Nextcore · Proof · deverus

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

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

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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

Use casesReal numbers from deverus · every status stated plainly

What we automated,
and how it's going.

01 · Workflow AS · built by our team for deverusRunning · 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, and then runs it on a schedule. For deverus customers it runs 100+ automations across ~24 environments, and for one firm it handled 5,500+ orders and reviews in 45 days, giving back about 50 hours of QA work a week. Watch it full screen

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 was 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 & 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. Results are compared 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 platformBuilding now

deverus 3.0: daily admin work by chat and voice.

The next deverus platform puts one place to ask in front of the menus. Staff do daily admin work by chat or voice, with the classic screens kept as a fallback while it's proven.

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.

A full review of cloud, code, and access, with AI-built fixes that a second AI verifies and a person approves. We don't publish deverus findings.

Meet Edward →

How we engageStart narrow. Prove it. Expand.

One small first step.
Every next one, earned.

60minutes

01 · 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.

30days

02 · Proof

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

03 · Ongoing

Expand with evidence.

We run what's 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