Demos

Interactive Demos

Each demo starts with the outcome it demonstrates and the baseline a real pilot would compare it against — then walks through the workflow step by step. Every demo includes a human checkpoint — that is deliberate, not decorative. Use the step pills to jump to a step, or use Previous / Next to walk through in order.

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How to read these demos

What to look for: Start with the outcome box at the top of each demo, then (1) what goes in, (2) what the automation drafts or checks, and (3) where a person must approve before anything is sent, posted, or changed. Automation drafts, checks, and routes; a person approves before anything leaves the company. Synthetic names, companies, order numbers, and amounts are invented for illustration only. Each demo ends with an edge path — what happens when the input or a system does not behave.

Tip: inside a demo, use the step buttons or your keyboard arrow keys to move through the steps.

Jump to a demo: A · Sales Follow-Up · B · Customer Support · C · Document Automation · D · Lead Scoring & Routing · E · Purchase Request Approval

Illustrative demonstration — not a client case study.

Demo A: AI Sales Follow-Up

Outcome this demo shows: Every enquiry is qualified, recorded, drafted, and approved by a named owner — nothing is sent automatically, and nothing is left sitting in an inbox.
Baseline a real pilot would compare it against: The share of enquiries with a recorded qualification decision and a named next action, counted over an agreed period before the pilot and again during it. No values exist yet — the baseline is defined before any pilot starts.

The problem: Follow-ups live in inboxes and get chased late.

A new enquiry arrives on a website form. Watch how it is qualified, recorded, and drafted — without sending anything automatically.

Synthetic example data
Name: Sara Haddad (invented) · Role: Operations Director · Company: Northwind Supplies (invented) · Message: “We spend too long chasing order follow-ups.” · Source: website form · No real customer data is used.

1. Incoming enquiry

A visitor submits a form: name, work email, company, and a short description of their problem.

Synthetic example: Sara, Operations Director, asks about automating order follow-up.

2. Qualification

The workflow checks the enquiry against agreed criteria: company, role, and problem type.

Result in demo: qualifies for an AI Opportunity Discovery Call.

3. CRM entry

A lead record is created with the enquiry, source, and qualification notes.

No duplicate is created for an existing contact in this example.

4. Draft follow-up

A draft reply is prepared, referencing the stated problem and proposing next steps.

Draft only — nothing is sent yet.

Draft reply (synthetic): “Hi Sara — thanks for sharing how much time order follow-ups take. The next step is a short call to map that workflow and agree what a pilot would measure.” Shown beside Approve / Edit / Reject — nothing sends until approved.

5. Human approval

Human checkpoint — approval required. A sales owner reviews, edits, and approves the draft. If it is not approved, nothing is sent.

Approval is required before any outbound message.

6. Logged activity

The approved message and the outcome are logged in the CRM for follow-up.

The loop closes with a named owner and a next action.

7. Edge path: duplicate contact

The enquiry matches a contact already in the CRM. The workflow proposes a merge and stops: a sales owner confirms the merge before any record changes. No duplicate record is created, and no draft is sent from a duplicate record.

Synthetic example: the same Sara Haddad enquiry arriving twice still produces one CRM record.

Tip: use the step buttons or your keyboard arrow keys inside this demo.

Book a call about this workflow → · Related: AI Automation & Agents · Professional services case

What changed: The approved reply is sent and the next action has a named owner.

Illustrative demonstration — not a client case study.

Demo B: AI Customer Support

Outcome this demo shows: Every customer gets a verified answer: straightforward questions answered from approved sources, sensitive requests escalated to a person rather than answered unverified, and every case recorded for quality review.
Baseline a real pilot would compare it against: The share of answers sent only after verification or escalation where required, and the share of cases with a recorded resolution — counted over an agreed period before the pilot and again during it. No values exist yet.

The problem: Customers wait for order answers, and sensitive requests risk an unverified reply.

A customer asks about an order. Watch how approved knowledge, confidence checks, and escalation work together.

Synthetic example data
Order: DEMO-8841 (invented) · Question: “Where is my order, and can I change the delivery address?” · Approved sources only: help articles + order-status record. No real customer data is used.

1. Incoming question

A customer asks: “Where is my order, and can I change the delivery address?”

Synthetic example only.

2. Approved knowledge retrieval

The assistant retrieves only from the approved help articles and order-status source.

Unapproved web sources are not used.

3. Draft response

A draft answer is prepared with the order status and the address-change options.

Draft only.

Draft answer (synthetic): “Your order DEMO-8841 is being prepared for dispatch. For the delivery address change, a support agent needs to confirm it with you first.” The draft is held for review because the request is flagged sensitive — it is not sent as-is.

4. Confidence / risk check

The draft is checked for confidence and for policy-sensitive content.

Address changes are flagged as sensitive in this example.

5. Human escalation

Human checkpoint — escalation required. Because the request is sensitive, the case is routed to a support agent with the draft attached. The customer is not left with an unverified answer.

6. Resolution & learning

The agent resolves the case and the outcome is recorded for quality review.

Patterns feed the next evaluation cycle.

7. Edge path: order system unavailable

The order-status source cannot be reached. The assistant does not guess a status: it tells the customer plainly that the status cannot be checked right now and routes the case to a support agent with the question attached. No unverified answer is sent.

Synthetic example: order DEMO-8841 with its source down still ends with a person, not a guessed answer.

Tip: use the step buttons or your keyboard arrow keys inside this demo.

Book a call about this workflow → · Related: Managed AI Solutions · Customer support case

What changed: The customer gets a verified answer, and the case is recorded for quality review.

Illustrative demonstration — not a client case study.

Demo C: Document Automation

Outcome this demo shows: An invoice is posted only after extraction, validation, and human approval of any correction, with an audit trail linking the original, the correction, and the posting.
Baseline a real pilot would compare it against: The share of invoices where a mismatch is caught before posting rather than after, counted over an agreed period before the pilot and again during it. No values exist yet.

The problem: Invoices wait for manual checking and errors surface after posting.

An invoice arrives by email. Watch extraction, validation, and approval before anything is posted.

Synthetic example data
Invoice: INV-DEMO-1042 (invented) · Supplier: Demo Supplies Ltd (invented) · Stated total: 1,240 (illustrative currency units) · Extracted total: 1,420 — a mismatch is flagged on purpose in this example.

1. Document received

An invoice PDF arrives in a monitored inbox.

Synthetic invoice: INV-DEMO-1042.

2. Data extraction

Supplier, date, line items, and totals are extracted.

Extraction is a proposal, not a posting.

Extracted fields (synthetic): Supplier: Demo Supplies Ltd · Stated total: 1,240 · Extracted total: 1,420 · Status: mismatch — held for finance review.

3. Validation

Extracted values are checked against expected formats, totals, and known suppliers.

A total mismatch is flagged in this example.

4. Human approval

Human checkpoint — approval required. A finance reviewer corrects the flagged total and approves the document. No posting happens before approval.

5. System update

The approved data is posted to the finance system and filed with the original document.

An audit trail links the original, the correction, and the posting.

6. Edge path: finance system unavailable

The approved invoice cannot be posted because the finance system is unreachable. The approved data is held, not posted, and posted only once the system is reachable again. The audit trail records the hold, the original, and the eventual posting.

Synthetic example: INV-DEMO-1042 approved but unposted stays held — nothing is recorded as posted before it is.

Tip: use the step buttons or your keyboard arrow keys inside this demo.

Book a call about this workflow → · Related: AI Automation & Agents · Document-heavy back-office case

What changed: The corrected invoice is posted with an audit trail linking the original, the correction, and the posting.

Illustrative demonstration — not a client case study.

Demo D: Lead Scoring & Routing

Outcome this demo shows: Every enquiry has a score, a route, and a named next step — none silently disappears — and borderline enquiries are reviewed by a person, not auto-sent.
Baseline a real pilot would compare it against: The share of enquiries with a recorded score, route, and next step, and the share of borderline enquiries reviewed by a person — counted over an agreed period before the pilot and again during it. Scores in this demo are illustrative only, and no values exist yet.

The problem: Good enquiries wait behind vague ones because nobody scores them the same way.

Not every enquiry deserves the same next step. Watch a transparent score decide routing — with a person reviewing borderline cases, not an automatic send.

Synthetic example data — illustrative scoring only, not a proven model
Example weights used only in this demo: role fit 30, company fit 25, problem fit 25, timeline 20 (total 100). Enquiry: Omar, Sales Manager at Demo Retail Group (invented), wants faster quote follow-up, timeline “this quarter”. Demo score: 30 + 20 + 25 + 15 = 90 — routed as high priority in this example. A second, borderline example scores 55 and goes to human review.

1. Enquiry intake

Two synthetic enquiries arrive: one clear sales follow-up need, one vague request with missing company details.

Both are recorded before any scoring happens.

2. Transparent scoring

Each enquiry is scored against published, agreed criteria — role, company fit, problem fit, and timeline — so the score can be explained and challenged.

Scores in this demo are illustrative and recorded in this demo only; they are not conversion predictors.

Score breakdown (synthetic): Role fit 30/30 · Company fit 20/25 · Problem fit 25/25 · Timeline 15/20 = 90 (high priority). Borderline example: 55 — human review.

3. Threshold check

Demo thresholds: 75+ = high priority, 50–74 = review, below 50 = nurture / request more detail. Thresholds are agreed with the client before launch, not hidden in the tool.

Example scores: 90 (high) and 55 (review).

4. Routing

The high-priority enquiry is routed to the named sales owner with its score breakdown and source attached. The borderline enquiry is NOT auto-sent to sales.

Routing moves the record; it does not send a customer message by itself.

5. Human review of borderline cases

Human checkpoint — review required. A reviewer checks the borderline enquiry, requests the missing detail, and decides whether it qualifies. Automation proposes; a person decides.

6. Logged outcome

Scores, routing, reviewer decisions, and next actions are logged so the scoring rules can be audited and improved — and so no enquiry silently disappears.

What this demo does NOT claim: that a higher score guarantees a sale, or any improvement recorded in this demo.

7. Edge path: abusive or spam input

An enquiry is flagged as abusive or spam content. It is not scored and not routed: it is held for a person to confirm rejection, and the rejection is logged with its reason. Filtering never sends a customer message by itself.

Scores in this demo are illustrative and recorded in this demo only; a filtered enquiry receives no score.

Tip: use the step buttons or your keyboard arrow keys inside this demo.

Book a call about this workflow → · Related: AI Automation & Agents · Sales case

What changed: Every enquiry has a score, a route, and a named next step — none silently disappears.

Illustrative demonstration — not a client case study.

Demo E: Purchase Request Approval

Outcome this demo shows: A purchase order exists only because both required human approvals happened, and the request, checks, approvals, comments, and order are linked in one trail.
Baseline a real pilot would compare it against: The share of purchase orders issued with both approvals and a complete linked trail recorded before issue — counted over an agreed period before the pilot and again during it. No values exist yet.

The problem: Purchase requests stall on email approvals with no visible trail.

An employee needs new equipment. Watch policy checks and two human approvals happen before any purchase order is issued — nothing is bought automatically.

Synthetic example data
Request: PR-DEMO-77 (invented) · Requester: Lina, Support Lead (invented) · Item: 4 headsets, illustrative total 480 · Budget code: OPS-DEMO-2026 (invented) · Policy in this example: manager approval required above 250; finance approval required above 400.

1. Request submitted

Lina submits a purchase request with item, quantity, reason, budget code, and requested delivery date.

The request is a proposal, not an order.

2. Automated policy check

The workflow checks the budget code, the amount thresholds, and whether required fields are complete. Missing or out-of-policy items are flagged, not bypassed.

In this example the request passes the completeness check and triggers both approvals (above 250 and above 400).

3. Manager approval

Human checkpoint — approval required. The line manager reviews business need and budget, and approves with a comment. A rejection here stops the workflow.

4. Finance approval

Human checkpoint — approval required. Finance confirms the budget code and amount before any commitment. No purchase order is issued before this step.

5. Purchase order issued

Only after both approvals, a purchase order is generated from the approved request — with the approved amount, not a changed amount.

If either approver rejects, the requester is notified with the reason and can revise and resubmit.

Purchase order (synthetic): 4 headsets · Illustrative total 480 · Manager: approved · Finance: approved · Issued only after both approvals.

6. Audit trail

Request, policy checks, both approvals, comments, and the purchase order are linked in one trail for later review.

What this demo does NOT claim: any saving, speed improvement, or compliance certification shown in this demo.

7. Edge path: incomplete request

The request is missing its budget code. The policy check fails on completeness, so the request is returned to the requester with what is missing — before any approval is sought. Nothing is ordered, and no approver's time is used on an incomplete request.

Synthetic example: PR-DEMO-77 without a budget code goes back to Lina, not to a manager.

Tip: use the step buttons or your keyboard arrow keys inside this demo.

Book a call about this workflow → · Related: Managed AI Solutions · Operations case

What changed: The purchase order exists only because both approvals happened, and the whole trail is linked.

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