About
Jordan Sekula
AI Systems Architect — agentic orchestration, minimal human-in-the-loop by design
I'm Jordan. I don't consult on AI strategy decks — I operate AI-native businesses and ship the systems that run them. My proving ground is live operations: a multi-brand commercial platform where agents run the full client lifecycle — booking engines, CRM portals, review response, web builds, and a complete content-generation engine from research to creative to scheduling to reporting — across hundreds of brand channels. And a public-data intelligence platform that tracks 8,000+ entities across 56 jurisdictions, autonomously.
Different industries, same architecture: agents do the work, evals prove it's right, humans approve only what can't be undone.
We are in a brief window where humans and AI work in hybrid partnership before AI handles most of the work independently. I build for exactly this transition. The companies I work with aren't experimenting with AI — they're restructuring around it, turning slow, manual operational layers into agentic systems that schedule, communicate, report, and decide, so their teams spend their hours on judgment and relationships instead of busywork.
This hybrid stretch — call it the centaur window — is measured in low single-digit years. The question isn't whether your workflows get automated. It's whether you're the one designing how, or reacting after someone else does.
Proven outcomes
3 roles → 1 system
Partnership ops across hundreds of brand channels
4 hrs → 12 min
Client response time, in the owner's own voice
3 days → overnight
Monthly reporting, 12 locations, 6 data sources
What I operate
Revenue & client systems — booking engines, CRM portals, e-sign workflows, and client-facing web builds, wired into the tools the business already runs.
Communication engines — multi-persona, voice-matched outbound across email, social DMs, and review platforms. Hundreds of interactions a week; humans see only the exceptions.
Content-generation verticals — end-to-end engines: brand research, creative rendering, QA against brand systems, scheduling, and performance reporting. The whole pipeline, not a writing assistant.
Reputation & presence — review response across Google and the major vertical review platforms; Wikipedia and Reddit presence management; the public-facing layer most firms handle reactively, run proactively.
Data & intelligence platforms — multi-source ingestion under hard rate-limit budgets, AI summarization with groundedness checks, drift detection, and automated data-health audits. See the flagship platform below for the live example.
The connective tissue — custom MCP servers in production, deployed as OAuth-protected connectors, so every system above speaks to every tool below it.
Flagship build: autonomous data intelligence, zero staff
The clearest demonstration of what AI-native actually means is a platform I run with zero staff: a public-data intelligence system tracking thousands of entities, the documents they produce, and the financial filings that connect them — across 56 jurisdictions.
The numbers a CTO will care about:
- 8,000+ entity profiles — synced on scheduled, unattended pipelines, with status and role changes caught automatically
- 13,000+ documents ingested and AI-summarized, with groundedness checks before a summary ever reaches an end user
- 34,000+ geographic records across 56 jurisdictions, so any user can resolve every relevant record from a single location lookup
- Five authoritative data sources — registries, filing databases, and geographic datasets — reconciled into one schema, including parsing disclosure PDFs that were never meant to be machine-read
- Hard API budgets, engineered around — one upstream source allows 250 requests a day; the sync pipelines rotate segments across the week to live inside that budget without ever going stale
- Automated data-health audits — stale records, orphaned relationships, and broken sources get caught by the system, not by users
No data-entry team. No moderation queue. The humans in the loop are the readers. That's the operating model I bring to commercial systems.
How I know it works
Anyone can demo an agent. The enterprise question is how you know it's still right on day 200.
Evals before deploys — golden datasets and evaluation harnesses for anything client-facing; prompts are release artifacts with version control and change review.
Observability end to end — every prompt, tool call, and outcome traced. When something drifts, I see it before the client does.
Drift is assumed, not discovered — scheduled regression runs and data-health audits, because model behavior changes even when your code doesn't.
How I work
Discovery — map the workflow, the tools it touches, and the specific hours it's costing you.
Pilot — a fixed-scope build on one workflow, typically live in 6–8 weeks, with a clear success metric agreed up front.
Production — hardened, monitored, and rolled out with your team trained on it.
Support — ongoing monitoring, iteration, and expansion to the next workflow.
Built to be trusted
Your data stays yours — systems run against your own accounts with scoped, least-privilege credentials and permission boundaries at every tool interface. PII never lands where it doesn't belong.
Guardrails, then autonomy — approval gates on anything irreversible or client-facing, with the explicit goal of retiring each gate as the eval record earns it. Minimal human-in-the-loop isn't recklessness — it's the dividend of testing.
Observable and reversible — every agent action logged and traceable, failures degrade safely, and there's always a way back.