The advantage is in the connection.
Bring pipelines, analytics, governance, and AI into one shared context. Spend less time maintaining the seams between tools.
Most of a small data team’s month goes to the seams between tools.
The work people were hired for — modelling, building, answering the question behind the question — is the smallest bar. The rest is the tax a fragmented stack charges every month: reconciling the same metric across three systems, re-running the pipeline that broke on a schema change, and waiting on a vendor ticket to find out why.
The chart is a modelled month for a two-person team running a five-tool stack. It is illustrative, not a measurement of any customer.
| Category | Value | Note |
|---|---|---|
| Reconciling numbers between tools | 42h | Which revenue figure is right? |
| Fixing broken pipelines | 36h | Schema drift, retries, catch-up scripts |
| Answering ad-hoc questions | 30h | Tickets, one-off queries, screenshots |
| Waiting on vendors | 18h | Support tickets across five contracts |
| Actually building | 34h | New models, new sources, new dashboards |
One platform only pays off if it removes the seams. Here is where it does.
Each page below takes one claim and shows the product surface behind it — the engine, the lineage, the monitoring, and the controls.
The GeneFlow engine
One execution graph carries data from source to dashboard with a contract checked at every step, so a pipeline never leaves the platform to be scheduled, tested, or rolled back somewhere else.
Data you can trace to the source
Every number on a dashboard resolves to a committed record, the contract it passed, and the transforms in between — so a disputed figure is a query, not a meeting.
Pipelines that fix themselves before you wake up
Anomalies are caught on the pipeline rather than on the dashboard, and routine failures retry from the last good checkpoint without a page — against a published 99.9% uptime objective.
Security you can show an auditor
Row- and column-level policy, encrypted secrets, and a seven-year audit trail live in the same system as the data — with controls mapped to the frameworks your auditor asks about.
Fragmented stack versus Genedata, on the five things that cost a team its month.
Enterprise capability at a price a small data team can approve. The difference is not feature count — it is who owns the gaps.
| Dimension | Fragmented stack | Genedata |
|---|---|---|
| Cost model | Five subscriptions plus usage-metered compute; the bill is a forecast nobody owns | One plan with a hard cap, from $1,500 a month — the number you approve is the number you pay |
| Who owns the join | Nobody — the warehouse, the BI tool, and the notebook each compute revenue their own way | The platform — one committed record and one metric definition that every surface reads |
| When drift is caught | When a stakeholder notices a dashboard is wrong, usually days after the schema changed | At the contract check on the pipeline, before the change is published downstream |
| How questions get answered | A ticket to the data team, an ad-hoc query, a screenshot pasted into chat | Cortex AI answers against governed tables, with the lineage behind each figure attached |
| Audit trail | Reassembled from five vendors' logs, each with its own retention and format | One append-only log across access, policy, and pipeline events, retained for 7 years |
Bring one pipeline and see the seams disappear.
Connect a source, put a contract on it, and watch the same record reach the warehouse, the dashboard, and the agent — without leaving the platform.
Everything you need to evaluate the fit.
Explore a banking workflow
Work through sample records and see how decisions stay connected to their evidence.
Choose an implementation kit
Download synthetic data, rules, and a review checklist for a focused pilot.
Check your integrations
Understand connection methods, supported operations, and setup constraints.
Review enterprise readiness
Bring security, architecture, privacy, and procurement into the conversation early.
Build your evaluation plan
Define success criteria and estimate value using your own assumptions.
Learn through your role
Follow a practical learning path with a clear output and review checkpoints.