Fullstacked
You cannot automate bad data. You can only scale it faster.
Every vendor is selling you an AI agent. Almost none are checking what it would actually run on. I diagnose the foundation before you bet your HR function on it.
Book a 30-minute call →Former Navy SEAL. Columbia HCM. Most recently led a high-stakes UKG stabilization for a large multi-site organization.
The problem most HR functions are about to learn.
The bottleneck is not the agent. It is the foundation the agent will run on.
Layer an AI agent on top of drifted field mappings, five years of accumulated org-data inconsistencies, and integrations nobody owns, and it will operate with total confidence on broken inputs. The errors will not slow down. They will speed up.
Here is what is usually hiding underneath the dashboard:
- Field mappings have drifted from their original intent across multiple admin transitions.
- Org structure data carries five-plus years of accumulated inconsistencies.
- Integration jobs run with no notification and no named owner.
- Workflows depend on tribal knowledge that walked out when a key admin left.
- Reporting pulls from sources that quietly disagree with each other.
None of this shows up on the executive dashboard. All of it surfaces the moment an agent acts on it.
The three layers I diagnose.
Data Integrity
Are the fields an agent would read trustworthy? Field mapping, validation rules, source-of-truth ownership, reconciliation paths.
Integration Reliability
Can the agent's inputs and outputs be trusted to fail loudly instead of silently? Job ownership, notification configuration, dependency mapping, monitoring.
Human-in-the-Loop Governance
When the agent makes a recommendation, who reviews it and how? Escalation paths, decision logs, control points, named ownership.
What you walk away with.
- A receipts-backed assessment of each layer, with named failure points.
- A prioritized risk register tied to payroll, compliance, and reporting exposure.
- A 30-60-90 stabilization sequence.
- Named ownership recommendations for every control point.
- A go or no-go signal for any agent or automation deployment currently under consideration.
No 40-slide deck. No platitudes. Receipts.
Case patterns.
Names redacted. Patterns are universal. If one of these sounds like your system, it probably is your system.
Pattern A. A termination workflow that paid people after they left.
The surface symptom was recurring payments that kept firing after assignments had ended. Root cause: the termination process defaulted to leaving recurring pay active unless someone manually turned it off, and the manual step rarely happened. I built an admin-initiated termination workflow with manager approval gates, automated inactivation of recurring pay, and named ownership for every step. The single most important payroll control point moved from manual to automatic. Future leakage prevented by design, not by somebody remembering.
Pattern B. Modules paid for, never turned on.
The surface symptom was a leave-of-absence process running on a spreadsheet, eating hours per case. Actual finding: the org was paying for a leave module that had been misconfigured under prior leadership and abandoned, and nobody on the current team knew it existed. Three dormant paid-for systems surfaced in the inventory. One was activated and put into production. The renewal posture was reset to reflect what was actually being used.
Pattern C. Integrations that look green but are not.
The weekly payroll-to-timekeeping feed looked clean in the logs. Employees still got paid at wrong rates. Root cause: one system treated Employee Type like a Job Code. The other did not. The mismatch was invisible until it hit payroll. I ran a field-mapping walkthrough, set a naming standard, and rebuilt the integration against real test cases instead of synthetic data. Payroll errors traced to root cause and fixed.
How I work.
Diagnostic first. Fixed price. Four to six weeks. I map what is actually happening versus what leadership thinks is happening. You get a receipts-based report with ranked risk and a stabilization sequence. This is the Agent-Readiness Diagnostic.
Stabilization second. If the gaps warrant it, I run a fixed-scope stabilization engagement. Process redesign, system reconfiguration, SOP build, governance handoff. You own the system when I leave.
Ongoing, if you want it. A lower monthly retainer for standards maintenance, integration monitoring, and governance enforcement after the build.
AI-first delivery. Small team, AI-leveraged. A senior operator on every engagement, not a partner pitch followed by a room of associates.
Clean handoff. My job is to work myself out of the build, not to manufacture dependency. The ongoing retainer is there if you want your standards held, never because you are stuck with me.
Who I am.
Brett Chappell. Founder, Fullstacked.
Former Navy SEAL. Columbia University HCM. Former Chief People Officer at mission-driven organizations. Twenty years operating in environments where a quiet system failure gets counted in hours, money, or trust, and where nobody cares about your explanation after the fact.
I built Fullstacked because I got tired of seeing the same failures in different companies. Same payroll leaks. Same orphaned modules. Same people doing heroic manual work to paper over systems nobody owns.
I work on a receipts-only standard. Every claim is backed by evidence. Every recommendation traces to a control point. No theater. No automation before stabilization.
If you want a motivational deck, there are better firms. If you want receipts and a foundation that will not quietly break the moment AI touches it, I am the call.
Contact.
One way in.
Book a 30-minute call →Or email brett@fullstacked.ai
No contact form. No lead-capture ladder. If the work fits, a conversation is the next step.