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What Render builds

Render turns scattered financial and operational data into clear answers for leadership. We build and run a governed data foundation, a single source of truth. On top of it is one AI analytics platform, where your team asks questions in chat, pulls the monthly financial package, and works from dashboards and reports.

The data foundation

Everything else depends on this. Data from every system of record lands in one warehouse, where it is cleaned and modeled into a single source of truth, structured by a semantic layer and monitored for quality. Every change is versioned and reviewed, and no one edits the data directly, so any number in the platform traces back to one definition.

  1. 1Data evaluationWe map the systems of record and find where the data lines up and where it falls short. You get a plan that ties fixes to the data model, including process changes to capture data that isn't recorded yet.
  2. 2Foundation buildWe build the data warehouse, the semantic layer and the business logic. All of it is versioned and reviewed, so every number traces back to a single definition.
  3. 3Managed serviceWe keep the foundation current as the business changes. New systems get integrated, the logic gets updated, and automated checks catch problems before anyone reads a wrong number.

Governed · Versioned · Monitored end to end

The app's Model tab: the warehouse tables Render builds for a company, and how they join. Swipe it sideways to see every table.

One AI analytics platform

The Render AI Analytics App is where the foundation gets used. It is one place for a wide range of business analytics, and it has three modules: Chat, Financial reporting, and Dashboards and reports. All three run on the same governed numbers. An AI analyst, built on Claude and grounded in your data, works across them.

Module 1 of 3

Chat

Ask the AI analyst a question in plain language and get the answer from your own data. It works from the governed models and your business context, so it gives the same numbers as the dashboards and the financials. Nobody needs SQL, and nobody waits on an analyst.

  • AskType a question the way you'd ask a colleague. The answer comes back in seconds.
  • ExploreFollow up, chart it, drill in. Each answer shows how it was worked out.
  • TrustTwo people asking the same question get the same number.

Plain-language questions · Governed definitions

The app's Chat tab: a question in plain language and the analyst's answer. Sample data.

Module 2 of 3

Financial reporting

The monthly reporting package, ready right after close. The statements come straight from the governed foundation and tie to the general ledger. The finance team gets back the days it used to spend assembling them.

  • Statement packagesAutomated financial statement packages, ready for monthly management meetings and quarterly boards.
  • ConsolidationFinancials from multiple accounting systems come out to one reporting standard, and nobody rolls them up by hand.
  • Account mappingA lengthy chart of accounts is mapped to reporting views that an AI agent recommends and you confirm.
  • AnalysisAI-driven financial analysis and variance commentary come with the statements.

Monthly reporting package · Tied to the general ledger

The app's Financials tab: a published monthly package, open at the income statement. Sample data.

Module 3 of 3

Dashboards and reports

The view people work from every day. Dashboards bring data from multiple systems of record into one place, built for whoever uses them: sales teams chasing monthly targets, finance teams running variance analysis, plant managers watching throughput. Reports go deeper. The AI analyst writes up what changed and what needs attention, tied to the initiatives in the value creation plan.

  • For executivesKPI dashboards for the C-suite, organized around the value drivers and kept current. This is the view leadership opens first thing Monday morning.
  • For operatorsSite, region, SKU and customer views for the people running the business day to day. Each one ties to a real operating decision and feeds the strategic numbers.
  • Deep-dive reportsThe AI agent pulls the data, finds what changed and writes it up for each strategic initiative. You edit and approve. Briefings can run on a schedule.

KPI dashboards · Deep-dive reports · Scheduled briefings

The app's Reports tab: a published management report. Sample data.

Self-service analytics

Your team can also build its own views in Claude and Claude Dashboards. They read the warehouse Render builds, so they start from the same cleaned and tested numbers as the app.

Where the work shows up

Examples from client engagements, each tied to the driver it moves.

  • Sales growth

    Sales analytics

    Price, volume and mix analysis by product, customer and region. Shows where revenue is coming from and which opportunities the sales team can act on now.

  • Margin expansion

    Labor and workforce analytics

    Productivity by role, site and shift, and overtime patterns. Shows where labor cost is rising and where capacity sits idle.

  • Margin expansion

    Operations analytics

    Capacity utilization and throughput by line and shift. Shows what limits output and where it is moving in the right direction.

  • Margin expansion

    Opex analytics

    Marketing, G&A and overhead spend by function, with leakage and vendor consolidation. Shows where spend grows faster than the business.

  • Working capital

    Working capital optimization

    AR aging, DSO, inventory and supplier payment terms. Finds the cash tied up in the cycle and the actions to free it.

  • Cross-functional

    Month-end financial reporting

    Automated month-end reporting, variance commentary and board-ready packages. You see the numbers days sooner.

Some projects pay off in time instead. When reporting that took days of downloads and spreadsheets runs on its own, those hours go back to running the business.

Four principles behind every engagement

Dashboards are the easy part. The harder part is deciding what to build, what to leave out and how to deliver it, and that takes knowing how businesses run and create value.

  1. 1

    Value

    Every engagement ties to value in one of two ways. It moves an enterprise value driver (sales growth, margin expansion or working capital efficiency), or it gives the team its time back by automating manual reporting.

  2. 2

    Data

    Analytics is only as good as the data under it. We build the governed data foundation first and test it before anything else is built on it.

  3. 3

    Signals

    Decision-makers get KPIs and findings they can act on. Most of the work is choosing what is worth watching and what is noise.

  4. 4

    Action

    Analysis means little if it never reaches the business. Whether it's automated reporting, a KPI dashboard or a strategic review, each deliverable is clear enough to read at a glance and act on.

Where Render fits across the hold

We work at every stage of an investment. What we build in one stage carries into the next, so the business reaches exit with the KPIs to show how it improved.

  1. 1

    Diligence

    Assess a target's data readiness and surface the operational signals that pressure-test the investment thesis.

  2. 2

    First 100 days

    Stand up the governed data foundation and the platform on top of it: chat, financial reporting and the core dashboards. Management gets numbers to work from early.

  3. 3

    Value creation

    Keep working with management as the plan plays out. We add analyses as priorities shift and tighten the ones already in use.

  4. 4

    Exit

    The business goes to market more valuable, with KPIs that show how it improved over the hold period.

We work in the tools you already use.

Render builds on technology from Google, Anthropic and other established platforms for enterprise data and analytics.

Tell us what's on your plate.

Tell us where you want performance to improve and we'll bring the data and analytics to get you there.

Let's talkinfo@renderanalytics.com