Market Horizon | AI Workforce Systems
Market Horizon is an AI consulting firm that engineers continuously-operating AI workforce systems for enterprises. The platform combines four layers: data ingestion (ERP, marketplace APIs, documents, internal systems), orchestration (Claude 4.7 Opus, GPT-5.5, Gemini 3 Pro, Llama 4 Maverick), specialized agent execution (finance, commerce, research, logistics), and an action plane (ERP write-back, communications, analytics, human review queues). Deployments go live in weeks across 24 use-case categories spanning 11+ industry verticals, with a 168-hour continuous operation window, ~90 percent payroll dependency reduction for modeled functions, and human approval gates for high-stakes decisions.
Case Study
Case Study
How a $50M consumer-health brand
replaced 90% of routine ops payroll dependency in 12 weeks.
01 / Background
High complexity. High stakes. High SKU count.
A $50M consumer-health brand operating across Amazon, retail club channels, and direct-to-consumer faced mounting operational drag. With hundreds of active SKUs, six marketplace storefronts, and expanding regulatory requirements, the business had outgrown its manual operating model.
Senior staff were spending the majority of their working hours on reconciliation, bid management, review triage, and compliance tracking — tasks that demanded precision but not judgment. The cognitive load was real; the leverage was not.
Market Horizon was engaged to assess whether an AI workforce deployment could absorb the routine operational load and return senior bandwidth to strategy.
02 / The Bottleneck
Four failure points compounding each other.
Manual reconciliation across ERP, marketplace, and 3PL systems introduced a 48–72 hour lag between events and awareness. Inventory drift was caught only after stockouts or overstock events had already damaged margin and rank.
PPC fragmentation across hundreds of campaigns meant bid adjustments were made weekly at best, against data that was already stale. ACoS crept upward quarter-over-quarter while the team’s attention was elsewhere.
Review triage, regulatory literature tracking, and compliance monitoring all required dedicated human attention that the organization couldn’t sustain at scale. Every new SKU added overhead, not leverage.
03 / The Deployment
Four agent waves. Twelve weeks. Full coverage.
Weeks 1–3 — Finance agents: Continuous reconciliation across ERP, marketplace, and 3PL. Anomaly detection with human-approval escalation. Automated PO generation for routine replenishment cycles.
Weeks 4–6 — Commerce agents: Real-time PPC bid optimization across Amazon Sponsored Products. Listing health monitoring, keyword harvesting, and pricing signal detection. Campaign pacing and dayparting automation.
Weeks 7–9 — Research & Regulatory agents: FDA and regulatory literature crawling with structured synthesis output. Ingredient compliance flagging, label review drafts, and competitive intelligence digests delivered to the team daily.
Weeks 10–12 — Logistics agents: Inbound shipment scheduling, carrier rate comparison, and 3PL coordination. Inventory reorder signals with lead-time awareness baked into recommendations.
04 / Results
Measured outcomes at 90-day mark.
| Metric | Before | After | Change |
|---|---|---|---|
| Payroll dependency (modeled functions) | 100% | ~10% | −90% |
| Ops response latency | 48–72 hrs | <1 hr | −98% |
| Regulatory pull-through | Baseline | 6× baseline | +600% |
| PPC ACoS | Baseline | −22% vs baseline | −22% |
| Weekly ops-meeting time | ~8 hrs/wk | ~2.5 hrs/wk | −70% |
| Inventory drift incidents | Monthly | Near-zero | ~−95% |
05 / Always On
Twelve agents running 168 hours a week.
06 / What’s Next
Agent generalization and cross-account routing.
The next phase extends the agent fleet across additional brand accounts within the same parent organization, enabling cross-account inventory rebalancing and shared intelligence on regulatory signals. Agent personas are being generalized for deployment across adjacent verticals.
Human review queues are being refined to surface only genuinely novel edge cases — reducing the approval overhead while maintaining human authority over high-stakes decisions.