Services

Nine services, mapped to the insurance value chain.

Insurers don't buy "data and AI" — they buy sharper pricing, faster underwriting decisions, claims paid on time, less fraud, complaints found and fixed the same day and an IFRS 17 close that finishes on time. Below, each DaasLabs service line is mapped to the domains where it does that work, with the use cases, the KPIs it moves and the accelerators you can open today.

See the value-chain map How we add value, by domain Find your service by role
Chapter 2 · Services on the value chain

Where each service line works on the insurance value chain

Read across a row to see where a service line leads and where it supports. Read down a column to see the team an insurer gets in that domain. Select any domain to see use cases, the KPIs we help move and the working accelerators.

9
Service lines
8
Value-chain domains
19
Lead roles across the map
34
Supporting roles across the map
Leads the work Supports Hover a dot for detail · select a domain to explore it
01 · Advise
Data & AI StrategyStrategy
Data Governance, Privacy & Regulatory DataGovernance
02 · Build
Data Engineering & Platform ModernisationData platform
AI & Agentic EngineeringAI & agents
03 · Transform
Underwriting & PricingUnderwriting
Claims & FraudClaims & fraud
Distribution, Customer & Voice of the CustomerCustomer & VoC
Finance, Actuarial & Regulatory ReportingFinance
04 · Run
Managed Services: DataOps, MLOps & AgentOpsManaged
Service lines engaged 6 5 6 7 7 8 7 7

The mapping shows where each service line typically leads or supports; every engagement is scoped to the insurer. The value chain itself is explained on the overview.

How we add value

Domain by domain: from data to a measurable outcome

Each domain follows the same path — source data, a governed data product, AI and agents, an outcome the business measures. KPIs are the measures we help you move and track; we agree targets with you, we don't promise them in advance.

Domain 1 of 8

Product, pricing & actuarial

Loss trends, inflation and catastrophe exposure move faster than annual rate reviews, and actuaries spend more time assembling data than modelling.

Data
Policy, claims, exposure & competitor rates
Data product
Pricing & experience data product
AI & agents
Loss-cost & elasticity models
Outcome
Sharper rates, faster filings

Insurance use cases

  • Loss-cost and frequency/severity modelling on governed data
  • Price-elasticity and retention-aware rate scenarios
  • Rate-filing documentation drafted from the model evidence
  • Product performance monitoring by segment and channel

KPIs we help you move

Loss ratioRate adequacyTime to file a rate changeRetention at renewal

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working COMPASS NARRATOR
Domain 2 of 8

Distribution, agents & partners

Agents, brokers, bank partners, retailers and embedded journeys each report differently, and partner programmes demand their own insight.

Data
Sales, quotes, commissions & partner feedback
Data product
Channel & partner data product
AI & agents
Conversion & producer models
Outcome
More policies bound per channel

Insurance use cases

  • Quote-to-bind conversion analytics by channel and partner
  • Partner-programme insight from surveys and feedback
  • Agent performance and next-best-action for producers
  • Commission accuracy and leakage checks

KPIs we help you move

Quote-to-bind rateNew business premium by channelProducer productivityCommission leakage

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Survey Verbatim Tagging CATALYST
Domain 3 of 8

Underwriting & risk selection

Submissions arrive as emails, PDFs and spreadsheets; underwriters re-key data, chase information and check guidelines by hand.

Data
Submissions, evidence & guidelines
Data product
Structured submission data product
AI & agents
Intake, appetite & triage agents
Outcome
Faster quotes, on-appetite portfolio

Insurance use cases

  • Submission intake: document AI extracts and validates the risk data
  • Appetite and guideline checks with the reasoning shown
  • Risk scoring and referral triage for the underwriter
  • Portfolio accumulation and appetite monitoring

KPIs we help you move

Submission-to-quote timeUnderwriter capacityHit ratioOut-of-appetite exposure

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Run an agent live
Domain 4 of 8

Policy administration & servicing

Policies live on several admin systems; endorsements, renewals and cancellations run on hand-offs, and billing errors turn into calls.

Data
Admin systems, billing & service requests
Data product
Single policy data product
AI & agents
Servicing bots & renewal models
Outcome
Fewer errors, more renewals

Insurance use cases

  • Renewal and cancellation risk with the reasons explained
  • Servicing requests classified and routed automatically
  • Policy-document generation and checks with GenAI
  • Billing-exception and premium-leakage detection

KPIs we help you move

Servicing turnaroundCancellation ratePremium leakageStraight-through endorsements

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Revenue Assurance RPA Automation
Domain 5 of 8

Customer experience, complaints & retention

Millions of calls, surveys and complaints hold the reasons customers stay or leave, but they are unstructured, multilingual and full of personal data.

Data
Calls, surveys, complaints & chat
Data product
PII-safe interaction data product
AI & agents
Intent, sentiment & triage GenAI
Outcome
Complaints fixed, customers kept

Insurance use cases

  • Intent, sentiment and complaint detection on every call
  • Complaint triage by impact (L0–L3) with same-day escalation
  • Multilingual survey themes with regional nuance
  • Agent quality scoring and call summarisation

KPIs we help you move

Complaint escalation timeRepeat-call rateNet promoter scoreAgent quality score

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Call Transcript Intelligence Complaints Triage Engine Multilingual Survey Intelligence
Domain 6 of 8

Claims

FNOL arrives through every channel, adjusters juggle documents and estimates, and payment delays turn approved claims into complaints.

Data
FNOL, documents, estimates & payments
Data product
Claim timeline data product
AI & agents
Triage, document & settlement agents
Outcome
Claims paid faster and right

Insurance use cases

  • FNOL intake and triage: simple claims straight through
  • Document AI on estimates, invoices and medical bills
  • Claim-status signals from calls and complaints
  • Reserve and settlement recommendations with the rationale

KPIs we help you move

Claim cycle timeStraight-through claimsLeakage per claimClaims complaints

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Call Transcript Intelligence Dispute & Chargeback
Domain 7 of 8

Fraud & special investigations

Organised rings, inflated claims and application fraud hide across policies, claims and third parties; investigators spend their time gathering evidence.

Data
Claims, applications & third parties
Data product
Entity-resolved fraud data product
AI & agents
Scoring, network & SIU agents
Outcome
Less fraud paid, faster cases

Insurance use cases

  • Claim and application fraud scoring at FNOL and before payment
  • Network analysis linking claimants, providers and repairers
  • SIU case preparation with the evidence assembled
  • Recovery and subrogation opportunity detection

KPIs we help you move

Fraud detected before paymentSIU referral hit rateInvestigation cycle timeRecoveries

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Fraud & Authorization Agent governance
Domain 8 of 8

Finance, reserving, reinsurance & regulatory

IFRS 17 / Ind AS 117, Solvency II and IRDAI returns demand granular, traceable data; reserving, reinsurance and reconciliations still lean on spreadsheets.

Data
GL, premiums, claims, treaties & triangles
Data product
Governed finance & actuarial data product
AI & agents
Matching, reserving & commentary agents
Outcome
A faster, auditable close

Insurance use cases

  • Premium, claims and bank reconciliations with breaks explained
  • Reserving and IFRS 17 measurement data prepared with lineage
  • Reinsurance recovery and bordereaux checks
  • AI commentary for results, the board pack and regulatory returns

KPIs we help you move

Days to closeAuto-match rateReinsurance recoveries outstandingRegulatory return preparation time

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working CLARION COMPASS NARRATOR SOX Compliance
Find your service

Start from your role

Each service line has a clear owner on the client side. Pick yours to jump to the services most teams like yours start with.

CUO
Chief Underwriting Officer & pricing
CCO
Chief Claims Officer & SIU
CFO
CFO & Chief Actuary
CDO
Chief Data Officer
CIO
Chief Information Officer
Distribution
Heads of distribution, customer & complaints
01

Advise

Set direction, make the business case and put the rules in place that data and AI must meet.

Advise · Service line

Data & AI Strategy

ForCDOCIOCEO office
On the value chainProduct & pricingDistributionUnderwritingPolicy & servicingCustomer & complaintsClaimsFraud & SIUFinance & regulatory

The client problem

AI pilots multiply across pricing, claims and the contact centre, but few reach production. There is no shared, evidence-based view of where the insurer stands on data and AI, which use cases pay back, or who owns them.

Outcomes

  • A maturity baseline across 12 capability layers and five stages, scored on evidence rather than opinion
  • A prioritised roadmap of use cases across the value chain, with a business case for each
  • An AI operating model: ownership, funding, delivery and controls

What we do

  • Data & AI maturity assessment
  • AI strategy & use-case prioritisation
  • AI operating model & centre of excellence design
  • Business case & value tracking

Typical engagements

  • AssessmentMaturity assessment and roadmap
  • Pilot · 30-45 daysProve the top-ranked use case on your data
  • BuildRoadmap delivered through our Build and Transform services
  • Managed runValue tracking and roadmap refresh

Delivered with

Maturity assessment Value calculator Data Fabric Framework
Advise · Service line

Data Governance, Privacy & Regulatory Data

ForCDOChief Compliance OfficerDPO
On the value chainUnderwritingPolicy & servicingCustomer & complaintsClaimsFraud & SIUFinance & regulatory

The client problem

Policyholder data is spread across admin, claims and contact-centre systems under the DPDP Act, GDPR, CCPA and GLBA. IFRS 17 (Ind AS 117 in India), Solvency II, IRDAI returns and complaint reporting want proof of where a number came from, and GenAI on customer conversations adds a new privacy risk.

Outcomes

  • A working CDO function and data operating model
  • PII detected and masked before any model sees it, with the controls documented
  • Critical data elements with owners, lineage and data quality for regulatory returns, plus AI and agent governance
Delivered · Fortune 500 insurerMicrosoft Presidio and spaCy detect SSNs, phones, emails, addresses and account numbers in transcripts, surveys and complaints and redact them before any LLM call — designed for CCPA, GDPR and GLBA.

What we do

  • CDO set-up & operating model
  • PII detection, masking & consent controls
  • Lineage for IFRS 17 / Ind AS 117, Solvency II, IRDAI & complaint reporting
  • Metadata catalogue & data quality rules
  • AI & agent governance, including bias testing

Typical engagements

  • AssessmentGovernance, privacy and lineage gap review
  • Pilot · 30-45 daysCatalogue, lineage and DQ for one regulatory return
  • BuildCDO function, metadata repository and controls, enterprise-wide
  • Managed runOngoing DQ monitoring and catalogue stewardship

Delivered with

Data Governance Hub PII Guard (case study) Agent governance
02

Build

Engineer the data platforms and the AI that runs on them, with governance built in.

Build · Service line

Data Engineering & Platform Modernisation

ForCIOCDO
On the value chainProduct & pricingDistributionUnderwritingPolicy & servicingCustomer & complaintsClaimsFraud & SIUFinance & regulatory

The client problem

Policies, claims and billing sit on several admin systems from past acquisitions, and the richest data is in transcripts, PDFs and emails. Every new report or model starts with another bespoke extract.

Outcomes

  • One governed, cloud-agnostic data platform on Azure, AWS or hybrid
  • Batch and near-real-time pipelines with lineage from source, including unstructured text
  • Repeatable environments deployed with Infrastructure as Code
Delivered · Fortune 500 insurerAzure Data Factory, Databricks and Delta Lake pipelines feeding a SQL Server EDW and Power BI, with daily batch processing and near-real-time triggers for critical complaint escalation.

What we do

  • Pipelines, batch & streaming
  • Insurance data models: policy, claim, party, coverage
  • Lakehouse / warehouse on Azure or AWS
  • Unstructured-data ingestion: transcripts, documents, surveys
  • Infrastructure as Code & legacy admin-system modernisation

Typical engagements

  • AssessmentData estate and target-architecture review
  • Pilot · 30-45 daysConnect and curate priority sources end to end
  • BuildPlatform build in sprints, tested with real data
  • Managed runDataOps under agreed SLAs

Delivered with

Data Fabric Framework 167+ pre-built connectors Control Tower
Build · Service line

AI & Agentic Engineering

ForCIOCOOChief Claims Officer
On the value chainProduct & pricingDistributionUnderwritingPolicy & servicingCustomer & complaintsClaimsFraud & SIU

The client problem

GenAI and agent pilots stall at the controls review: no autonomy limits, no audit trail, no named owner for the exceptions — and decisions that touch policyholders must be fair and explained.

Outcomes

  • Supervised agent squads in production, with autonomy and guardrails set per agent
  • GenAI that turns calls, surveys, complaints and documents into structured data
  • People review only the exceptions, with the agent's draft and evidence in front of them
Delivered · Fortune 500 insurerAzure OpenAI pipelines classify intent, sentiment and complaint level on every call transcript at an average of 2.3s each, with deterministic settings and structured JSON output.

What we do

  • Agent design: roles, squads & autonomy levels
  • GenAI text & document extraction
  • Multilingual NLP with regional prompt tuning
  • Human-in-the-loop review interfaces
  • Evaluation & guardrails: policy, limits, kill switch

Typical engagements

  • AssessmentAgent opportunity and controls review
  • Pilot · 30-45 daysOne agent squad working real cases under your controls
  • BuildSquads integrated with your systems and scaled
  • Managed runAgentOps: monitoring, overrides, drift

Delivered with

Call Transcript Intelligence Run an agent live Governance & controls
03

Transform

Domain practices that change how an insurance function works, end to end, with our accelerators as the starting point.

Transform · Service line

Underwriting & Pricing

ForChief Underwriting OfficerChief ActuaryPricing
On the value chainProduct & pricingDistributionUnderwritingFraud & SIUFinance & regulatory

The client problem

Submissions arrive as emails, PDFs and spreadsheets; underwriters re-key data and check guidelines by hand. Loss costs move faster than rate reviews, and pricing evidence is assembled by hand for every filing.

Outcomes

  • Submissions structured and checked against appetite before an underwriter opens them
  • Referrals that arrive with the evidence and a draft decision
  • Faster, traceable rate evidence from governed policy and claims data

What we do

  • Submission intake & document AI
  • Appetite & guideline checks
  • Risk scoring & referral triage
  • Loss-cost & price-elasticity modelling
  • Rate-filing evidence & documentation

Typical engagements

  • AssessmentUnderwriting and pricing diagnostic
  • Pilot · 30-45 daysSubmission intake for one line of business
  • BuildUnderwriting workbench and pricing data products
  • Managed runModel monitoring and MLOps

Delivered with

Data Fabric Framework COMPASS Run an agent live
Transform · Service line

Claims & Fraud

ForChief Claims OfficerSIUClaims operations
On the value chainPolicy & servicingCustomer & complaintsClaimsFraud & SIUFinance & regulatory

The client problem

First notice of loss arrives through every channel, adjusters juggle documents and estimates, and payment delays turn approved claims into complaints. Fraud hides in the volume and investigators spend their time gathering evidence.

Outcomes

  • Simple claims straight through; complex ones routed with the file assembled
  • Claim-status problems surfaced from calls and complaints before they escalate
  • SIU referrals that arrive with the evidence and the links between parties
Delivered · Fortune 500 insurerClaims detection on every call transcript: claim IDs, status and payment issues extracted and summarised, so delays flagged by customers reach the claims team the same day.

What we do

  • FNOL intake & triage
  • Claims document AI: estimates, invoices, medical bills
  • Claims fraud scoring & network analysis
  • SIU case preparation
  • Recovery & subrogation detection

Typical engagements

  • AssessmentClaims and fraud operations review
  • Pilot · 30-45 daysFNOL triage or a fraud-scoring agent on one line
  • BuildClaims and SIU agent squads
  • Managed runAgentOps with evaluation and override review

Delivered with

Call Transcript Intelligence Fraud & Authorization Dispute & Chargeback
Transform · Service line

Distribution, Customer & Voice of the Customer

ForChief Customer OfficerHead of distributionComplaints & compliance
On the value chainProduct & pricingDistributionPolicy & servicingCustomer & complaintsClaims
100%
Of call transcripts analysed, not a sample
4K+
Complaint records triaged automatically every day
2.3s
Average GenAI processing time per call transcript
5
Markets unified in one survey view, across six languages

Scale of the build: GenAI solutions delivered for a Fortune 500 specialty insurer. These figures describe the build, not measured business outcomes. Read the case study

The client problem

Calls, surveys and complaints arrive by the million in many languages and are full of personal data. Keyword tools miss nuance, sampling misses the complaint that matters, and partner programmes each want their own insight.

Outcomes

  • Every interaction analysed for intent, sentiment and complaint severity
  • Critical complaints escalated the same day, with analysts validating the model
  • One view of customer and partner feedback across markets and languages
Delivered · Fortune 500 insurerComplaints Triage Engine: 4,000+ complaints a day classified by scope and impact (L0–L3), with L0/L1 escalated the same day and a human-in-the-loop override interface.

What we do

  • Call transcript intelligence & agent quality scoring
  • Complaints triage & regulatory escalation
  • Multilingual survey & verbatim analytics
  • Partner-programme insight & dashboards
  • Retention signals & next-best-action

Typical engagements

  • AssessmentVoice-of-the-customer and complaints review
  • Pilot · 30-45 daysOne accelerator on a live call, survey or complaints feed
  • BuildEnterprise VoC platform across channels and markets
  • Managed runPrompt, model and dashboard operations under SLA

Delivered with

Call Transcript Intelligence Complaints Triage Engine Multilingual Survey Intelligence CATALYST
Transform · Service line

Finance, Actuarial & Regulatory Reporting

ForCFOChief ActuaryFinancial controllers
On the value chainProduct & pricingUnderwritingClaimsFinance & regulatory

The client problem

IFRS 17 (Ind AS 117 for Indian insurers, as IRDAI moves to it), Solvency II and IRDAI returns need granular, traceable data and faster closes. Reserving, reinsurance recoveries and premium and claims reconciliations still lean on spreadsheets, manual matching and hand-written commentary.

Outcomes

  • A faster, cleaner close with automated premium, claims and bank reconciliation
  • Reserving and IFRS 17 measurement data prepared with lineage
  • Results and regulatory-return commentary drafted by AI and signed off by people

What we do

  • Premium, claims & bank reconciliation
  • IFRS 17 / Ind AS 117, Solvency II & IRDAI data preparation
  • Reserving & reinsurance analytics
  • FP&A, forecasting & board pack
  • SOX controls & regulatory reporting

Typical engagements

  • AssessmentClose, reserving and reporting diagnostic
  • Pilot · 30-45 daysOne accelerator on a live reconciliation or reporting stream
  • BuildFinance and actuarial data products and rollout
  • Managed runRecon and close squads run under SLA

Delivered with

CLARION COMPASS NARRATOR Revenue Assurance SOX Compliance
04

Run

Keep platforms, models and agents healthy and improving after go-live.

Run · Service line

Managed Services: DataOps, MLOps & AgentOps

ForCIOCOO
On the value chainPolicy & servicingCustomer & complaintsClaimsFraud & SIUFinance & regulatory

The client problem

After go-live, pipelines break, prompts and models drift as language and products change, and agents need someone watching overrides, limits and evaluation results every day.

Outcomes

  • Platforms, pipelines, models and agents run under agreed SLAs
  • Continuous improvement driven by analyst overrides and evaluation data
  • Your teams freed from L2/L3 support

What we do

  • Run & L2/L3 support
  • DataOps
  • MLOps & prompt operations
  • AgentOps: logs, overrides, drift, kill switch
  • Continuous improvement

Typical engagements

  • AssessmentRun-readiness and support model review
  • Pilot · 30-45 daysHypercare for a newly live capability
  • BuildMonitoring, runbooks and SLAs
  • Managed runOngoing service under agreed SLAs

Delivered with

Governance & controls Data Fabric Framework
Our assets

What makes our services faster

Every engagement starts from DaasLabs IP rather than a blank page. These assets are how we deliver — they come with the service.

1
Data Fabric Framework

The governed foundation every engagement runs on: the 4C method (Connect, Curate, Contextualize, Consume), 167+ pre-built connectors, and governance, lineage, data quality, PII detection and masking, an AI/agent layer and security built in — cloud-agnostic on Azure, AWS or hybrid.

Explore the framework
2
Accelerators

Four insurance GenAI accelerators from our work for a Fortune 500 specialty insurer — Call Transcript Intelligence, the Complaints Triage Engine, Multilingual Survey Intelligence and Survey Verbatim Tagging — plus cross-industry accelerators such as CLARION, COMPASS and NARRATOR, tailored to your rules, data and controls.

Accelerators by service line The case study
3
Supervised digital workforce

AI agents that plan, call tools and gather evidence on the governed data. Policy decides what goes straight through; people approve everything else; every step is logged and a kill switch halts all agents.

How it works Watch one work
How we engage

From a business outcome to measured value

Every engagement starts from the business outcome, not the technology. We frame it through the same business lens each time, then deliver in five phase-gated stages. Most insurers start with a discovery and value case for one value-chain domain, then scale to the next on the same foundation.

How we frame an engagement

  1. 1Business outcome & KPI
  2. 2Value-chain domain
  3. 3Decisions
  4. 4Data
  5. 5AI & agents
  6. 6Governance & adoption
  7. 7Measured value

Delivery phases

Phase 1
Discover & value case

Outcome, domain and KPIs agreed; data and process assessment; baseline and business case.

Phase 2
Design

Decisions, data products, models, agents and controls designed for the chosen domain.

Phase 3
Build & integrate

Sprint delivery on the Data Fabric Framework, integrated with policy administration, claims, billing, CRM, contact-centre and finance systems; tested on real data.

Phase 4
Deploy & adopt

Go-live, people and process change, agent autonomy limits agreed with underwriting, claims, compliance and finance; value tracked against the baseline.

Phase 5
Run & scale

Managed service — DataOps, MLOps and AgentOps under SLA — and the next domain on the same foundation.

Each phase ends with a gate signed off by your steering group; a pilot in one domain typically reaches a production-ready capability in 30-45 days. The Data Fabric Framework we build on

Engagement models

Staff Augmentation

Data engineers, architects, analysts and AI specialists embedded in your teams, under your delivery lead.

Project Delivery

Outcome-based delivery of an accelerator or platform build, owned end-to-end by DaasLabs with phase gates.

Managed Services

We run and improve your data platforms, models and agents — DataOps, MLOps, AgentOps and support under agreed SLAs.

Weekly status Bi-weekly steering Phase-gated sign-off 30-45 day pilot → scale

Start with the outcome you need

Pick a domain and a KPI. We'll propose a discovery and value case or a 30-45 day pilot and show you what the first weeks look like.

Talk to us Take the self-assessment Read the case study
Next chapter · 3 of 6
The foundation

Every domain above runs on the same governed data fabric — policy, claims, billing, customer and the ledger connected, curated, contextualised and consumed, with lineage from source to regulatory report.

Next chapter: The foundation