Building the data foundation for AI across an insurance carrier in Brazil
A Brazilian insurance carrier returns to the capture layer so its AI ambitions can be built on complete, reliable data.
The Situation
An insurance carrier in Brazil was drawn toward AI-driven underwriting and service improvements, but its underlying data was fragmented and unevenly captured across products and channels. The result was a growing gap between the sophistication of the AI the business wanted to run and the trustworthiness of the signals it would learn from. In a market where underwriting accuracy and regulatory scrutiny compound every small data error, deploying intelligence on a broken capture layer would have multiplied mistakes rather than removed them. The challenge was to consolidate the measurement layer first, so that any AI initiative had a trustworthy base to learn from.
The Insight
Insurers earn margin on the accuracy of their estimates, not on the volume of their activity. Every underwriting or service decision that automation makes is only as good as the data it inherits — so a carrier whose capture layer is incomplete faces asymmetric downside: each layer of AI built on partial signals amplifies the original gaps downstream. The binding constraint is not a lack of AI ambition or tooling, but the fact that no amount of orchestration or scale can compensate for an unreliable foundation. Fixing capture first is therefore the highest-leverage move, because it converts every subsequent investment from speculative into compounding.
Diagnosis
Through CORE™, the constraint was at Capture: ambition outran the data. Before AI could orchestrate or scale, the organization needed complete and dependable capture of the signals it would build upon.
CORE™ Maturity Diagnosis
Scale 1–7. Highlighted = the real constraint this diagnosis identified.
Framework applied: core-framework
The Strategy
The decision was to resist building AI capabilities on top of a broken measurement layer and instead invest first in making capture complete and dependable. Evox AI-First™ was the correct program because its sequence — data foundations before deployment — matched the diagnosed constraint exactly: it ordered the work so that every subsequent automation and intelligence effort would rest on a trustworthy base, turning what could have been a fragile patchwork of pilots into a single compounding platform. The plan was deliberately front-loaded, accepting slower early visible progress in exchange for durable, accurate automation later.
Execution
The engagement applied Evox AI-First™ beginning at the capture layer, re-engineering operational workflows around autonomous agentic pipelines that parse, validate, and score complex regulatory documents and inquiries in real time. The concrete work ordered data foundations first — consolidating and completing the signals the business would build on — before any deployment, so that each subsequent AI capability rests on a measurement layer that is complete and trustworthy rather than on fragmented, uneven inputs.
The Investment
The engagement was structured as an 8-month strategic program rather than a one-off build: a bounded sequence of foundation-first work carried through into the deployment of autonomous workflows. Its nature was to invest in the data layer as the enabling asset, accepting that the payoff concentrates at the end of the period once the refined automation goes live.
The Results
The AI-First™ strategic program transformed the enterprise operating model by ensuring artificial intelligence became the structural core rather than a superficial accessory. Under the CORE™ Orchestrate diagnostic, operational throughput had been throttled by legacy manual verification procedures that delayed customer onboarding. Evox re-engineered operational workflows around autonomous agentic pipelines that parse, validate, and score complex regulatory documents and inquiries in real time. Over the 8 months deployment, 80% of routine qualification was automated, accelerating operational speed by 3.2x while maintaining a 99.4% accuracy standard. The intervention unlocked R$2.10M in annualized OPEX savings, proving that integrating AI as foundational operational infrastructure multiplies employee leverage and scales business volume without head-count expansion.
| Indicator | Result | Detail |
|---|---|---|
| Core Process Automation Rate | 80% | Proportion of routine operational qualification and customer data processing executed via autonomous AI workflows |
| Operational Turnaround Speed | 3.2x | Acceleration in response time and risk evaluation from 48 hours to under 2 hours |
| Annualized Operating Expense Savings | R$2.10M | Elimination of manual third-party auditing costs and repetitive administrative processing overhead |
| Decision Accuracy Score | 99.4% | Precision match rate on automated triage compared to senior underwriter benchmarking |
Operational Turnaround Speed
Core Process Automation Rate
Annualized Operating Expense Savings
Decision Accuracy Score
Operational Turnaround Speed
Core Process Automation Rate
Annualized Operating Expense Savings
Decision Accuracy Score
The Exact Mechanism
Consolidating and completing the capture layer made the data trustworthy, which let autonomous agentic pipelines automate 80% of routine qualification — accelerating operational speed by 3.2x while holding a 99.4% accuracy standard and unlocking R$2.10M in annualized OPEX savings.
Transferable Lessons
- Ambition for AI is worth little if the data it learns from is incomplete — fix the capture layer before adding intelligence on top.
- Ordering foundation work ahead of deployment trades slower early wins for automation that compounds instead of multiplying errors.
- Accuracy and speed do not have to be a trade-off when the underlying data is dependable; automation can deliver both at once.
- Front-loading measurement work is the highest-leverage way to protect every downstream technology investment.
Discussion Questions
- When is the cost of delaying an AI deployment to fix data foundations worth the downstream accuracy it protects?
- How should an organization measure the compounding downside of building automation on partial signals?
- Where is the line between a legitimate front-loaded data investment and an endless preparation phase that never ships?