Built on Databricks Genie, it enables a low-risk, production-ready claims assistant that is governed, traceable, and designed to prove value in just 4-6 weeks.
Why this matters
ZenseAI Smart Claims Assistant on Databricks Genie provides claims teams with plain-language answers from trusted Lakehouse data. That is the promise. Zensar turns that promise into real operational value for claims through shared definitions, proven Guidewire integration patterns, and a deployment approach built to minimize rollout risk.
What it delivers
No SQL required:
Adjusters and team leads self-serve answers without an analyst queue.
Governed by design:
Unity Catalog governance helps keep access controlled and supports audit needs.
Auditable answers:
Built-in lineage helps trace responses back to the data source, improving transparency and explainability.
Why Zensar
We make Genie work for claims in production - not just in demos. That includes a claims-specific semantic layer, validated integration patterns (including Guidewire), and a disciplined launch model using golden questions, regression testing, and governance checkpoints. Backed by Zensar’s participation in the Databricks Brickbuilder Accelerator Program, this is a faster, safer path to production AI for claims.
What we’ve industrialized (so this is more than delivery capacity)
Claims KPI library (shared claims terms, business definitions aligned across users and functions)
How this reduces risk: Eliminates definition drift, so users see consistent answers and trust builds faster.
Unity Catalog policy templates (prebuilt governance models covering roles, row- and column-level access rules, and audit patterns)
How this reduces risk: Establishes governance early, making access control and audit readiness easier to review and maintain.
“Golden questions” regression pack (library of known-correct business questions used to validate changes before production)
How this reduces risk: Detects answer regressions before users do when data, definitions, or configurations change.
Repeatable integration patterns (accelerators for Guidewire and other core systems, plus document and operational data sources)
How this reduces risk: Reduces bespoke integration work and prevents fragile one-off pipelines.
Claims teams want something simple: ask a question, get an answer. The idea is easy. Trust is not.
If definitions vary by team, access rules are fuzzy, or no one can explain where an answer came from, adoption stalls. Databricks Genie provides a strong foundation with plain-language analytics on governed Lakehouse data, with Unity Catalog enforcement and lineage. Zensar’s role is making that foundation work for claims - quickly and safely.
After enough client sessions, a pattern emerges. The carriers are different. The lines of business differ. The systems vary. But the same friction points keep appearing.
Not dramatic failures - slow leaks. An AI answer that does not match the report. A definition that means one thing to underwriting and another to the adjuster. A governance conversation pushed to “after launch” and never revisited.
Nobody designed those gaps; they just accumulated over time. And when you put an AI layer on top, the gaps do not disappear - they get louder, and more expensive.
So we started asking: what if we just fixed those gaps first? What if we built something that handled the groundwork - the definitions, the governance, the data foundation - so the AI part could actually deliver on its promises?
That question became ZenseAI Smart Claims Assistant on Databricks Genie.
One conversation that stayed with us |
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We were in a discovery session with a mid-sized carrier that had already tried conversational AI once and walked away from it. As part of our normal assessment, we were walking through the claims workflow term by term when we reached one word: “covered.” |
We asked the adjuster in the room what it meant. She gave us a clear answer. Then we asked the team lead. Different answer. Then someone from the commercial auto side of the house. Third answer. All of them were confident. None of them were the same. |
No one was exactly wrong. Each definition made sense within its own context. But that was the issue. The organization had been operating with multiple meanings for years, and no one had needed to confront it. Until AI began answering “Is this covered?” hundreds of times a day. Suddenly, the inconsistency was visible. Results looked plausible, but confidence eroded. Quietly. Quickly. |
It took three days to align the definition, document it, and enforce it. That one conversation likely saved the entire initiative. It also reinforced something we now bring to every engagement: Do not start with technology. Start with terminology. |
The AI was never the problem. It was the groundwork most organizations skip before deployment that kept causing failure. So we built that groundwork in from the start. |
01. What we keep finding
Across those sessions, three issues surface again and again. Not because every carrier is the same - they are not. But these gaps appear whether an organization is just starting its AI or is already invested in tools and dashboards. But these gaps appear whether an organization is just starting its AI journey or already invested in tools and dashboards.
1. Definitions Drift
Terms like covered, closed, cycle time, and leakage often mean different things to different teams in the same company.
Once AI begins answering questions based on those terms, inconsistencies that were previously tolerated become impossible to ignore. Confidence in the output can fall quickly.
2. Governance Delayed
Not governance in the abstract, policy-deck sense. Governance in the real sense:
Who is allowed to see what?
How is access controlled?
Can you prove where an answer came from?
Many teams plan to solve this after launch. That plan rarely holds.
3. Repeatability Missing
A pilot may work brilliantly for one team or one line of business. But scaling it elsewhere often means starting over.
Definitions do not transfer cleanly
Access models must be redesigned
Momentum from the first win gets consumed by the second rollout instead of fueling the third, fourth, and fifth
None of these are unusual problems. They are common operational gaps that feel manageable - until AI depends on them to produce answers that adjusters, team leads, and compliance teams all need to trust.
02. What we built
At the center of ZenseAI Smart Claims Assistant is Databricks Genie. Claims teams ask a question in plain English and get an answer from governed Lakehouse data - no SQL, no dashboard scavenger hunt, no waiting on an analyst queue. For claims leaders, the real advantage is trust. With Unity Catalog governance, access controls, and auditability are built into how data is accessed and answered. That means:
The right people see the right data
Access policies are controlled and reviewable
Audit requirements are easier to support
Data lineage helps explain where answers came from
When compliance teams or regulators ask questions, you have traceability built in.
Zensar is the enablement partner that makes Genie work for claims in production. We bring the operational layer required to move from demo to dependable value:
Claims semantic layer with shared definitions and KPI libraries
Proven Guidewire integration patterns
Golden questions for business validation
Regression checks to protect answer quality as systems evolve
The result: faster deployment, lower rollout risk, and measurable value before broader scale-up.
A quick credibility note: Zensar’s ZenseAI.Data participates in the Databricks Brickbuilder Accelerator Program. The Everest Group has also recognized Zensar as a Major Contender and Star Performer in the P&C Insurance IT Services PEAK Matrix® Assessment 2025.
Introducing · Available now
ZenseAI Smart Claims Assistant on Databricks Genie
Powered by Databricks Genie
A governed, conversational AI for claims teams - built on the Databricks Lakehouse. Ask a question in plain English. Get a trusted, auditable answer in seconds, with lineage to prove it.
A claims adjuster can use it on day one and get useful answers without training or a technical background.
03. How it works
The sequence matters.
Connecting data, configuring Genie, and setting up access is usually the faster part. The week that requires the most discipline is the one at the beginning - aligning definitions and governance. That is what determines whether the launch succeeds in production.
W1 | Define before you deploy We sit with adjusters, team leads, and compliance. We ask the uncomfortable questions: What does “covered” mean here? What officially counts as closed? How is cycle time measured? Does everyone in the team define it the same way? We document and resolve ambiguity before it becomes a production problem. This week has saved more pilots than any line of code. |
W2-3 | Build the foundation, not just the interface We bring approved claims, policy, billing, and document data into the Databricks Lakehouse. We configure the Zensar Claims Semantic Layer with your agreed definitions and set up Unity Catalog governance for access controls and audit logging. Once the foundation is in place, we tune the Genie Space using real business questions - the ones claim leaders and supervisors actually ask. |
W4-6 | Validate, harden, launch Before go-live, we run our golden question regression pack - a library of known-correct questions validated on real claims data. We harden access controls, train power users, and launch with a support model. Governance does not end at launch; it starts there. Architecture at a glance: How data, governance, and Genie come together for the claims assistant. |
04. What changes
We are careful about outcomes. Every carrier starts from a different baseline, data readiness, and operating model. These are directional ranges observed in real engagements - not guarantees or projections.
2-10 min Coverage verification at FNOL is down from 15-45 minutes | Same day Routing for most claims that previously sat for hours | 60 sec Query response for team leads down from 3-5 business days |
But the number that matters most does not appear on a dashboard. It shows up in renewal. In widely cited surveys, 83% of policyholders say they would switch carriers after a poor claims experience. Every minute you take off cycle time helps. Every status-check call you prevent helps. That is retention. That is trust.
05. Where to start
We do not lead with a demo. We lead with three questions. If these are hard to answer, you already know where the work is. And we have learned that the conversation those questions start is more valuable than any slide deck.
Start here |
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1. How long does it take to verify coverage and route a claim after FNOL - and what is causing the wait? If the answer is “it depends on who picks it up,” you have found the definition problem. |
2. How many systems does an adjuster touch to answer: Is this covered, and what happens next? If the answer is more than two, you have found the fragmentation problem. |
3. How many status-check calls does your team receive each week, and what would it take to cut them in half? If the answer is “we’d need to fix how updates are communicated,” then you have likely found the governance problem. |
Those three problems - definition, fragmentation, governance gaps - are exactly what
ZenseAI Smart Claims Assistant on Databricks Genie is built to solve. In production. Without replacing core systems or disrupting existing workflows.
We built this after watching strong pilots fail for reasons that were entirely preventable. It is built on Databricks Genie and packaged with reusable assets - KPI library, templates, and integration patterns - so carriers do not have to pay to solve the same problems twice.
The adjuster gets coverage confirmed, prior history visible, and routing decided in the first ten minutes. The team lead gets cycle time drivers, backlog signals, and reserve anomalies without waiting days for a report. The policyholder gets an update before they think to call.
That is the product. That is the point. If you have watched a good initiative run out of steam, you already know why this matters.
ZenseAI Smart Claims Assistant on Databricks Genie
Ready to put Databricks Genie to work in claims - and prove value quickly?
Launch a governed Genie experience for claims in 4–6 weeks (depending on scope and data readiness), with engagement options starting at $20K. Databricks provides the platform. Zensar is the claims enablement partner that makes it production-ready.
Daniel Gomez · Data, Engineering & Analytics Practice · daniel.gomez@zensar.com
ZenseAI Smart Claims Assistant on Databricks Genie is built on the Databricks Lakehouse. Zensar’s ZenseAI.Data is a participant in the Databricks Brickbuilder Accelerator Program. Zensar has been recognized by Everest Group as a Major Contender and Star Performer in the P&C Insurance IT Services PEAK Matrix® Assessment 2025. Outcome figures represent directional ranges from Zensar-delivered engagements; individual results vary by carrier baseline and operating environment. Industry retention statistics are commonly published estimates.