Deployment shape · Anonymous
When the customer-facing motion runs end-to-end on one platform.
Some Fairshift deployments go four layers deep. Field app for the team in the warehouse. Operations dashboard for HQ. Customer-facing voice and WhatsApp. Vendor and contractor workspace. Same platform underneath. This is the shape of one.
Four apps. One platform underneath.
A deep deployment is rarely one app. The customer-facing channels need an operations layer behind them. The HQ dashboard needs a field app feeding it. The vendor side needs a workspace that talks to all three.
Field mobile app
Native Android, used by field teams to submit weight tickets, capture photos, and log inward inventory. Offline-capable, local-language UI, GPS-stamped.
Operations dashboard
HQ-level dashboard showing real-time inventory across facilities, processing flows, and quality grades. The agent answers operational questions in plain language.
Customer-facing channels
Voice + WhatsApp + Web chat for B2B procurement enquiries. Same agent, same memory across channels, replies in the customer's language.
Vendor & contractor app
Lightweight workspace for upstream contractors and vendors. Submit deliveries, view payment status, communicate with HQ via WhatsApp.
Live in weeks, deeper every month.
Not a six-month implementation project. The customer-facing channels go live in week one. The deeper surfaces — field app, vendor workspace, operations dashboard — wire up over the following months as the business teaches the platform its operational logic.
Generic AI answers ten questions about your business. Fairshift answers ten thousand.
A horizontal AI tool reaches a ceiling about three weeks in. The questions get specific — domain-specific workflows, regulated quality grades, recursive multi-stage processing, multi-facility movements, per-vendor terms. Fairshift answers those because the data lives on the platform itself, not in a side database the AI is querying through a hose.
The data is the platform's data.
When operations live on Fairshift, the agent answers from primary records, not from a snapshot pulled into a vector store.
Reasoning over real workflows.
Quality grades, multi-step processing, recursive transforms — the agent reasons over them because they're modeled, not sketched in a prompt.
Context that compounds.
Every interaction across every channel adds context. The agent your customer talks to next week has read every interaction up to now.
Want a deployment this deep?
Tell us your industry and the operational questions a generic AI can't answer. We'll show you a path to a deployment that does.