Business Intelligence Platform

Deliver data to the right requester at the right time

Clatani makes complex datasets accessible, secure, and ready for enterprise decision-making through elite backend transformation and custom frontend analytics layers.

Analyst reviewing enterprise performance charts on a laptop

Built for the data teams tired of waiting on their pipelines

Clatani is a data infrastructure partner for organizations handling fragmented, high-stakes information, health records spread across five systems, claims history locked in decades-old formats, alternative data feeds that change shape every quarter. We design, build, and operate the pipelines that turn that chaos into a governed layer your team can build real decisions on.

Every engagement starts the same way: map where the data actually lives, engineer a safe path to extract it, and deliver it in a form your analysts, researchers, and executives can use without a data engineering degree.

Industry insight
73%

Most enterprise data never makes it into a decision

By some estimates, nearly three-quarters of the data companies collect goes unused for analytics. It isn’t a collection problem, it’s a pipeline problem. Clatani exists to close that gap: every source connected, every record governed, every dataset delivered in a form someone can actually act on.

Industry estimate, based on widely cited research.

Built on

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What every engagement comes with

Clatani is a young firm. Rather than ask you to take scale on faith, we put four commitments in writing on every engagement.

Scope
48h
From first call to a written scope with fixed pricing. No open-ended discovery phase.
Pricing
Fixed
Agreed in writing before any work starts. No hourly drift, no surprise invoices.
Ownership
1:1
The engineer who scopes your pipeline is the one who builds and runs it.
Operation
We run it
Once live, monitoring, source changes, and fixes are ours. You consume the data, not the maintenance.

The work that happens between the dashboards

Most data projects don’t fail on the dashboard. They fail two layers down, in the extract nobody owns, the join that quietly drops rows, the source that changed format on a Tuesday. That layer is unglamorous, it is where the risk actually sits, and it is what we build.

It is rarely the modelling that sinks a data project. It is the source that changes format without warning, the join that silently drops ten percent of rows, the extract nobody can explain six months later. That layer is what we build, document, and then run, so the people who depend on the numbers can say where each one came from and trust that it is still current.

Have a dataset that needs a home? Let's talk.