DatumLabs

8+

Workspace views across portfolio, daily ops, financials, rents, occupancy, and budgets

On demand

Branded PDF reports generated by clients without internal team involvement

Structural

Client separation built into the access model, not applied as a filter after the fact

"Warehouse data becomes useful the moment the right person can open it at the right level of detail without waiting for someone to compile it. Building client separation into the access model from the start, rather than applying it as a filter later, is what made every client's view trustworthy from day one."

The Problem

Ember Capital manages multifamily real estate portfolios where investment decisions depend on a consistent, accurate view of property performance. Occupancy, rent collections, delinquency, leasing activity, budget comparisons, and financial snapshots all matter. So does knowing which user should see which property and which client's data stays completely separate from another's.

The data existed across warehouse-style tables covering income statements, rent rolls, leasing summaries, proformas, and daily operational records. What did not exist was a client-facing interface that made that data accessible, navigable, and useful for the investors, asset managers, and internal teams who needed to act on it. Manual report assembly was the workaround. It was slow, inconsistent, and did not scale.

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Portfolio Performance Is Only Useful
_{When the Right People Can See It}

Real estate investment decisions run on data that has to be current, accurate, and accessible to the right people at the right level of detail. A portfolio manager needs a high-level view across all properties. A client reviewing a specific asset needs occupancy trends, rent variance, and budget comparison. An internal admin needs to manage users, set branding, and configure access without touching the underlying data.

None of those needs can be met by a warehouse alone. A warehouse organizes data. It does not give clients a governed interface to explore it, compare it, annotate it, and export it. That is the gap Ember Capital was working around with manual report assembly.

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Manual Report Assembly Was Slow,
_{Inconsistent, and Did Not Scale}

The data was warehouse-ready. Income statements, rent rolls, leasing summaries, proformas, and daily operational records were structured and available. But every time a client needed a performance update, someone had to pull the relevant tables, format the numbers, build the visuals, and package the output. The process depended on who was doing it and when, which meant the same question could produce different answers depending on timing and interpretation.

Investor reporting is not a place where inconsistency is acceptable. Ember Capital needed a system that removed the manual step entirely and gave clients direct, governed access to their own data.

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Client-Scoped, Role-Aware,
_{and Built for Recurring Investor Reporting}

Datum Labs designed and built a multi-tenant property analytics portal that connects warehouse data to a governed, client-scoped interface.

The portfolio dashboard gives users a summary view of properties, units, AUM, a property map, capital stack chart, trailing NOI, occupancy, receivables, expenses, economic occupancy, and lease expirations. A daily overview surfaces occupancy, a delinquency chart, leasing activity, leasing trends, and a rent roll weekly summary. A property analytics workspace provides tabbed views covering Overview, Financials, Rents, Occupancy, Operations, Trailing Financials, and Budget Comparison.

Users can configure and export branded PDF reports with page selection, cover pages, comment toggles, and write-up fields. Client-level branding controls include color scheme and logo upload. User and client administration handles access, password resets, role assignments, and client settings. Supabase authentication supports superadmin, admin, and regular user roles.

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Client Separation Is Structural,
_{Not a Filter Applied After the Fact}

The data layer connects directly to PostgreSQL through a read-only connection pool. Warehouse tables include final index property records, merged income statements, budget and proforma tables, merged rent rolls, leasing summaries, balance sheets, resident aged receivables, and daily operational tables. Analytics calculations run inside SQL within Next.js API routes, covering rolling periods, per-unit metrics, variances, historical series, and snapshot comparisons.

Client filtering enforces separation through client ID scoping, property-level filters, and role-based UI gating. Google Maps powers the property map. AWS S3 handles client logo uploads through signed URLs. PDF generation uses React PDF with html2canvas support.

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Tech Stack:

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The Application Layer
_‍_{That the Data Always Needed}

Contract intelligence that previously required manual assembly is now accessible as a governed, role-scoped operational interface. Finance can pull spend reports without requesting a data export. Operations has a renewals workspace with deadlines and status visible at once. Department leads can see their own vendor activity directly. Workflow requests follow a structured process tied to actual records. Renewal reminders run on schedule without manual intervention.

This is what a data application layer looks like in practice. Not a dashboard on top of a database. A system where users view data, act on it, manage workflows, and operate with confidence that what they see is accurate and current.

If your business data exists but your team is still coordinating around it manually, the gap is in the application layer, not the data itself.

Company Overview

A multifamily real estate investment firm managing portfolios across multiple clients and properties. Ember Capital relies on consistent, accurate performance data covering occupancy, rent collections, delinquency, leasing activity, and financial snapshots to support recurring investor reporting and asset management decisions.