Stories, case studies, points of view and experiments from the intersection of retail operations, product and AI. Start with these public posts; fuller work stories can join this collection as they are cleared to share.
Each entry has a clear type and a source. Public posts link to LinkedIn; original work published here has its own page. The collection is curated, not an auto-synced feed.
A proposed concept and build blueprint for a gesture-driven smart mirror that helps a user get ready for the day. It covers the experience, computer vision, hardware, software, network, systems and operations, with every claim tagged by evidence level. It is a concept, not a deployment.
Read the entryA first-person experiment in AI knowledge work: one orchestrator, a council of specialist harnesses, and evidence before every done. Every claim is labelled Reported, Observed, Design or Roadmap. Kept current as the experiment changes.
Read the entryCase study | Mining and resources | Retrieval-augmented generation | Reference design Key findings Exploration decisions rest on data that already exists. Geological maps, geophysical and geochemical surveys, earlier drilling and tenure records are mostly collected before a rig arrives, and they are scattered across formats, decades and organisations. Retrieval-augmented generation fits this problem because […]
Read the entryCase study | Retail operations | Video analytics | Solution design for an existing store estate Key findings A video analytics proposal is only as persuasive as its metric catalogue. Buyers approve numbers they can read in a weekly review, not capabilities they cannot price. The value sits in the join. A camera count means […]
Read the entryCase study | Engineering operating model | Agentic AI and governance | Design programme, 2026 Key findings Agentic coding raises throughput, but throughput without governance moves risk faster than value. The design question is where humans stay in the loop, not whether to use agents. A three-plane model separates concerns cleanly: a control plane that […]
Read the entryCase study | Retail operations | Computer vision and analytics | Solution proposal, September to October 2021 Key findings A large installed camera base is a sunk cost that can be turned into an operating-data asset without replacing a single device. The winning move against an incumbent was specificity. A catalogue of about 150 metrics, […]
Read the entryCase study | Retail and commerce systems | Generative AI | Engagement period: March 2024 to March 2025 Key findings Product data fails at the structure layer first. Search, discovery and categorisation errors trace back to an inconsistent taxonomy and attribute model, and every downstream system inherits them. Generative AI helps most when it works […]
Read the entryCase study | Practical AI | Computer vision data operations | Problem, opportunity and reference design Key findings In retail computer vision, the model is rarely the bottleneck. Getting enough correctly labelled images of the right situations is. The catalogue changes constantly, so a labelled set is never finished. Data operations need a standing process, […]
Read the entryCase study | Retail technology | Unattended retail | Problem, opportunity and reference design Key findings Unattended retail only pays when it removes cost the store actually carries, such as labour hours and shrink. Convenience alone does not fund it. The format is a spectrum. A smart cooler, a micro-market and a full walk-out store […]
Read the entryCase study | Retail technology | Computer vision | Problem, opportunity and reference design Key findings Retailers have already scaled self-checkout. The open problem is losses and exceptions at the lane, not whether shoppers will use it. A vision-based tray checkout replaces scanning with recognition. That moves risk from the shopper’s hands to the model’s […]
Read the entryCase study | Quick-service restaurants | Supply chain planning | Proposed solution for a multi-market deployment (response prepared August 2024)
Read the entryA detailed experiment in coordinating specialist AI profiles, choosing structured execution paths and checking outcomes. Implementation changes are attributed to Hermes's own execution report, not independently verified; performance estimates are excluded.
Read the entryPicture a colleague carrying out a task on a busy floor. A system notices a possible mistake. What should happen next: an alert to a manager, a useful prompt to the colleague, or nothing until a person checks the evidence?
Read the entryA small-business owner does not wake up wanting a workflow engine. They want fewer missed appointments, clearer order updates or a way to follow up without losing the thread.
Read the entryA student opens a learning app after missing several days. The streak is gone. The backlog is longer. The app has measured absence, but has it helped the student begin again?
Read the entryPicture a housing society agreeing to buy an essential together. The price looks attractive. Then the questions begin: who chose the vendor, who agreed to the quantity, and what happens if a household changes its mind?
Read the entryImagine a hospital receiving an early warning that demand may rise. The warning lands in a dashboard. Staffing, supplies and patient-flow decisions stay unchanged.
Read the entryImagine asking a company knowledge bot how a product works. It answers confidently. Then an engineer points out that the document it used belongs to an older release.
Read the entryThe sales receipt is the visible part of a checkout. Behind it sit products, prices, tax rules, tender types, staff permissions and a history of changes.
Read the entryPicture a shopper holding an item at the shelf. The label says one price. The till says another. The shopper does not care which system was updated first. The store has broken a promise.
Read the entryAn agent needs a deliberate path when its first inference route slows down or fails. This five-tier architecture experiment maps the handoff choices and what should be observed at each step. It is a design record, not proof of production uptime, low cost or answer quality.
Read the entryA personal AI workflow can screen material against interests the reader declares, then prepare a short brief for review. This entry explains the curation loop I am building and the questions it still needs to answer. It makes no claim of measured time savings or automatic improvement.
Read the entryA warehouse management system can record a pick after it happens. What would it take to catch a mismatch while a person can still correct it? This concept sets out an observe, compare, guide and confirm pattern, with human control and field testing still open.
Read the entryWhen product categories and attributes disagree, adding GenAI can make catalog errors harder to spot. This entry shows my approach to a product ontology: organize the relationships first, then design enrichment and integration. It does not claim a verified uplift in discovery or revenue.
Read the entryStore cameras can capture movement and queues, but footage alone does not tell a team what to do. I designed a video-intelligence concept connecting potential signals to operational decisions, integration work and a measurement plan. This is solution design, not a production deployment.
Read the entryA retailer already had cameras, but no clear way to turn footage into operating decisions. This proposal framed video analytics around questions a store team could act on, using existing systems. It was a proposal, not a deployment or measured result.
Read the entryAn argument for connecting the decisions made at HQ to what teams can actually carry out in stores.
Read the entryA practical exploration of where domain-tuned models might help merchandising, supply chain and customer experience. An idea to test, not a deployed case result.
Read the entryA look at AI-assisted product-data matching, enrichment and policy checks - and the need to keep ownership clear.
Read the entryA field-facing view of how product choices and store operations can meet in the customer experience.
Read the entryNew stories, case studies, points of view and experiments will join this collection when their evidence and sharing rights are clear. These are curated selections, not a live feed.
Start with the context; we can choose the right conversation from there.
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