Claude AI training for sales & marketing teams in Singapore
Claude AI training for sales teams in Singapore: one hands-on day, onsite, on your own files. Account research. Proposals and campaigns. Content and follow-up.
Claude Cowork for sales & marketing teams
Bid documents, RFPs and contracts are where sales teams lose days. This programme uses Claude on that long material, and keeps approved claims and inferred claims clearly apart.
Where the work happens
- Long documents
- Contract review
- Structured extraction
- Multi-source synthesis
This programme is built around long, dense documents — the contracts, reports and policy sets where the work is reading carefully rather than writing quickly.
Who this programme is for
Sales, account management and marketing teams. Knowledge, legal and research teams.
- Account managers and sales teams preparing for discovery meetings and renewals
- Bid and proposal teams responding to RFPs and tenders
- Marketers adapting approved content across channels and audiences
- Sales and marketing leaders deciding which claims AI may draft and who approves them
Not the right fit
- Teams looking for a vendor demo rather than hands-on practice
- Anyone expecting AI to send customer communications or publish content without review
- CRM implementation or marketing-automation build projects — this is applied assistant training
What the day covers
Module 1: Account research
RFPs and tender packs are read into a requirements matrix your team answers against.
Exercise. Build an account brief from approved sources, distinguish evidence from assumptions and prepare discovery questions.
What you leave with. A referenced account brief and meeting plan.
Module 2: Proposals and campaigns
Proposal responses are drafted from approved material, with every claim tied to its source.
Exercise. Turn a customer brief into a proposal outline, campaign concept and audience-specific messages.
What you leave with. A proposal template and campaign brief.
Module 3: Content and follow-up
Contract redlines and customer correspondence are summarised without losing commitments.
Exercise. Adapt approved content across channels and draft follow-ups from meeting notes with brand and factual review.
What you leave with. A content pack and follow-up workflow.
A worked example: RFP to requirements matrix
One exercise from the day, as it runs on your own material in Claude Cowork.
- What goes in
- A 90-page tender document with appendices.
- What is asked
- List every mandatory and optional requirement, the clause it sits in, the submission format and the deadline.
- What comes out
- A requirements matrix the bid team answers against.
- What the reviewer checks
- The bid lead spot-checks a sample of clauses and reads the evaluation criteria in full, because a missed mandatory item disqualifies a bid.
Singapore rules the exercises respect
Sales and marketing output in Singapore carries obligations on both data and claims.
- Prospect data used in research and outreach is subject to the PDPA, including the Do Not Call provisions for telephone and SMS marketing.
- Marketing claims are checked against the Singapore Code of Advertising Practice and your own approved claims list.
- Customer names and deal terms stay out of prompts unless your workspace terms and internal policy allow them.
What this day does not cover
Scope is agreed before the session so the day stays hands-on.
- Coding, scripting or developer workflows, even though the tool is capable of them. This is a day on document and analysis work.
- API integration or automated pipelines.
- Legal advice. We cover how to review a document carefully and what to escalate, not what position to take on it.
Before the session
- A licensed Claude Cowork seat for each participant
- Working knowledge of your own sales process, offer and brand guidelines
- Anonymised or sample working files — an account brief, a past proposal, approved product copy
- No coding background required
Format and schedule
- Delivery
- On site in Singapore or remote, delivered live. Cohorts are kept small enough that every participant works on their own files rather than watching a screen share.
- Duration
- Typically one full day, or two half-days scheduled around quarter-end. Module order is adjusted to your calendar and tool stack.
- How each module runs
- Each module follows the same pattern: a short walkthrough of the technique, a hands-on exercise on your own data, then a review of what the assistant got wrong and how to catch it.
- Afterwards
- Participants leave with the prompt patterns, templates and review checklists produced during the session, so the work continues after the training day.
How your data is handled
- Exercises run inside your own tenant and your own licences, so your data does not leave your environment for the training.
- Files shared with the trainer in advance are anonymised extracts; customer records, pipeline data and pricing stay inside your own tenant and licences.
- Every module includes verification: checking output against the source, confirming what the assistant left out, and marking what still needs a human read.
- Customer-facing output is reviewed before it is sent or published. The programme teaches AI-assisted drafting and research, never unattended outreach.
Who runs the session
Abhijit Ghosh
Founder, Eagentix · Data & Generative AI Practitioner · Singapore
The programme is delivered by Abhijit Ghosh, who has spent 25 years in data and AI, including roles at Accenture, IBM, Deloitte, Cloudera, Tibco and Wipro. Sessions are run by the practitioner, not handed to a junior facilitator.
Questions about Claude Cowork for sales & marketing
Will it help with RFPs and tender packs?
That is the first module. A long tender document is read into a requirements matrix your team answers against, which is usually the step that costs days and gets rushed.
Why choose Claude for this work rather than the assistant bundled with our office suite?
Because the bottleneck here is long, dense reading rather than quick drafting. The programme is built around documents that take a person an afternoon, and around making the reasoning visible enough that a reviewer can check it clause by clause.
How long can the documents be?
Long enough that the constraint in practice is your review capacity, not the tool. We work with your real documents on the day, and part of the exercise is deciding what still needs a full human read.
Can we put prospect lists into the assistant?
Only within your workspace terms and internal policy, and outreach still has to respect the PDPA's Do Not Call provisions. The session uses public sources and anonymised CRM extracts for the research exercises.
How do we keep generated claims accurate?
Every exercise works from an approved claims list. Anything the assistant produces outside that list is flagged for marketing sign-off before it reaches a customer.