Most enterprise AI deployments fail in production. Not because the model is wrong — because nothing stops it when it is.
I build agentic systems, HITL workflows, and multi-tenant cloud infrastructure that run safely in production for engineering teams at scale.
01 / Problems
The problems I solve
Alert fatigue killing your on-call rotation
Engineering teams spending 40%+ of on-call hours on noise that should never reach a human.
AI that doesn't hold up in production
Promising pilots that fail when they hit real data, real scale, and real edge cases.
No guardrails on autonomous systems
AI writing to databases, calling external APIs, and making decisions with no human checkpoint.
02 / Services
What I build
S/01
Agentic Workflow Implementation
Multi-agent pipelines with HITL gates — AI that escalates to humans before taking consequential actions. Built for production, not demos.
S/02
Cloud & DevOps Architecture
Kubernetes, multi-cloud IaC, CI/CD pipelines, and observability stacks designed for teams that can't afford downtime.
S/03
AI Security & Governance
Guardrails, audit logging, data boundary enforcement, and compliance-ready AI deployments for regulated environments.
STACK I WORK IN
ORCHESTRATION
PLATFORM
OBSERVABILITY
GOVERNANCE
Multi-tenant data isolation enforced at the database layer — not the application layer.
03 / Process
How an engagement works
STEP 01 · Submit intake
Answer 8 questions about your stack, your pain, and your timeline. Takes 4 minutes.
STEP 02 · Fit call or proposal
If there's a match, you get a scoped proposal within 48 hours. No generic decks.
STEP 03 · Execution
Async delivery by default. Defined deliverables, no scope creep, no standing meetings unless your tier includes them.
05 / Track record
15 years. Real production systems.
15+ years
enterprise cloud and AI infrastructure
Boeing · Honeywell Aerospace · CorVel
prior employers
Live in production
agentic systems running today
From aerospace manufacturing to enterprise cloud engineering — the systems I build are designed for environments where failure has real consequences.
06 / Operator
Who you're actually hiring
Kelvin Davis / Principal · Phoenix, AZ
I spent the first half of my career in environments where a bad deploy wasn't a rollback — it was a grounded aircraft, a failed audit, or a claim that didn't pay.
Boeing and Honeywell Aerospace taught me that safety-critical systems aren't built by people who are careful; they're built by people who assume failure and design the stop condition first. CorVel taught me what that looks like when the data is regulated and the customer is a claimant, not a developer.
I build agentic systems the same way. The interesting engineering is never the model — it's the gate in front of the consequential action, the audit trail behind it, and the runbook your team uses at 3 a.m. when I'm not there. That's the whole practice.
I work with a maximum of three clients at a time, asynchronously, in your repositories and your cloud accounts. When an engagement ends, your team owns it — no runtime to license, nothing to unwind.
07 / Fit
Who this is for — and who it isn't
A fit
- +50–500 employees, an existing engineering org, and production traffic today.
- +An AI pilot that works in a notebook and breaks under real data.
- +Autonomous systems touching production data with no approval path.
- +An on-call rotation drowning in alerts nobody has time to tune.
- +A budget owner who can sign without a six-week committee cycle.
Not a fit
- −Pre-product or pre-revenue teams looking for a technical co-founder.
- −Staff augmentation — a body in your standups and your ticket queue.
- −Daily synchronous collaboration or fixed onsite hours.
- −Scope that shifts every week, or no budget owner who can commit to an engagement.
- −Demoware — a proof of concept with no intent to run it in production.
08 / Pricing
Engagement tiers
Most engagements start with a 2-week audit. Some stay for the architecture.
AI READINESS AUDIT
Starting at $5,000
Fixed price · 2–3 week engagement
- —Assessment across 5 dimensions: LLM security, IaC drift, CI/CD posture, agent governance, multi-tenant data isolation
- —Risk-rated findings report (Critical/High/Medium)
- —Prioritized remediation roadmap
- —30-minute findings presentation call
- —PDF report you own regardless of next steps
For CTOs who want to understand their risk before committing to ongoing architecture work.
No long-term commitment. You own the report.
Apply for the Audit →FRACTIONAL AI PLATFORM ARCHITECT
$10,000 / mo
Ongoing retainer · 30-day notice after day 60
- —Full governance baseline deployed in weeks 1–2
- —Ongoing drift detection and remediation
- —HITL governance layer maintenance
- —Architectural review of your team's changes
- —Pull request delivery for approved changes
- —Bi-weekly 30-minute executive alignment call
- —Client portal access and environment monitoring
- —Emergency SLA: 4 business hours response, 4 hours remediation per month
For Series A–C CTOs who need senior architectural direction without $350K+ FTE headcount.
Deployed in 2 weeks. Month-to-month after day 60.
Start with an Audit →EMBEDDED EXECUTION
$15,000 / mo
Ongoing retainer · 30-day notice after day 60
- —Everything in Fractional AI Platform Architect
- —Direct hands-on implementation of new features and integrations
- —Agent architecture build and deployment
- —Monthly implementation deliverable (one scoped build per month, agreed in advance)
- —Up to 8 hours emergency remediation per month
For companies mid-migration, under deadline, or shipping complex AI platform work that can't wait.
For teams that need it built, not just designed.
Start with an Audit →Scope changes outside defined deliverables require a signed change order. No exceptions.
Work that doesn't map to any tier is quoted individually — email me or note it in the intake form. There is no discounted trial and no hourly rate.
09 / Risk
What happens if it doesn't work
The questions engineering buyers ask before signing, answered before you have to ask them.
Month to month, 30 days notice.
No annual contract, no termination penalty, no auto-renew clause. The first 60 days establish the governance baseline; after that, it's 30 days' notice to end it, and you keep everything shipped to that point.
You own everything.
All code, IaC, prompts, eval suites, and documentation are work for hire — delivered into your repositories and your cloud accounts. No proprietary runtime, no license, nothing to unwind if we stop working together.
Bus factor is handled deliberately.
Every workstream is paired with a named engineer on your team, and runbooks plus architecture decision records ship alongside the code. The goal is that your team operates it without me by the end of the engagement.
No staff augmentation, no ticket queue.
Engagements are scoped to deliverables, not hours or headcount. No standing meetings, no sprint ceremonies, and nothing billed by the hour.
Regulated environments are normal here.
Aerospace, enterprise cloud, and claims systems — least-privilege access, audit logging, data boundary enforcement. MSA, NDA, and BAA signed before any access is granted; SOC 2 evidence supported.
Response time is contractual.
Fractional AI Platform Architect: 4 business hours response, up to 4 hours of emergency remediation per month. Embedded Execution: same response time, up to 8 hours of remediation per month. If that slips, the month is credited.
Still a question I haven't answered? Put it in the intake form — I answer every submission personally.
See if we're a fit →Not ready to apply?
Run your infrastructure through the Agent Governance Scorecard first — takes 4 minutes, gives you a risk rating you can share with your team.
10 / Intake
See if we're a fit
Answer 8 questions. If there's a match, you'll get a link to book a 15-minute call.