, giving back about 50 hours of QA work a week.

AI doesRuns the scheduled work and logs every action
People ownTeaching, approving the playbook, and every exception
02 · Automated developmentRunning

181 releases in one week. A person approved every one.

AI agents write, test and ship changes through a scripted, reversible release path. The old build server is retired. People review and approve each release before it reaches production.

AI doesWrites the change, tests it, and prepares a reversible release
People ownReview and approval of every production release
03 · Automated cloud and reliabilityRunning

A smaller cloud bill. A much quieter one.

A reliability agent sends a daily health report and routes each issue to its owner with a one-click fix. Cost work took the monthly cloud bill down about 17%, with a human database specialist sharing the credit.

AI doesWatches health and cost, and prepares the fix for each issue
People ownWhich fixes run, and anything that touches data
04 · Automated supportRunning

Support agents that can actually look things up.

We made a legacy app safe for AI. One connector gives support agents read-only lookups across all 42 client instances. It grew from 4 tools to 24 in about two days. An urgent order problem was solved in about 11 minutes.

AI doesLooks up orders, routes and integration health to diagnose a ticket
People ownEvery reply and every change to a customer's account
05 · Automated integrationsShadow testing

Replacing a legacy integration platform, safely.

An AI-built integration engine runs beside the current one, read-only, on the same orders. We compare the results before any client moves. Nothing switches over until the outputs match and a person signs off.

AI doesBuilds the connectors and compares every result
People ownEach client's switch-over, one at a time
06 · Automated new platformEarly access

Verocity AI: daily admin work by chat.

The next deverus platform puts one place to ask in front of the menus. Staff do daily admin work by chat.

AI doesTurns a request into the right steps in the platform
People ownApproval of anything that changes an account
07 · Cyber defenseRunning

Edward started at home, too.

We pointed frontier AI at our own platform first: cloud, code and access, with AI-drafted fixes that a person approved. Now we show other teams how. We do not publish deverus findings.

Cyber defense services

How we engageStart narrow. Prove it. Expand.

One small first step. Every next one, earned.

45minutes · free

Opportunity scan.

One operating area, with the people who run it. We find the work AI should take over first.

You leave withA ranked list of what AI should take first, and what it never should.
Proofone layer of work

Operational proof.

One layer of real work runs inside an approved boundary. Read-only first, measured, and logged.

You leave withResults against your own baseline, and every run on the record.
+1layer at a time

Expand with evidence.

We run what is proven, and add the next layer only when the results earn it.

You getAI running more of the routine work, with your people approving what matters.
Read-only firstInside your boundaryA decision at every step

Want this for your company?

Show us one recurring job. We will tell you where AI should start.

Find your first automation Book a 45-minute scan