Data & AI services for insurers — advise, build, transform, run.
SCIKIQ teams set your data and AI strategy, build the platforms, transform underwriting, claims, the customer voice and finance, and run what we build. Our SCIKIQ Data Fabric, insurance accelerators and a supervised digital workforce of AI agents are how we deliver it faster.
Insurance data and AI programmes stall because every one starts from zero.
Policy, claims and billing systems from past acquisitions, unstructured customer data and long implementations mean value arrives late — or not at all.
1 · No governed data foundation
Policy admin, claims, billing and actuarial systems don't agree; calls, surveys and complaints are unstructured and full of personal data.
2 · Services firms rebuild from scratch
Each project re-invents pipelines, models and controls, so timelines and fees grow.
3 · Software alone doesn't change the process
Point products solve one use case and leave integration, data and adoption to the insurer.
4 · AI pilots never reach production
Proofs of concept built without data, controls and an operating model stay on the shelf.
Insurers are moving from sampling and copilots to supervised agents on every interaction.
GenAI now reads every call, complaint and survey; agents prepare the case; people decide the critical ones.
So what for an insurer: once agents read everything, the deciding capabilities are privacy by design, autonomy limits and a human owner for every claim, complaint and underwriting decision.
A platform that arrives with industry IP and the team to run it — so insurers pay for outcomes, not reinvention.
Services
Nine service lines across advise, build, transform and run — delivered by teams who know policies, claims, the customer voice and insurance regulation.
SCIKIQ Data Fabric
The governed foundation: the 4C method, 268 data sources, metadata, lineage, data quality and security.
Accelerators
Six flagship and nine solution accelerators — pre-built, configurable starting points for each service line.
Supervised digital workforce
AI agents that do the routine work end to end, with people approving exceptions and every step logged.
Nine service lines cover the lifecycle, organised the way insurers buy them.
Select a stage on the wheel, or start from your role.
Set direction and the rules the data must meet.
Data & AI StrategyMaturity assessment, AI operating model, business case Data Governance, Privacy & Regulatory DataCDO set-up, PII controls, lineage, AI governanceEngineer the platforms and the AI that runs on them.
Data Engineering & Platform ModernisationPolicy, claims & billing data, lakehouse, cloud AI & Agentic EngineeringGenAI, document AI, supervised agent squadsChange how an insurance function works, end to end.
Underwriting & PricingSubmission intake, guideline checks, pricing data Claims & FraudFNOL, claim files, SIU case preparation Distribution, Customer & Voice of the CustomerCalls, complaints, surveys, partners Finance, Actuarial & Regulatory ReportingIFRS 17, solvency, reinsurance, closeKeep it healthy and improving after go-live.
Managed ServicesDataOps, MLOps & AgentOps under SLAsEvery service line runs on the same framework, accelerators and digital workforce. All services in detail →
A repeatable 4C method turns raw insurance data into production-ready capabilities in 30–45 days.
- Step 1 · Week 1–2Connect268 data sources to policy admin, claims, billing, GL/ERP, contact-centre and survey platforms, actuarial systems and documents; batch and streaming.
- Step 2 · Week 2–4CurateStandardise, cleanse and enrich: MDM, de-duplication and data quality rules.
- Step 3 · Week 3–5ContextualizeMetadata catalog, critical data elements, multi-hop lineage, ownership and policy.
- Step 4 · Week 4–6ConsumeData products, APIs, BI, NLQ GenAI studio and the agents behind each accelerator.
Twelve capability layers give every insurer one blueprint — and one way to measure progress.
The same layers we build and score in the maturity assessment. Hover a layer to see what it does.
Runs in your cloud tenancy or on-prem. Code, ontology and agents are handed over — you own what we build. Explore the blueprint →
MIT CISR found enterprises at stages 3–4 perform well above their industry average financially, while those at stages 1–2 perform below it. Source
Insurance and cross-industry accelerators mean no service line starts from a blank page.
Insurance GenAI accelerators, plus cross-industry accelerators configured to insurance data. Hover or tap a tile.
Distribution, Customer & VoC Customer
Claims & Fraud Claims
Data Governance CDO
Finance, Actuarial & Regulatory CFO
26 agent roles across eight insurance domains — your people supervise the exceptions.
Agents prepare submissions, claim files, complaint triage, fraud referrals and reconciliations. Underwriting, coverage, settlement and SIU decisions stay with people.
Squads are designs from our AI & Agentic Engineering practice.
Agents plan, call tools and gather evidence. Policy decides what goes straight through; a named owner approves everything else; a kill switch halts all agents.
An agent does the legwork on every case; a person makes the call when policy says so.
Replay: a complaint about an approved claim that hasn't been paid. The Complaints Triage Agent works it.
- 1Source systemsA complaint arrives on the partner feed: two claims were approved three weeks ago and still haven't been paid.
- 2Data fabricThe record lands in the governed complaints pipeline; a near-real-time trigger fires for potentially critical cases.
- 3PII guardPolicy number, email and phone are detected and masked before any text reaches the model.
- 4ContextThe complaint is joined to the policy, the claim status and the customer's recent calls — only what this analyst may see.
- 5AgentCalls tools through the secure AI gateway:
classify_scopescore_frustrationassign_impact_level; proposes L1 · High with the phrases that drove it. - 6PolicyKill switch, autonomy level and confidence are checked. L0 and L1 complaints can never be closed by an agent: this one goes to a person today.
- 7Human gateThe complaints analyst reviews the evidence on one screen and confirms, raises or lowers the level.
- 8ActionThe case is escalated to claims operations with the claim numbers and a same-day response target.
- 9AuditEvery step, tool call and decision is recorded; the analyst's override, if any, feeds the next model evaluation.
Autonomy is set per agent and raised only on evidence — inside hard guardrails.
Confidence threshold
Straight-through only if confidence and evidence checks pass; set per agent with your risk team.
Decision limits
Coverage, settlement, payments, denials and underwriting decisions always go to a person.
Named human owner
Approves, edits or rejects every exception; L0 and L1 complaints and SIU referrals always need a person.
Privacy & logging
Personal data masked before any model call; every plan, tool call and decision logged; overrides feed evaluation.
Kill switch
One control halts every agent at once.
What could a supervised agent squad free up in your operation?
Move the sliders to your own volumes. Agents work the cases; people review only the exceptions.
Estimate only, not a quote or a SCIKIQ result. Manual hours = cases × minutes ÷ 60. Supervised hours = cases × (1 − STP share) × review minutes ÷ 60. Hours saved = manual − supervised. FTE = hours saved ÷ 150. Cost saved = hours saved × cost per hour. Supervisor capacity = 150 h × 60 ÷ ((1 − STP share) × review minutes). Excludes platform and run costs.
Faster than services alone, a better fit than software alone.
How the SCIKIQ model compares with the usual ways insurers deliver data, AI and agentic automation.
| Criterion | Big-4 / SIservices only | Point productssoftware only | In-house build | SCIKIQplatform + accelerators + delivery |
|---|---|---|---|---|
| Time to production value | 6–12 months | 3–6 months + integration | 12–18 months | 30–45 days |
| Insurance data models & domain depth | Generic methods | One use case | Build | Pre-built |
| Reusable data foundation | Rebuilt per project | Vendor-specific | Build | SCIKIQ Data Fabric |
| Ready-made accelerators | Varies | Single product | None | Insurance + cross-industry |
| Supervised AI agents in operations | Pilots / PoCs | Copilot features | Build & govern | Agent squads with guardrails & AgentOps |
| Process change & adoption | Yes | Left to insurer | Partial | Yes |
| Run & continuous improvement | Separate contract | Product support | Internal team | Managed services |
| Total cost of ownership | High | Medium–High | Very High | Low |
Big-4 / SI (6–12 months), point products (3–6 months + integration) and in-house builds (12–18 months) are compared on larger screens.
Four phases, each ending with something you keep — and three ways to buy it.
Discovery & Planning
You receive a maturity assessment and target blueprint, with a prioritised roadmap.
Gate: pilot scope signed offAnalysis & Design
Target-state design and ontology, mapped to your controls; agent autonomy agreed with claims, underwriting and compliance.
Gate: design authorityBuild & Deploy
Landing zone as code, pipelines, accelerators and agents configured and tested on real data.
Gate: go-live readinessSupport & Embed
Runbooks, evaluation suites and knowledge transfer to a trained team.
Gate: handover sign-offStaff Augmentation
Architects, data and AI engineers embedded in your teams, under your delivery lead.
Project Delivery
Outcome-based delivery of an accelerator or platform build by a SCIKIQ pod, with phase gates.
Managed Services
We run and improve the platform, models and agents — DataOps, MLOps and AgentOps under SLAs.
A 30–45 day accelerator pilot proves value on one use case before you commit to scale.
Fixed scope, fixed timeline, agreed success criteria — and a scale-up business case at the end.
One accelerator & its agent squad
Typically Call Transcript Intelligence or the Complaints Triage Engine, agents starting at “act with approval”.
2–3 source systems
e.g. call transcripts or a complaints feed, plus the policy and claims systems.
One business unit
Named business owner and SMEs for rules and UAT.
SCIKIQ pod
Engagement lead, data engineer, domain SME, AI / agent engineer.
Success-criteria targets are agreed in week 1.
Three steps take us from this conversation to value in production.
Start small with one accelerator, prove it, then scale on the same framework.
Scoping workshop
Walk through your data landscape and pain points, pick the pilot use case and agree success criteria.
1–2 weeksAccelerator pilot
Deploy one accelerator and its agent squad on the framework against live data, and measure the result.
30–45 daysScale & embed
Roll out across business units and further accelerators through project delivery or managed services.
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