Insurance
BI modernisation and claims forecasting for a top-10 SA insurer
Month-end reporting cut from nine days to one; claims forecasting live
Read the case study →AI that ships inside regulated enterprises
Our core practice is building the mobile and desktop applications businesses run on — and AI earns its place inside them. Banks, insurers and pharmaceutical companies already trust us with their core systems; we bring the same delivery discipline to AI: production capability with governance and measurable returns, not proofs-of-concept that die in a slide deck.
Four ways enterprises put us to work
Document intelligence & workflow automation
Extract, classify and route the paperwork that clogs your operations — claims, KYC packs, invoices, contracts — with human review where the risk requires it.
Forecasting & decision support
Demand, risk and revenue models built on your existing BI estate, delivered into the dashboards your teams already use.
Customer-facing assistants
Chat and WhatsApp assistants that resolve routine queries and hand over to humans gracefully — with every conversation logged and auditable.
AI readiness & data foundations
The unglamorous prerequisite: consolidated, quality-controlled, POPIA-compliant data. We build the foundation and the models on top of it.
Governance is the product
An AI system that cannot pass your compliance review is a demo. Our delivery approach is built for regulated environments:
- Every AI engagement starts with a use-case and risk assessment — we will tell you when AI is the wrong tool.
- Personal information is handled under POPIA from the data layer up: minimisation, purpose limitation and documented lawful basis.
- Human-in-the-loop controls on any decision that affects a customer; full audit trails on model inputs and outputs.
- Models are monitored in production for drift and cost — an AI system is a system, and we support it like one.
Proof
AI & data in production
Common questions
We don't know where to start with AI. What is the first step?
A two-week AI readiness assessment: we map your data estate against high-value use-cases and return a ranked, costed shortlist — including the honest 'not yet' items.
Is our data safe to use with AI models?
We design so personal information stays inside your controlled environment, apply POPIA minimisation before any processing, and never use your data to train shared models.
What does an AI project cost?
AI engagements are phased — readiness assessment, pilot, production — and each phase is priced separately and gates the next, so you never commit the full investment upfront. The assessment returns a costed roadmap before any build decision.
Do you use OpenAI / Anthropic / local models?
We are model-agnostic: managed cloud models where speed matters, self-hosted models where data residency or cost dictates. The architecture decision is documented and yours.
Ready to find your first shippable AI use-case?
A two-week readiness assessment returns a ranked, costed shortlist — including the honest 'not yet' items.