One-size-fits-all offers
The same price and coverage are shown to customers with different needs, intent and risk context.
Optimise · AI optimisation and decision engine
Make every protection offer more relevant.
Sherpa+Lens analyses live customer, journey and portfolio signals to recommend the right eligible price, coverage and offer within approved product boundaries.
The operating problem
Why the conventional approach does not scale.
Insurance complexity compounds when products, decisions and operations are managed as separate systems.
Low attachment
Relevant protection is often offered at the wrong moment or without the coverage configuration that earns trust.
Static pricing
Approved flexibility exists in the product but cannot be applied intelligently in the live journey.
Unused journey signals
Behaviour, purchase context and historical performance sit outside the insurance decision.
Slow experimentation
Teams wait for manual analyses and broad campaigns before learning what improves conversion and retention.
Opaque decisions
Black-box recommendations are difficult to govern, explain or roll back.
What Sherpa+Lens provides
Infrastructure built around insurance reality.
Each capability is designed to work with the other layers of the Gangkhar platform and the systems already in place.
Live context analysis
Read customer attributes, purchase context, journey behaviour and programme performance at the decision point.
Real-time segmentation
Group customers and moments according to relevant, permitted signals rather than broad static categories.
Pricing recommendation
Select the most suitable price from the approved product range without changing the underlying authority.
Coverage optimisation
Recommend eligible limits, benefits, deductibles and upgrade paths that fit the customer context.
Continuous learning
Measure quote, purchase, renewal and portfolio outcomes so recommendations improve with evidence.
Decision observability
Expose versions, inputs, outcomes and performance in dashboards built for business and governance teams.
Decision loop
Observe, decide, measure and improve within approved limits.
Observe
Capture the permitted signals available in the current journey and portfolio.
Segment
Identify the relevant customer and moment without interrupting the experience.
Recommend
Return the best eligible price, coverage or upgrade from the configured product.
Measure
Track conversion, attachment, renewal, collections and portfolio quality.
Learn
Improve future recommendations while retaining versions, controls and rollback.
Governance
Control is part of the architecture.
Automation only earns its place in insurance when authority, evidence and intervention are designed into the operating model.
Approved decision space
Lens recommends only among products, prices and terms authorised in Sherpa+.
Explainable evidence
Inputs, model versions, recommendations and outcomes remain observable and auditable.
Human override
Business and underwriting teams retain approval, intervention and rollback controls.
Clear product boundary
Lens optimises live offers; it does not distribute, message customers or operate policies on its own.
Operating outcomes
Built to create measurable business value.
Conversion improvement
Each programme is measured against its own baseline; results depend on the product, journey, market and approved decision space.
Decision at relevance
Recommendations arrive inside the customer journey, when protection is being considered.
Continuous feedback
The programme learns from measurable outcomes rather than isolated campaign reviews.
One connected platform
Activate. Optimise. Scale.
Sherpa+ holds the product truth. Sherpa+Lens improves each eligible decision. Sherpa+Mesh executes governed work across the policy lifecycle.
