01
instead of one-size recommendationsRecommendation & personalisation
Transparent drivers and eligibility rules you can audit.
Customer Experience & Service
Personalise, support, and root-cause — at scale you can trust.
Insight for customer-facing and service teams — without unmanaged AI risk.
Which tickets drive most escalations — and how should we route them?
15% of tickets drive 60% of escalations — auto-route billing disputes to finance first.
1 Ticket taxonomy × escalation log · service exceptions
Projects we deliver
Engagements in this function — not a catalogue
02
instead of survey-only insightConsumer insights
Behaviour and feedback into cited insight — challenge pet theories.
03
instead of unmanaged LLM chatbotsLLM support chatbot
Grounded in your policies, with escalation rules and continuous eval.
04
instead of manual triage pilesTicket routing
Route and prioritise with evidence — surface systemic issues behind the queue.
05
instead of vague “bad data”Data-quality root-cause
Cite which systems and definitions conflict — and why metrics disagree.
Common questions
Answers to what people actually ask
How does an AI support chatbot stay grounded in company policy?
A grounded LLM support chatbot answers only from your actual policies and documentation, with escalation rules for edge cases and continuous evaluation against known-good answers, instead of an unmanaged general-purpose chatbot that can hallucinate.
How can AI improve customer service ticket routing?
AI-based ticket routing analyzes ticket content and escalation history to route and prioritize tickets by evidence, often revealing that a small share of tickets drives most escalations.
How do you find the root cause of conflicting data quality issues?
Data-quality root-cause analysis traces which systems and metric definitions conflict and cites exactly why two reports disagree, replacing vague 'bad data' explanations with a specific, auditable answer.
Customer Experience & Service
Tell us the decision. We’ll map the project.
A short call on the question that matters — and the right first step.