Custom data systems and software
See what your systems can't show you alone.
Ridgepole connects the systems your business already uses, then builds the metrics, workflows and tools you need on top. A clearer view of performance. Less manual work. AI that can work with your company's information.
Your systems today. Accounting, payroll, CRM and the rest, held together by exports, re-keying and spreadsheets. Each keeps its own version of the numbers.
One real build
- Built for a 20+ community operator
- 6+ source systems connected
- 2.8M+ general-ledger lines
- Automated reporting workflows
A business runs on connections
See the work. Then see what connects it.
Different problems often depend on the same information. Pick a problem to trace its systems and the shared foundation under it.
Follow a route
“Which locations or services are losing margin, and why?”
4 systems4 foundation parts→ Trusted report
- 1Match revenue and costs to the same location.
- 2Apply one agreed margin definition.
- 3Flag the locations that move when new data lands.
Illustrative routes. Nothing here is a live connection.
The boring AI and data infrastructure company
The foundation that holds up your house.
Ridgepole is the foundation under your reports, tools and AI. Your systems stay as they are. We build the layer that makes them agree, then the software on top.
- 03
Useful software
Reports, internal apps, your BI and AI, built for the work your team does.
- 02
The foundation we build
Custom connectors, matched records, shared definitions and data checks, in your private cloud.
- 01
The systems you use today
Accounting, payroll, scheduling, CRM and the spreadsheets between them. Kept as they are.
What becomes possible
More becomes possible when the information is connected.
Bring financial, staffing, customer and operating information together. Then use it to answer better questions and change how the work gets done.
01
Measure what separate systems miss.
Combine information to understand performance across locations, services, customers or teams.
- Shared definitions for every key figure
- Forecasts and scenarios, tested against a baseline
- A data science team, included
02
Remove the work between systems.
Automate recurring preparation, reconciliations, updates and handoffs.
- Scheduled reports, built for you
- Exceptions routed to the right person
- Your workbooks filled, formulas kept
03
Give people tools that fit their work.
Build practical internal applications, dashboards or workflows on the same connected foundation.
- Apps built on your real records
- Your BI connected to clean data
- Every result traces back to its source
04
Make AI useful in your business.
Give AI access to relevant company information within agreed permissions, so it can answer questions with business context.
- Answers from your real numbers
- Sources, dates and gaps shown
- Software for the repeats, AI for the exceptions
Examples of questions and tasks to scope
- Why is margin slipping at certain locations?
- Where is overtime spiking, and why?
- Which referral sources actually convert to revenue?
- Where is revenue leaking between the work and the bill?
- Which services are growing without improving contribution after direct costs?
- Can we stop re-entering information between teams and flag the exceptions that need attention?
These are capabilities we build to scope. The first outcome and the work included are agreed around your business.
Manual vs connected
See where the manual version breaks.
One business question. Five steps. Run it by hand, then run it on a connected foundation.
The question
Are higher sales leaving more after direct labor?
- Sales & billingExport file 1
- PayrollExport file 2
- Time recordsExport file 3
- Location listExport file 4
Step 1 / 5Someone signs in to each system, exports files and checks which version belongs in this run.
The limit
The sales system shows growth. Payroll sits in another file, and its locations use other codes.
A careful manual process can work. Repeating it across teams and frequent changes becomes the bottleneck.
Manual output
Made-up example- Revenue up 12%
- !Labor cost: still matching locations
- After labor: not known yet
A snapshot to recheck on the next run.
Chat to software
Turn stable steps into software.
Tokens are about to get expensive. Spend model reasoning on interpretation and exceptions, and run the repeatable steps as software you own.
- 1Define the job
- 2Test the agreed rules
- 3Run stable steps as code
- 4Review the exceptions
- space
- run once
- t
- run ten
- 0
- reset
100runs
Assume 10% of runs need a model to review an exception, with the same full context. Stable calculations run as code.
Repeat the full prompt
1,200,000 tokens
100 runs × 12,000 context tokens
Software + selected reasoning
132,000 tokens
12,000 setup + 10 exception reviews × 12,000 tokens
9× fewer tokens in this illustration. The repeatable rules are versioned, tested and traceable.
Illustrative model-token accounting, not a bill or a promise of savings. It leaves out API pricing, tool calls, storage, connectors and build costs. Real use and review rates vary.
A real build
Built for a 20+ community operator.
A custom AWS data-infrastructure build for a senior-living operator with more than 20 communities. We don't name our clients.
- The starting point
- Information was spread across six or more source systems, including scheduling, payroll, accounting and clinical and operational information. Recurring reporting meant spreadsheet assembly.
- The work delivered
- Ridgepole built a shared data foundation and automated recurring reporting. Then we used the foundation to show leadership where margins were under pressure, and what to check next.
M+
General-ledger lines
Ingested into the reference warehouse.
Location-mapping records
One identity for each community across five platforms.
+
Source systems
Scheduling, payroll, accounting, clinical and operational.
Workbook values filled
In a weekly workbook test, with every existing formula kept.
How we work
Start with what matters. Build from there.
We reuse existing components and handle the technical work, so you don't have to assemble a separate data team to get started.
- 01Systems and process audit
Choose the first outcome.
Understand the decision, recurring work or missing capability that matters most, along with the systems and access you need. Agree what a useful first result does and how your team will use it.
- 02One-time build fee
Connect and build.
Bring the relevant information together, agree how important figures are defined, and deliver the first useful analysis, workflow or tool. We set up AWS for you as part of the build.
- 03Ongoing maintenance fee
Maintain and expand.
Keep the agreed connections and software working, and add capabilities on the same foundation as priorities change. Ongoing engineering supports the work as systems, definitions and needs change.
A one-time build fee plus an ongoing maintenance fee, with the AWS setup included. We scope both in discovery and give you an all-in year-one estimate. We never quote a number before we look.
How an engagement runsWhat would you like to understand or get done?
A question your systems can't answer. Work your team repeats. A tool you wish existed. Tell us where you want to start.