Project examples

AI applied to
real work.

Representative engagement patterns showing how Fast Technology Services approaches customer, factory, and warehouse problems.

Customer experience01

Example engagementPattern 01

Customer service answer workspace

A source-backed assistant that helps service teams resolve technical product questions without searching across disconnected manuals and notes.

Starting point: Agents lose time searching across product documentation, past cases, and internal guidance while customers wait for a confident answer.
Applied approach: Design a focused retrieval workflow that finds relevant passages, presents the evidence, and gives the agent control over the final response.

Useful outcome

A clearer path from customer question to verified answer, with less searching and more consistent service.

Smart manufacturing02

Example engagementPattern 02

An operator-facing tool for finding procedures, troubleshooting steps, and shift knowledge at the moment a production issue appears.

Starting point: Critical know-how sits across SOPs, maintenance records, and experienced operators, making response quality depend too heavily on who is available.
Applied approach: Map the troubleshooting journey, connect the right knowledge sources, and design answers around safe next actions rather than generic summaries.

Useful outcome

Operational knowledge becomes easier to access, verify, and carry across teams and shifts.

Warehouse operations03

Example engagementPattern 03

A guided workspace that brings order, shipment, inventory, and procedure context together for faster exception handling.

Starting point: When a pick, stock, or shipment exception occurs, coordinators switch between systems and messages before they can decide what to do next.
Applied approach: Create a single guided flow that gathers the relevant context, classifies the exception, and suggests the next best operational step.

Useful outcome

Less time assembling context and a more consistent handoff between planning, support, and floor teams.

AI product operations04

Example engagementPattern 04

A practical optimization pass for an existing AI product, balancing answer quality, response time, and model spend.

Starting point: An AI feature is useful, but long prompts, repeated context, and one-model-fits-all routing make costs difficult to predict as usage grows.
Applied approach: Measure demand by task, reduce unnecessary context, introduce fit-for-purpose model routing, and make quality checks part of every change.

Useful outcome

A cost structure the product team can understand and improve without treating answer quality as an afterthought.

Your project does not need to look like these