EDGE GenAI chatbot: test answers and human handoffs

An EDGE GenAI chatbot project needs an answer boundary and a human handoff before it needs more content. The company should identify the enquiries the chatbot may answer, the matters it must refuse or escalate, and the employee who owns each escalation queue. That operating design turns a vendor package into a controlled customer-service project and creates evidence for acceptance, benefits and the eventual claim.

Select a bounded enquiry set

Review recent chat, email and call records and group enquiries by purpose. Start with stable, low-risk questions such as opening hours, order status, appointment availability or published product information. Exclude complaints, refunds, safety issues, contractual advice and sensitive account changes until a responsible owner has designed a reliable escalation path.

For each included question, name the approved source and review date. The source may be a product catalogue, public policy page or controlled knowledge article. Do not let the system improvise prices, eligibility, legal terms or delivery commitments from outdated conversation history. A small, maintained answer set is more useful than broad access to uncontrolled documents.

Understand the supported package

The current GenAI chatbot activity page describes software that automates customer enquiries, personalised recommendations, feedback collection and lead follow-up. It lists a S$15,000 activity cost cap, up to 12 months of software costs and an 18-month completion period. GST and maintenance are not supported under the published activity.

Build the package through SMEs Go Digital and match it to the vendor quotation. State channels, licences, included integrations, implementation work, training and subscription dates. Keep optional marketing services or custom development separate. The company needs the full cost and scope even where only part of the purchase is supportable.

Design the human handoff

Define triggers that send a conversation to a person: repeated failure, low confidence, an angry customer, a request involving personal data, a regulated claim or a request to change an account. Tell the customer when the handoff happens and what information will be transferred. Do not make the customer repeat sensitive details merely because the systems are disconnected.

Assign queue owners and service times by enquiry type. Create a fallback when the chatbot or integration is unavailable. The employee receiving the case should see the source answer, conversation history and reason for escalation. Measure whether the handoff resolves the issue, rather than treating the transfer itself as success.

Run an acceptance set based on real language

Build test questions from genuine customer wording, spelling errors, mixed languages and incomplete requests. Include questions that look similar but require different outcomes. Review accuracy, source alignment, tone, privacy, escalation and whether the system states uncertainty. Keep screenshots and results for each test version.

Repeat the set after configuration changes. A demonstration that worked once cannot show that the deployed version remains controlled. Record who approved the final answer set and the date. If the vendor changes the underlying model or retrieval method, rerun the material tests before widening customer access.

Calculate savings without counting every contact

Use a baseline of enquiry volume, average handling time, repeat contacts and escalation rates. Count only time genuinely removed from staff work. If the chatbot drafts an answer that an employee still checks and sends, measure the shorter handling time rather than claiming the entire enquiry was automated. State adoption and containment assumptions.

Track service quality beside savings: first-contact resolution, unresolved conversations, complaint rate and handoff delay. A cheaper service that creates repeat contacts is not a productivity gain. Keep sales leads separate from cost savings unless the company can show how the lead process changed and avoid double counting.

Keep the deployment and claim record together

The activity requires a photo of the deployed software and a unique identifier. Capture the production account, licence or user identifier in company context. Reconcile it with the quotation, package reference, invoice and proof of payment. The EDGE FAQ says grant eligibility continues until claim submission, so recheck the applicant before filing.

Use the AI approval checkpoint guide for higher-autonomy use cases and the agentic AI materiality screen when the tool can act rather than only answer. The business support cornerstone remains the funding route reference.

Control knowledge and personal data

Create a register for every document or page used to answer customers. Record its owner, approval date, review date and the questions it may support. Remove draft policies, superseded prices and internal commentary from the retrieval set. Where an answer changes by customer, product or contract, require identity and entitlement checks before the chatbot reveals account-specific information.

Minimise the personal data collected in free text. Tell users not to submit passwords, full payment-card details or unnecessary identity documents. Define how transcripts are stored, who may search them and when they are deleted. Test whether exported logs expose names, contact details or order information to staff who do not need them. Privacy controls should apply to the vendor console as well as the public chat window.

Introduce changes through a dated release record. State which source pages, prompts, routing rules and integrations changed, then run the affected acceptance questions again. Keep a rollback copy and a clear contact for urgent suspension. If the chatbot gives a harmful or materially wrong answer, preserve the conversation, correct the source or rule and check for similar answers before restoring normal service.

The final acceptance paper should show the approved scope, pass and fail results, privacy review, handoff performance and unresolved limitations. Customer-service management, the system owner and the information owner should each sign the part they control. That is stronger evidence than a vendor completion email because it shows the company can operate the service after implementation.

A chatbot is ready when it answers a controlled set well, recognises when it should stop and gives a person enough context to help. Put that service standard into the project file before application, then test the deployed version against it before claiming completion.