The AI-native system of memory for insurance that connects
underwriting, pricing, claims, and compliance across the policy lifecycle.

Every claim, endorsement, and regulatory response already holds the reasoning your next decision needs. Qubru extracts it from multiple unstructured sources — external documents and the unstructured content already living inside your core systems — and delivers real-time insights when the human needs to decide.

See how the memory works
The product

From four disciplines, many sources, to one policy lifecycle memory

Underwriting, pricing, claims, and compliance each built their own tools and their own memory. Qubru doesn't replace any of them — it connects what they already know across multiple unstructured sources, so the reasoning that used to get lost between systems flows back to where it's needed. The insurance-specific ontology arrives fully built — no training project required, no multi-year customization to fit your carrier's document universe.

1

Insurance-native document understanding

Ingest documents and unstructured text from core systems. Classify against a canonical ontology of insurance document types — broker submissions, underwriting and pricing documents, inspections, adjuster narratives, endorsements, regulatory correspondence — built into the product, not configured post-deployment. Everything starts here: raw content becomes structured insurance signals.

2

Policy lifecycle memory

Every classified signal enters the memory — organized by policy, by lifecycle stage, by domain. A claims outcome traced back to the underwriting decision, pricing logic, and compliance response that preceded it, automatically. The memory that used to live in documents no system connected now flows continuously back to where it's needed.

3

Cross-domain pattern surfacing

With memory in place, patterns emerge across the four disciplines — a broker's submissions correlating with adverse outcomes, a corridor's claims correlating with underpriced risk. Patterns that only exist across underwriting, pricing, and claims, surfaced automatically. Drill any pattern down to the document, or roll it up across every line of business.

4

Entity & relationship graph

Patterns become durable knowledge only when the same insured, broker, or principal is recognized across auto, home, and commercial — across files, systems, and years. The graph is what keeps the intelligence together: a pattern in one line of business doesn't stay invisible to the others, and a signal from three years ago still connects to the decision being made today.

5

Governance & audit

Everything above happens under continuous governance — retention rules, tenant isolation, least-privilege access, and a tamper-evident audit trail on every action. Every signal traces back to its source with full lineage. Every decision the memory informs is auditable. Governance isn't the last step; it's the layer that makes the other four safe to deploy in a regulated carrier.

How it works

Five steps from separate systems and data formats to one actionable insight

The mechanism underneath — from ingestion to governance — laid out in the order it actually runs.

1

Ingest

Drop in documents from underwriting, pricing, and claims — from your existing stores, scanned or native. No system-by-system migration required to start. You can also connect your existing data lakes and connect the unstructured content already living inside your core.

2

Classify

Each document is triaged and labelled by domain and lifecycle stage, so it's usable by the discipline it came from and the ones that need it next.

3

Connect

A claims outcome is linked back to the underwriting decision, pricing logic, and compliance response that preceded it — across every line of business — building the policy lifecycle memory those disciplines couldn't build on their own.

4

Surface

Cross-domain patterns emerge on their own — a claims trend reaching the actuary, a pricing signal reaching the underwriter. Drill into the document, or roll it up across the book.

5

Govern

Retention, disposition, access, and audit run continuously underneath all of it — with every action recorded.

Built to sit on top of what you already run

Data flows from your existing systems, sources, and the unstructured data you upload as you need. Governance runs through every layer.

GOVERNANCE & COMPLIANCE — TENANT ISOLATION · ACCESS CONTROL · RETENTION · AUDIT · LEGAL HOLDS
SURFACE

Where insight reaches the people who decide

Chief Underwriter, Actuary, Claims teams, CFO/COO, Compliance — each sees the patterns relevant to their role, with evidence organized for the decision.

SUBSTRATE

The policy lifecycle memory

Entity & relationship graph, cross-domain patterns, and the continuous memory that connects underwriting, pricing, claims, and compliance across time.

INGESTION

Classification against the insurance-native ontology

Documents and unstructured text — from anywhere they live — are classified, entities are resolved, and signals are extracted with insurance semantics built in.

SOURCES — WHERE THE DATA LIVES

Your existing systems — read from, never replaced

Guidewire, Duck Creek, legacy PAS, claims systems, data warehouses. Qubru extracts value from both what's structured and what isn't.

The two types converge inside Qubru's substrate — structured records ground the entities, unstructured text reveals the why. Neither is enough alone; together they build the memory.
Structured data

Policies, claims, transactions, coverages — what every data warehouse already ingests.

Unstructured data inside the core and external files

Adjuster narratives, workshop quotes, medical approval text, underwriting memos — where the reasoning actually lives.

Why sources matter. Your insurance core and your stored documents together hold significant unstructured content that traditional data platforms leave untouched. Qubru extracts signal from all of it — using insurance-specific ontology and data mapping that make some paths dramatically faster than others.

Fastest route to substrate value at every carrier without having to spend months training a model. Accelerate the AI curve adoption and save hundreds of thousands of dollars in time to value by going Qubru.
Who it's for

Built for the people who live the policy lifecycle — and the ones who answer for it

VP of Line of Business

You own the P&L for this line — combined ratio, growth, retention — not just underwriting policy. Qubru scopes to your line specifically, and surfaces the pattern connecting your underwriting, pricing, and claims teams before it costs another point of combined ratio.

Chief Underwriter & Chief Actuary

See exactly what was known at the moment of every bind, and which underwriting and pricing decisions the claims record actually confirmed or refuted — the institutional knowledge that used to leave when experienced staff did.

CFO, CEO & COO

A combined ratio near 100% moves on decisions your systems don't currently connect — and the disconnect between underwriting, pricing, and claims is an operational problem before it's a financial one. Qubru surfaces the pattern before it costs another point of loss ratio.

Compliance & legal

Every signal traces back to source with full lineage — audit and regulatory response without a separate records project.

Claims teams

The operational layer that consumes Qubru on a daily basis. Adjusters, examiners, and claims specialists open Qubru inside every case — retrieving the underwriting decision, the pricing logic, and the pattern context that inform how this specific claim should be handled. The reasoning behind the policy is one click away, not buried in three systems.

Insurance operations

The operational layer that consumes Qubru on a daily basis. Policy operations, endorsement teams, and back-office staff use Qubru to reconstruct the full lifecycle of any policy on demand — resolving customer inquiries, processing endorsements, and preparing renewal packages with the full history at hand rather than assembled ad-hoc from separate systems.

Qubru provides insight.
The human decides.
The core executes.

The memory builds at your pace, not the system's. Qubru surfaces the reasoning that connects underwriting, pricing, claims, and compliance. Your actuaries review the patterns. Your underwriting committees evaluate the recommendations. Your claims professionals assess the outcomes. Your compliance officers verify the audit trail. Your teams make the calls.

Why the boundary matters. This structural position keeps Qubru outside the regulatory regimes governing automated decision-making in insurance — NAIC Model Bulletin (adopted in 24+ US states), NYDFS Circular Letter 7, Colorado Regulation 10-1-1, and the emerging LATAM frameworks that follow this trajectory. Your team's decisions remain your team's decisions for regulatory purposes.
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FAQ

Common questions before the first call

Does this replace Guidewire, Duck Creek, or our other core systems?

No. Qubru reads from the systems you already run — it doesn't migrate or replace them. Documents and data stay where they are; Qubru connects the reasoning across them.

What kinds of data does Qubru actually read?

Both external documents (PDFs, scans, emails, native files) and the unstructured content already living inside your existing core systems (adjuster narratives, workshop quotes, medical approval text, underwriting memos, endorsement notes). The second path is where most operational reasoning lives, and it's where Qubru delivers signal fastest — through mechanisms specific to how insurance core systems structure their data.

What happens to our underwriting and claims teams?

They keep the decision. Qubru surfaces the pattern and the evidence behind it — actuaries, underwriting committees, and claims professionals still review and decide.

How do you handle data security and access?

Every action runs through tenant isolation, least-privilege access, and an audit trail — the same controls a regulated carrier already answers to, built in rather than bolted on.

Do we need a full data migration to start?

No system-by-system migration is required to begin. Documents can be ingested from your existing stores, and unstructured fields from core systems can be extracted without disturbing production data flows.