Proof of Approach

AI that delivers business outcomes.

Detailed example use cases — the same structure we apply in every engagement: challenge, solution, implementation, outcome.

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Note: these are illustrative use cases, not reported client results. We don't publish performance claims until we can stand behind them — yours could be the first.

E-commerceExample Use Case 01

After-hours shoppers stopped waiting until morning

Challenge

A growing D2C store receives most support questions outside business hours — order status, returns, product questions. The morning queue means slow answers and abandoned carts.

Solution

An AI chat agent answers product and order questions instantly on web and WhatsApp, pulls live order status, and creates tickets only when a human is genuinely needed.

Implementation
  1. 1.Connect store and order data
  2. 2.Train the agent on policies, FAQs and catalogue
  3. 3.Launch on web + WhatsApp with human handoff
Outcome

Every visitor gets an instant answer at any hour. The support team starts the day with a prioritized queue instead of a backlog. (Illustrative example — not a client result.)

Real EstateExample Use Case 02

Every property enquiry gets a response in seconds

Challenge

A brokerage loses enquiries that arrive during viewings and weekends. By the time agents call back, prospects have booked with competitors.

Solution

An AI agent responds instantly to every enquiry, qualifies budget and timeline, answers listing questions and books viewings directly into agent calendars.

Implementation
  1. 1.Connect listings and agent calendars
  2. 2.Define qualification questions and routing rules
  3. 3.Deploy on website, portals and WhatsApp
Outcome

No enquiry waits for office hours. Agents spend their time on viewings and negotiations, not first-touch follow-up. (Illustrative example — not a client result.)

EducationExample Use Case 03

Admissions teams focus on the students who need them

Challenge

During intake season an education provider is flooded with repetitive admissions questions, while high-intent applicants wait for callbacks.

Solution

An AI agent answers programme, fee and eligibility questions, qualifies applicants, and schedules counselling sessions for those ready to apply.

Implementation
  1. 1.Load programme and admissions knowledge
  2. 2.Configure qualification and scoring
  3. 3.Integrate scheduling and CRM
Outcome

Applicants get answers immediately; counsellors get a calendar full of qualified conversations. (Illustrative example — not a client result.)

HealthcareExample Use Case 04

Front desks stop playing phone tag

Challenge

A multi-location clinic group loses bookings to voicemail, and staff spend hours confirming, rescheduling and reminding patients.

Solution

AI voice and chat agents book, confirm, reschedule and remind — reading and writing real availability, with staff escalation for clinical questions.

Implementation
  1. 1.Connect scheduling system
  2. 2.Define booking, reschedule and reminder policies
  3. 3.Enable voice + chat with staff handoff
Outcome

Patients book and change appointments any time. The front desk focuses on the people standing in front of them. (Illustrative example — not a client result.)

SaaSExample Use Case 05

Trials get a guide, not a drip campaign

Challenge

A B2B SaaS team watches trial users stall without adopting key features, while CSMs can only reach a fraction of accounts.

Solution

An AI agent triggers in-product check-ins based on usage, answers setup questions and books CSM time for accounts showing expansion signals.

Implementation
  1. 1.Stream product usage events
  2. 2.Define activation milestones and plays
  3. 3.Escalation rules to CSM team
Outcome

Every trial account gets guidance at the moment it matters, and CSMs spend time where expansion is likely. (Illustrative example — not a client result.)

Financial ServicesExample Use Case 06

Compliance-friendly follow-up, every time

Challenge

A lending team's follow-up cadence depends on individual discipline. Some prospects get five calls; others fall through the cracks.

Solution

A workflow agent runs a consistent, auditable follow-up process — reminders, document requests and status updates — with human approval at defined steps.

Implementation
  1. 1.Map the follow-up process and rules
  2. 2.Connect CRM and communication channels
  3. 3.Add approval checkpoints and audit logs
Outcome

Every application follows the same compliant path, and nothing depends on memory. (Illustrative example — not a client result.)

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