Questions about working with Ridgepole.

A custom system in your private AWS environment. It connects agreed business records and provides the reports, analysis, applications or recurring workflows in your scope. Reusable infrastructure gives us a starting point; your business definitions and working process shape the build.

Yes. Start with a defined reporting need, business question or workflow. Additional connections and features can extend the same foundation as your needs grow. We scope each addition before building it.

It can compare locations, examine changes and find patterns worth a closer look. Recurring analysis can be included in the build, so agreed comparisons run again as new records arrive. Your team receives findings and exceptions to review. New questions and follow-up work are handled through the agreed scope and support.

You do not need to build an internal data team to start. We handle the scoped implementation. We do need people who understand the records, agree on the business definitions and help check that the first result is useful.

We begin with the systems you already use and assess source access. Depending on the source, that may involve a suitable connector, a new connection or agreed exports. Source limits and refresh frequency are part of the proposed scope.

It can take over recurring preparation, reporting and workflow tasks. Whether it replaces a tool or changes a hiring plan depends on the features and human responsibilities you still need. We identify the work to automate and what should remain with your team and existing software.

Tell us which model or AI tool your team prefers. We assess that option and configure a supported connection to your business library. The goal is to give your chosen setup the relevant company records, definitions and access rules without making your team assemble that background for each question.

For information included in the connected library, your team can reuse the same records and business definitions across questions. The plugin gives a supported AI tool a way to retrieve relevant information when it needs it. We agree which sources belong in the library and how they are updated. New information enters through those connections or the agreed upload process.

We agree definitions and comparison checks with the people who know your business. A defined result is checked against the relevant source records. Missing inputs and unresolved differences should be visible, with a path to review the supporting information.

The system runs in your private AWS environment. We agree who can access the data and identify any information sent to an external model service before it is used.

Your data remains yours. The proposal defines rights to use the delivered system and reusable components, along with access, documentation and handoff arrangements. Those terms are agreed before work begins.

There is an upfront build, ongoing support and separate operating costs such as hosting and model usage. The proposal sets out the scope and year-one estimate. Source access, data quality, business rules and the first result determine the work involved.

We set the schedule after assessing the first scope, source access and validation work. Existing components give us a head start. The proposal explains what still needs to be built and what we need from your team.

The support arrangement defines how source changes, maintenance and issues are handled. New reports, workflows, locations or applications are scoped around the next useful result. We agree on responsibilities and any handoff if support ends.

We start with the work you need done, the systems involved and the people who will use the result. A useful first build could be a recurring report, a question spanning several systems or a task your team keeps repeating. Your industry does not need to appear on a list. We assess the records and agree on the first result before proposing the build.