Sample assessment · Retail / grocery
Meridian Grocers — Multimodal AI Readiness & Architecture
Illustrative sample. Meridian Grocers is a fictional 85-store regional grocery chain; figures are representative, not client data. This excerpt shows the shape and depth of the real deliverable.
Executive summary
Meridian generates multimodal data everywhere it loses money: 6,800 security cameras watching self-checkout shrink happen, and a paper-driven receiving dock leaking vendor overbilling. Neither is used by a single automated system. The AI program to date — a chatbot pilot on the intranet — touches none of this value.
Recommendation: start with documents, prove with video. Document extraction pays for the program in one quarter with almost no risk, while the self-checkout golden set is built. Do not sign a video-AI vendor before the eval set exists.
1 · Use-case & value map
| Use case | Data | Est. value | Verdict |
|---|---|---|---|
| Receiving-dock document extraction | Scanned invoices, bills of lading | $1.1M/yr | Do first |
| Self-checkout loss detection | Existing camera feeds | $2.8M/yr | Pilot in 5 stores |
| Shelf-gap & planogram compliance | Existing camera feeds | $900K/yr | Phase 2 |
| Customer journey heat-mapping | Camera feeds | Unclear | Defer |
| Supplier email triage | Text only | $120K/yr | Not multimodal |
- Receiving-dock document extraction: Highest certainty, lowest risk. Off-the-shelf document AI covers 90% of formats; 6-week payback.
- Self-checkout loss detection: Biggest prize — shrink at self-checkout is 3.5× staffed lanes. Needs an eval set before any vendor bake-off.
- Shelf-gap & planogram compliance: Same camera infrastructure as loss detection; sequence it second to reuse the eval and edge stack.
- Customer journey heat-mapping: No owner for the output and no decision it would change today. Revisit after Phase 2.
- Supplier email triage: Real value, but a text-only workflow tool — do not burden the multimodal program with it.
2 · Reference architecture
Two pipelines, one evaluation backbone:
- Documents (cloud): receiving-dock scans → hosted document AI → validation UI for exceptions → ERP. Buy the extraction; build only the thin validation and routing layer.
- Video (edge): camera feeds → edge inference boxes per store (existing NVR closets have rack space and power) → event clips + metadata to cloud for review and retraining. Store bandwidth makes cloud-first video unworkable.
- Eval backbone (shared): golden sets, per-store accuracy dashboards, drift alerts. Every model — bought or built — is measured on the same harness before and after deployment.
3 · Production & reliability gaps
- No evaluation set. No labeled examples of shrink events or extraction ground truth. Every vendor demo is unfalsifiable until this exists. Build a 500-example golden set in weeks 1–2.
- Store network constraints. 6,800 cameras stream to in-store NVRs on 100Mbps links. Cloud-only inference is a non-starter for video; plan edge inference, cloud for documents.
- No monitoring or drift plan. Store lighting, seasonal displays, and camera bumps will degrade accuracy silently. Production plan includes per-store accuracy dashboards and drift alerts.
- Single-person data team. One data engineer supports BI today. The roadmap assumes buy-over-build until a second hire lands.
4 · Roadmap
- Weeks 1–6Ship document extraction at the receiving dock (buy: hosted document AI + thin validation UI). Build the shrink golden set in parallel.
- Weeks 7–16Self-checkout loss detection pilot in 5 stores against the golden set. Vendor bake-off, edge deployment pattern, human-review loop.
- Quarter 3+Scale winner to all 85 stores; add shelf-gap detection on the same stack. Hire the second data engineer before this phase.