Teach the team what AI actually does.

Live, instructor-led sessions tailored to your team’s role and skill level. We start with a pre-engagement intake so the workshop opens at the right altitude — no time wasted explaining what a large language model is to a team that already knows, no time lost on advanced prompt engineering for a team that needs the basics first. Workshops run two to four hours, are capped at twelve participants for genuine interaction, and are recorded so anyone who missed the live session can catch up. Material is bilingual on request and delivered in either English or Mandarin without losing nuance in translation.

A curated collection of the AI use cases that actually apply to your business — drafted from interviews with each department, ranked by frequency and time saved, and documented as ready-to-run examples. Marketing has its own library, operations has its own, the legal or finance team has its own. Every entry includes the prompt, the expected output, the failure modes to watch for, and the human-review step that should follow. The library lives in the tool your team already uses — Notion, Confluence, SharePoint, Google Docs — and updates as the tooling and the team’s confidence evolve.

Most teams learn AI by trial and error, accumulating a private cache of prompts that work for them and never sharing them. We turn that scattered knowledge into a documented set of patterns — the structured templates for common tasks like summarising long documents, classifying inbound enquiries, drafting from a brief, extracting structured data from unstructured text, or rewriting in a particular voice. Patterns are tested against your real material before they are added to the library, and we teach the principles behind them so your team can adapt them rather than copying blindly.

A short written guide for each department covering the AI tools they have access to, the workflows where AI fits and where it does not, the data they may use it with and the data they may not, and the escalation path for anything ambiguous. Marketing, sales, customer service, operations, finance, and HR each get a playbook scoped to their work. Playbooks are concise — typically four to six pages, written in the team’s own language — because nobody reads a forty-page handbook. We update them as the rules of engagement and the tooling change.

A separate, focused session on what to put into AI tools and — far more important — what not to. We cover the data classification your business already operates under, the retention and training-opt-out posture of each provider you use, the regulatory framework that applies to your industry (PIPEDA, GDPR, HIPAA, FINRA, professional-conduct rules where relevant), and the practical scenarios where the wrong paste into a chat window becomes a disclosure incident. Delivered in plain language with concrete examples drawn from your sector, and updated whenever your provider mix or regulatory exposure changes.

Reading about AI is not the same as doing it. Labs are working sessions where the team brings real, current work — a campaign brief, a draft contract, a data extract, a customer complaint — and runs it through the tools we have set up, in real time, with us in the room. We pair stronger users with newer ones, surface the questions nobody wants to ask in the open workshop, and resolve them on the spot. By the end of a lab, every participant has produced at least one piece of real output that earns its way into their actual work.

Every workshop, lab, playbook, and compliance briefing leaves a written artefact behind. Quick-reference cards for the most common tasks, screen-recorded walkthroughs for the harder ones, decision trees for the judgement calls, and a single-page index your team can search when they hit something unfamiliar months later. Documentation is hosted where your team already lives — not on a separate portal nobody opens — and is written so a new starter can come up to speed without a fresh round of live sessions.

A standing weekly slot where anyone on your team can bring a problem and get it solved with us live. Office hours are how we close the gap between the workshop and the day-to-day, and they are where the most useful additions to the use-case library tend to come from — a marketer’s awkward report becomes a documented pattern, a paralegal’s clause-review approach becomes a department-wide standard. Office hours run as long as the engagement runs, and we keep a running log of every question and resolution so the same one never has to be answered twice.