Genedata customers

Genedata. Deployed and used.

Meet the organizations that deployed and used Genedata across banking, insurance, telecommunications, financial technology, and the nonprofit sector.

Genedata customer / 01
Deployed and used Genedata
Financial services

TD Bank

TD Bank deployed and used Genedata.

Customer context

Consolidation and transformation work for TD and Waterhouse Investor Services within a broader delivery team.

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Genedata customer / 02
Deployed and used Genedata
Insurance

Generali

Generali deployed and used Genedata.

Customer context

Insurance and data consolidation connected with server optimization and cost efficiency.

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Genedata customer / 03
Deployed and used Genedata
Financial services

CaixaBank

CaixaBank deployed and used Genedata.

Customer context

Digital transformation and AI enablement, contributing specialist expertise within a wider organizational program.

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Genedata customer / 04
Deployed and used Genedata
Telecommunications

Sigma Systems

Sigma Systems deployed and used Genedata.

Customer context

Product delivery and professional services improvements supporting expansion into Europe and Japan.

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Genedata customer / 05
Deployed and used Genedata
Not-for-profit

Anglican Church of Canada

Anglican Church of Canada deployed and used Genedata.

Customer context

Data, cloud, and network transformation followed by continuing support and maintenance.

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Genedata customer / 06
Deployed and used Genedata
Not-for-profit

Toronto Community Benefits Network

Toronto Community Benefits Network deployed and used Genedata.

Customer context

Cloud and network infrastructure optimization supporting a continuing community partnership.

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Genedata customer / 07
Deployed and used Genedata
Insurance

Vienna Insurance Group (VIG)

Vienna Insurance Group (VIG) deployed and used Genedata.

Customer context

Insurance transformation and data enablement across the European organization.

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Genedata customer / 08
Deployed and used Genedata
Financial technology

724 Solutions

724 Solutions deployed and used Genedata.

Customer context

Mobile banking and brokerage platform development and deployment across Canadian and US financial institutions.

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Built on Genedata

Products and operations on the platform

AegisNow

Insurance

An insurance operations platform covering underwriting, claims, bordereaux processing, and life actuarial work.

The problem

Insurance data outlives the systems that produce it. A policy written today generates claims for years and development triangles for a decade, so a change to how loss is coded quietly invalidates comparisons made against earlier years. Underwriting, claims, and actuarial each need the same policy and loss record, and each traditionally kept its own copy.

What it does on the platform

  • Bordereaux ingestion from broker and MGA feeds, catalogued and schema-versioned on arrival rather than reconciled after the fact
  • One policy and loss record read by underwriting, claims, and actuarial, so the loss ratio does not depend on who is asked
  • Life actuarial models running against governed tables, with the assumptions versioned alongside the results
  • Claims scored at first notice of loss under the invoking user's permissions, with the contributing signals attached to each referral

Capabilities it leans on

  • Column-level lineage from the broker feed through to a reserving figure
  • Schema versioning, so a triangle can be read under the coding in force at the time
  • Row-level policy separating underwriting, claims, and actuarial access on one record
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Retirewise AI

Financial services

A retirement planning platform serving Canadian and US residents, with country-specific tax and account workflows in one product.

The problem

Retirement projection is a regulated-adjacent calculation on personal financial data: the inputs are sensitive, the arithmetic has to be reproducible years later, and two countries' rules have to coexist without leaking into each other's results. A projection a customer saw last quarter has to be reconstructable under the assumptions in force then.

What it does on the platform

  • Account, contribution, and tax-rule data landing in one governed store with per-country partitioning
  • Projection runs pinned to a dataset version, so a plan shown in March can be reproduced in September
  • PII classified on write, so personal financial data is masked at query time rather than by a preprocessing step
  • Advisor-tier dashboards scoped by row-level policy, so an advisor sees their own clients and no others

Capabilities it leans on

  • Point-in-time reproduction of a projection under the rules that applied when it was shown
  • Content-based PII classification on personal financial fields
  • Row-level policy separating advisor books of business
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GeneComply

Governance and compliance

A compliance operations product: control catalogues, evidence collection, and framework mapping.

The problem

Compliance evidence is usually assembled by hand before an assessment and discarded afterwards, which makes control effectiveness a sampling exercise rather than a measurement. The evidence a framework asks for is mostly already produced by the systems being assessed — the problem is that it is not retained in a form anyone can query.

What it does on the platform

  • Control catalogues mapped to the code that enforces each control, rather than to a policy document describing it
  • Evidence collected continuously from running systems, so an assessment reads a live record instead of a reconstruction
  • Framework mapping treated as a view over one decision record, so adding a framework is a mapping exercise rather than a new evidence pipeline
  • Immutable audit of who saw which evidence and when, retained for the assessment window

Capabilities it leans on

  • Hash-chained audit, so an evidence record can be shown to be unaltered
  • Attribute-based policy evaluated per request and recorded with its inputs
  • Seven-year retention on the decision log
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GeneSecure

Security operations

A security operations product covering detection, retrospective hunting, and incident evidence.

The problem

A SOC's hunt quality is bounded by what it can afford to retain. Per-gigabyte ingest licensing creates pressure to drop the log source that turns out to matter, and cold storage that needs rehydrating turns a scoping question into a next-day answer.

What it does on the platform

  • Syslog, EDR, cloud audit, and NetFlow landing in open format the customer owns rather than a vendor index
  • Hunt queries across the full retention window without a rehydration step, so a newly disclosed indicator is checked the same day
  • Detections authored as code, versioned, and back-tested against historical telemetry before they go live
  • Agentic triage running under the analyst's own permissions, with every step in the same audit chain as a human query

Capabilities it leans on

  • Storage and compute priced separately, so retention is not the binding constraint on coverage
  • Open table formats in the customer's own object storage
  • Per-step audit on agent actions, under the invoking identity
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New York Yacht Club

Hospitality and private clubs

Member operations, food and beverage, and overnight room analytics for a private club.

The problem

Club operations run on systems that were never designed to agree with each other: point of sale, room bookings, membership, and billing each hold part of a member's activity, and the member-facing view has to reconcile all of them. A member who dined on Tuesday and stayed on Wednesday is two unrelated rows until something joins them.

What it does on the platform

  • Point-of-sale, room, and membership data resolved to one member record
  • Restaurant covers and overnight occupancy read from the same governed tables as the finance close
  • Member-360 views scoped by row-level policy, so staff see what their role allows and no more
  • Daily operations reporting run by the people who run the club, not by a data team

Capabilities it leans on

  • One governed store behind operations and finance
  • Row-level policy on member data
  • Pipelines supervised by people who are not full-time data engineers
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