Actuarial, claims, and regulatory analysis that explains every assumption
Help actuarial, finance, underwriting, and claims teams build review packs, compare model runs, investigate anomalies, and keep every assumption linked to its source.
See it work
Watch a close anomaly get explained before the review meeting
A DataQube session drawn from real customer conversations: motor loss-ratio drift traced to severity inflation, with every assumption linked to its memo and every figure to its run.
Motor loss-ratio drift review
Demonstration based on real customer conversations — all data fictional.
Use cases
Your recurring workflows, automated.
Investment teams don't need another chatbot.They need the recurring work they already do—performance attribution, mandate reviews, and investment committee preparation—to become faster, repeatable, and fully traceable.
Actuarial review packs
Schedule agents to rebuild review packs after close, compare each figure to prior cycles, and flag assumption drift before the meeting.
- Pack assembly moves from days to overnight
- Prior versions remain reconstructible
- Every model output links to its run and memo
Model and assumption governance
Connect model outputs, assumption memos, decisions, and sign-offs in connected, validated memory that agents can recall and auditors can inspect.
- Superseded assumptions retained for audit
- See everything a model change affects
- Review meetings start with what moved
Claims and underwriting analysis
Investigate claims trends, portfolio shifts, and underwriting questions against governed data sources without moving sensitive data out.
- Read-only access to claims and policy stores
- File citations for guidelines and treaties
- Evidence-ready answers for review committees
Workflow
From industry question to auditable answer
The page is organized around outcomes, but the operating model is always the same: ask, resolve evidence, publish a traceable answer.
Close the period
Model outputs land in the warehouse and scheduled agents begin the standing review workflow.
Compare and explain
Each figure is compared to the prior cycle and linked to model runs, assumption memos, and memory.
Review the exceptions
The pack is staged with drift flags, provenance, and a concise list of items that need actuarial judgement.
Stand out with AI that runs on your data
“A representative workflow for shifting actuarial review from manual pack assembly to automated preparation and exception review.”
Representative workflow example · Insurance Provider
Read the customer story ->Featured products
The DataQube modules behind the workflow
Each industry page surfaces the platform capabilities most relevant to that buyer, while keeping one product architecture underneath.
Scheduled agents
Month-end and quarter-end packs run automatically, compare to prior cycles, and stage drafts for approval.
Memory
Assumptions, caveats, and decisions become linked memory - replaced knowledge is retired, and kept for audit.
Threads and notebooks
Actuaries can challenge the agent's output in the same live session and preserve the challenge in the record.
Presentations
Review packs compile from the source analysis, with lineage retained months after sign-off.
Ecosystem
Connect the estate you already operate
The buyer question is practical: does this fit the controlled infrastructure, identity, logging, and internal tool estate already in place?
Kubernetes
Helm into your cluster; images through your registry
Identity
Your single sign-on groups mapped to roles and workspace policy
SIEM
Structured audit events shipped to your logging estate
Internal tools
Your own services exposed as governed, permissioned tools
Resources
Explore insurers in DataQube
FAQ
Questions industry teams ask first
Can DataQube track model assumption changes?
Yes. Assumptions, memos, model families, decisions, and runs can be represented as linked memory, with superseded knowledge retained for audit.
Can packs be reviewed before publication?
Yes. Scheduled agents stage drafts and keep the distributed version untouched until an approved user publishes the next version.
See your data answer questions - without leaving your infrastructure
Thirty minutes with an engineer: live product, deployment options, and your security team's questions answered by someone who wrote the code.