Briefings arrive prepared
Overnight advisories, expiring certificates, access reviews, and cross-team questions land summarized, with the items that need a decision marked.
Use cases
The AI agents we run every day.
These are not slide decks or demos. They are working systems we built and run ourselves, with the same disciplines we bring to client work. Agents prepare. People decide.
Interface images on this page are illustrative recreations with representative values, not client screenshots and not personal data.
Start from the result
Built · Operated · Documented
ATX · 30.27°N · 97.74°W
Use case 01 · The agent desk
An agent desk for a real executive team.
Keyfive is a digital-twin software company for energy and industrial operators. Behind its leadership runs the agent desk ATX DevOps engineered: finance, revenue, and technology agents that cooperate on the same question and hand one decision memo to the people in charge. The desk works unattended and on schedule. It drafts the go-to-market analysis, reconciles the financial reviews, summarizes the security advisories, and prepares actions that wait for a named sign-off. Not a replacement for the executive team. Superpowers for it.
Decisions stay human
Approve and hold are buttons for a person. Nothing ships, spends, or touches a customer without a named sign-off.
Agents cooperate
The revenue agent flags a security questionnaire, the technology agent drafts the answers from the controls inventory. One desk, one memo.
Everything leaves a trail
Held actions are logged, approvals are recorded, and the audit evidence attaches itself.
Read how the desk is governed in the proof section. More from the same fleet: the health second brain, the discovery engine, and the rest. Or bring us the first workflow: Map your leverage
Use case 02 · Built for ourselves
One coach that reads sleep, training, meals, and labs together.
Your data already exists. It just lives in places that never talk to each other. A ring scores your sleep, a training app logs your sets, a lab portal holds a PDF from March. We built a personal health second brain that reads all of it together, and we tested it on the most demanding user we know: our own founder. It has synced his real data nightly, hands off, since June 2026. If a system can turn a ring, a workout log, a meal log, and a lab history into one honest coaching loop, the same pattern can turn your ERP, CRM, ticket queue, and telemetry into one honest operating picture.
Nineteen metrics, one vocabulary
A smart ring, phone health apps, workout logs, and lab PDFs, all pulled into one structured record. Everything from sleep stages to blood pressure to supplements sits in one place, ready to compare.
Coaching against your baselines
It compares your nutrition to your own targets, weighs your training load against the same week’s recovery, and gives you three to five specific adjustments instead of a lecture.
Safety before advice
A safety check runs first on every clinical question, each finding carries a severity level, and anything that needs a clinician’s judgment is flagged for one rather than answered by the system.
Statistics that refuse to flatter
It will not draw conclusions from too little data, it calls a correlation a correlation rather than proof, and it treats ‘we don’t have enough data yet’ as a real answer. The analytics and dashboards run on our own machines.
Six systems that never talk to each other is also a business problem. Map your leverage
Use case 03 · Go-to-market discovery
Watch the whole government market. Pursue only where you fit.
The federal government publishes its demand in the open: thousands of notices a week across official sources. For a data and software services company weighing that market, the stream is both the opportunity and the trap, because one wrong pursuit burns months of senior hours. So we built a discovery engine that reads the stream so people don’t have to. This one is ours too, built for a company in our own orbit rather than a client. We test on ourselves first.
Evidence first, always
Notices from official sources like Grants.gov, SAM.gov, and SBIR.gov are normalized into one record, and every judgment traces back to the government’s own text.
Hard gates before any score
Open deadline, eligibility, real scope match. An unresolved unknown blocks pursuit. The system does not guess.
Fit, scored honestly
Each opportunity is scored out of 100, and every part of the score points back to the notice it came from. We score four ways to take part: as prime, research partner, technology provider, or subcontractor. Win probability comes as a range for the current stage, never a made-up precise number.
No-go, on the record
Weak fits are archived with the reason. The engine recommends no-go more often than go, because senior hours are the scarcest resource in a small company. It ran end to end against live notices in a validated first run, and it is deliberately gated before running as a recurring service. No wins are claimed, and that order of operations is the point.
Any high-volume inbound stream works this way: bids, leads, claims, applications. Map your leverage
Also in daily operation
The rest of the fleet.
Standards as code
An independent governance service checks every AI project in the portfolio against shared standards, then turns any gap it finds into tracked work.
A four-hat business advisor
CFO, CIO, and CRO perspectives convened over one question, synthesized into a single decision memo. The consultancy runs on it.
Governed hands for legacy software
When the software has no API, a broker drives the interface under the same rules as everything else: audited, bounded, reversible.
Engineering delivery analytics
Review latency and flow measured from the repositories a team already uses, so delivery debates start from evidence.
Evidence reconciler
Two systems that should agree usually do not. An agent compares the records on a schedule and files every discrepancy with receipts.
Adversarial review harness
Before a finding ships, independent agents argue against it. Only what survives the argument gets acted on.
What this translates to
Representative engagement patterns.
What the same disciplines look like applied to client work. These are anonymized patterns, not claims about a specific client.
Decision-ready intake
Collect requests from multiple channels, gather context, identify what’s missing, and route a decision-ready package to the right person, with the source and status of every item visible.
Issue to tested change
Turn an approved engineering issue into a bounded implementation workflow that can inspect the codebase, propose a change, run tests, and prepare it for human review and existing CI gates.
Evidence and reconciliation
Collect evidence from authorized systems, compare records, surface discrepancies, and prepare a traceable recommendation for review.
Your workflow next
Every one of these started as one stuck problem.
None of them came off a shelf, and a competitor could not copy one by buying the same tools, because each is shaped around one operation’s workflows, data, and sign-offs. Yours would be too. The Leverage Map finds your starting point and puts the answer in writing: what is worth building, what it takes, and whether AI is the right move at all. A free 30-minute fit call starts it.
Prefer email? Write to jay@atxdevops.com.