Everyone sells the build.

Nobody owns the run.

Agencies build prototypes and celebrate the launch. But once AI goes live, the risk shifts: models drift after unannounced supplier updates, answers quietly degrade, and high-paid staff end up babysitting the system.

We bring eight years of ex-Google Site Reliability Engineering (SRE) to your production AI – installing the automated testing, early-warning guardrails, and instant failovers required to protect your customer trust and operating margins.

Book an AI Fire Drill Explore The Run Method
Datastone — Outage cost
A one-hour undetected outage costs approximately $840,000 at an industry-average rate of $14,000 per minute. Against a monthly service level, that single hour takes availability from 100% to 99.86% — breaching both a 99.95% and a 99.90% commitment.

I engineer the operational discipline that keeps production AI resilient, compliant, and commercially viable.

Of outages start with a change

Google SRE, Site Reliability Engineering

Frontier AI providers suffered cascading outages in a single morning

3 September 2026 Industry Incident Analysis

Find AI failures through customer complaints

Monte Carlo, State of Data & AI Reliability 2026

Average downtime cost

Splunk, The Hidden Costs of Downtime 2026

— OPERATIONAL REALITY

The three failure modes draining your AI investment

“Your AI isn’t failing because the technology is flawed. It’s failing because nobody owns the run.”

1. The Supplier Trap (The SLA Mismatch)

You promise guaranteed reliability to your customers, but your AI relies on external providers operating on best-effort terms. When your AI supplier slows down, hits rate limits, or crashes, your core business workflows—quotes, claims triage, customer support—halt immediately. You carry the customer fallout and contractual penalties, with zero recourse from your vendor.

2. Silent Logic Drift (The Hidden Margin Leak)

Traditional software crashes loudly with an error code. AI fails in silence. When an AI supplier updates their model overnight, no IT alarms go off. Instead, the AI subtly changes how it thinks: offering unapproved discounts, loosening credit criteria, or misinterpreting policy terms—all while your standard IT dashboards stay 100% green.

3. The Compounding Clean-Up Bill (The Backlog Penalty)

The real financial hit from an AI mistake isn’t the single wrong answer—it’s the clean-up. When an unmonitored model gives flawed advice while reporting that everything is healthy, bad decisions accumulate unnoticed. By the time a client complaint or regulator flags the issue, you face an emergency system shutdown and weeks of expensive manual payroll to audit and untangle thousands of settled transactions.

Your AI isn’t failing because the technology is flawed. It’s failing because nobody owns the run.

Your supplier updates the model overnight. (Illustrative)
With Datastone
Model update
Hour 2 — answers drift, the change is flagged · Hour 3 — rolled back to the previous version
3 hours — Minor impact, contained
Without Datastone
Model update
Day 1 — customers notice, tickets start reaching the team · Day 2 — investigation begins · Day 2 — workaround in, cause still unknown
2 days — Patched, not fixed
The short bar isn’t faster firefighting. A bad change was spotted early and undone before it became an outage.
3 hours
Detected and rolled back
2 days
Customers living with it, cause unknown

— SYSTEM RELIABILITY

Most businesses find out their AI is failing in public.
I make sure you catch it in private.

It’s called SRE (Site Reliability Engineering). Google built it to run global systems.
I spent eight years executing it. I don’t build your AI. I build what keeps it running.

AI FIRE DRILL

Half a day stress-testing your systems before a real disaster tests your leadership.

We run a controlled simulation with your leadership and technical teams to see how your AI behaves under stress: simulated supplier outages, bad incoming data, malicious prompt injections, and sudden traffic spikes. You leave with an executive Production Vulnerability Scorecard detailing your architectural weak spots, who is accountable for what, and the exact fixes needed before you suffer a public failure. Fixed scope, agreed upfront, zero ongoing obligation.

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AI RELIABILITY COVER

Ongoing operational ownership over your production AI

We turn “is our AI working?” into clear, agreed numbers your board and executive team understand. Real customer scenarios are tested automatically every single morning, every supplier update is checked before customers see it, and any degradation is caught before it spreads. When issues occur, I coordinate the containment, pinpoint the root cause, and ensure the system is hardened so it cannot fail the same way again. Includes a monthly board-ready reliability report.

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AI RELIABILITY PROOF

For companies selling AI software to banks, insurers, and regulated enterprises

Enterprise procurement and risk committees reject unmonitored black-box software. We audit your product’s operational resilience, establish automated guardrails, and provide documented failover proof that satisfies enterprise risk teams under APRA CPS 230 and MAS guidelines. Win enterprise deals with audit-ready proof instead of sales promises

Explore Vendor Proof

Building AI and running it are two completely different jobs. Most businesses only planned for the first. Development agencies are paid to hit launch dates, but the real risk starts on Day 2: AI models shift, suppliers update without warning, and the data changes constantly. At Google, we learned that complex software only survives with disciplined operational guardrails.

Without them, your customers become your testing team.

– Dean Baron
Founder & Principal SRE · Ex-Google Operations (8 Years)

— BEYOND OBSERVABILITY DASHBOARDS

Deployed vs. Run (The Run Method)

Without Operational Ownership
With The Run MethodGoogle SRE Discipline

Success is declared on launch day and never re-evaluated

→
01

Define

Agree on what “working” means in clear business numbers—accuracy thresholds, response times, and acceptable cost.

Google SRE: Service Level Objectives

IT dashboards show green servers while the AI gives wrong answers

→
02

Watch

Real business scenarios are automatically tested every morning before staff or customers log in.

Google SRE: Synthetic Probers

Model drift is noticed weeks later after margins have leaked

→
03

Allow

A strict, agreed limit on mistakes. If that limit is crossed, feature development halts to fix stability first.

Google SRE: Error Budgets

Supplier updates push directly to live customers unverified

→
04

Gate

Every prompt change, supplier update, and database shift is automatically quarantined and tested before release.

Google SRE: Canaries & Release Reviews

The same edge case trips up the business repeatedly

→
05

Learn

Every customer complaint or failure is converted into an automated test, ensuring the system can never fail the same way twice.

Google SRE: Blameless Postmortems

Your first test of a failure is an actual public outage

→
06

Drill

Supplier blackouts and extreme scenarios are simulated during office hours so your system handles them automatically at night.

Google SRE: Chaos Testing / DiRT

Want to apply The Run Method to your stack? Schedule an introductory briefing

— QUALIFIED ENGAGEMENT

Who it’s for

Built for organisations that cannot afford to learn about failure in public.

Still at pilot stage?

Book an AI Fire Drill

AI That Directly Drives Revenue

Live in front of customers or embedded in core business processes where errors directly cost money: quoting, claims assessment, credit underwriting, support routing, or logistics scheduling

Regulated Under APRA CPS 230 or MAS

Australian financial institutions and Singapore enterprises are legally accountable for third-party operational risk and service continuity. Reliability Cover provides the operational evidence and resilience testing your risk committee requires.

B2B AI Vendors Selling to Enterprise

You need to pass enterprise vendor risk, info-sec, and operational continuity reviews before enterprise clients will sign your master services agreement

Teams Running In-House / Private Models

Moved AI in-house to protect proprietary data or control cloud costs? There is no supplier status page to blame and no vendor fixing it for you. Operational ownership belongs entirely to your business

Still evaluating a proof-of-concept? Run an AI Fire Drill before flipping the switch to live customers.

Production reliability isn’t theory

I’ve run this at Google scale

The discipline behind The Run Method comes from eight years managing production systems at Google, where availability was a binding contract measured in fractions of a percent. I managed multi-region infrastructure across APAC, where failure containment was measured in minutes, not days. I bring that exact operational discipline to businesses deploying AI where failure carries real commercial consequences.

35k+

Systems managed within Google-scale production environments

8 yrs

Hands-on Site Reliability and Production Systems Engineering at Google

APAC

Cross-border infrastructure footprint (Sydney & Singapore aligned)

Minutes

Incident containment target using automated circuit breakers and fallbacks

Frequently Asked Questions

A monitoring tool alerts you that a server is down or slow. It cannot tell you that your AI just hallucinated an unauthorized 40% discount, misquoted an insurance policy, or gave bad advice to 500 customers. Furthermore, developers are paid to build new features, not manage defensive edge cases. SRE builds the automated safety net so your developers can keep building without being pulled into emergency operational clean-up.

True SRE eliminates human dependency. I don’t sit in front of a screen manually watching your systems. I engineer automated safety mechanisms—daily business checks, automated pre-release test gates, and instant fallbacks—directly into your cloud environment. If an AI supplier crashes at 2:00 AM, the system handles it automatically. Everything is codified, documented, and fully owned by your team.

No. I don’t build your AI. I build what keeps it running. When your model needs prompt adjustments, retraining, or new features, your internal developers or original agency make the changes. I provide the automated testing gates that verify those changes won’t break production before they reach customers.

Your data stays inside your own systems. All testing harnesses, guardrails, and monitoring tools run directly inside your own cloud account (ie AWS Sydney or AWS Singapore, according to your locale). We do not store, copy, or retain your customer prompts, personal information, or business records on external Datastone servers.

Yes. Brisbane and Singapore share a near-identical time zone (+2 hours AEST / SGT). Operational drills, reviews, and technical response happen seamlessly across regular business hours in both regions, fully compliant with Australian Privacy / APRA regulations and Singapore MAS governance principles.

Find out what happens when your AI goes wrong – before your customers do.

Schedule a 20-minute introductory briefing. Tell me what your AI does and what an hour of downtime or flawed answers costs your business. I’ll tell you exactly where your operational weak spots are.

Schedule a 20-Minute Briefing Book an AI Fire Drill

Confidential · No obligation · Brisbane-based, serving Australia and Singapore

AI Fire Drill

AI Reliability Cover

AI Reliability Proof

The Run Method

Selling AI software to banks or insurers? AI Reliability Proof clears enterprise vendor risk reviews faster →

Datastone · I build what keeps your AI running · Brisbane, Australia · ABN 73 878 715 828