Why insurance is the right vertical for agentic AI
Insurance operations are dominated by repeatable, multi-step workflows that span policy admin, claims, distribution, and customer servicing. Each workflow touches structured policy data, semi-structured forms, unstructured documents, and regulator-mandated controls. Generic copilots stop at suggestion; carriers need agents that can take action - open claims, allocate reserves, pre-authorise treatments, generate endorsements - within strict audit boundaries.
Reference architecture
We deploy a four-layer architecture inside the carrier estate: (1) an orchestration layer that decomposes a workflow into deterministic steps, (2) a model layer that mixes domain-tuned LLMs, classical ML, and rules, (3) an integration layer with read/write connectors to the core (PAS, claims, billing, CRM), and (4) a governance layer that captures every input, prompt, model output, tool call, and human override into an append-only audit log.
- Orchestration: workflow graphs with confidence-thresholded handoffs
- Model layer: domain-tuned LLMs + traditional ML + business rules
- Integration: connectors to Guidewire, Duck Creek, custom PAS, Salesforce FSC
- Governance: append-only event log, explainability, IRDAI / FCA / CBUAE-aligned controls
Workflows live in production today
We have taken the following workflows from pilot to production with carriers and MGAs across India, the GCC, and the UK:
- Motor FNOL - voice-first intake, fraud signals, reserve allocation in under 90 seconds
- Health pre-authorisation - clinical extraction, policy match, network checks, 82% STP
- Endorsement processing - change request to issuance with full audit trail
- Underwriting triage - submission scoring, appetite match, broker routing
- Renewals - propensity scoring, action recommendation, agent-assisted execution
Governance and the regulator question
The single most common question from CROs and Chief Compliance Officers is: 'Can you prove what the agent did and why?' Our answer is architectural. Every agent action emits a structured event; every event is signed and stored; every customer-impacting decision carries an explanation token that maps back to the model, prompt, and data version used. This is the difference between a demo and a deployment in a regulated industry.
What it takes to get to production in 4–8 weeks
Carriers that move fastest share three traits: a single named workflow owner, access to one operational dataset, and a willingness to measure against the existing KPI rather than a vendor demo. We bring the senior insurance + AI team, the accelerators, and the governance scaffolding. You bring the workflow and the data.