AI Enablement

Data ingestion

Your data is scattered across drives, spreadsheets, email, SMS, your property management system, your CRM, and your accounting software. Some of it is not digital at all: receipts, checks, bills that arrive in the mail, executed contracts sitting in a filing cabinet.

We take stock of every source, map out how to pull each one in, and digitize what needs digitizing. Then we build the storage that centralizes it and the pipelines that keep it flowing, so ingestion keeps happening without anyone having to remember to do it.

Structured extraction

Data in one place is not the same as data you can use. Most of it is still trapped inside PDFs, spreadsheet tables, inspection reports, and scanned contracts with handwriting in the margins.

We apply OCR, image recognition, and LLM-based extraction to pull that information out in a structured form that both your software and your AI systems can read.

Context engineering

This is the most important component of the Data Layer and what gives it an actual understanding of your organization. Extracted data tells a system what exists. Context engineering tells it what those things mean and how they relate to each other: which entity owns which asset, which policy applies to which property, which approval a work order needs at which dollar threshold.

We use retrieval systems, vector databases, semantic search, and knowledge graphs to model that structure accurately, then build the search and retrieval tools that let people and agents get to what they need quickly and without burning money on every query.

This part is hands-on. We work directly with your team, because your entity structure, your approval thresholds, and your way of doing things are not written down in any document we can ingest.

Evaluations

The only way to know whether the system is any good is to test it repeatedly against a standard you set. Evaluations are those tests. They run after every iteration, so accuracy is measurably climbing during development and measurably not slipping once you are in production.

They test how well the system answers the questions your team actually asks, built around your workflows rather than a generic benchmark. We define them with your team up front and agree on what success looks like before we start building.

Provenance

A system that gives good answers but cannot explain them is a system nobody trusts and nobody can improve. Provenance means tracking where each piece of information came from, which sources a given answer drew on, and what steps the system took to get there.

For your team, that means citations on every answer. For you, it means you can see what the system is doing, audit it when something looks off, and catch a wrong answer before it becomes a wrong decision.

Permissions and audit logs

Your business already has information boundaries. An owner sees their own properties. A regional manager does not see another region's financials. The data layer has to respect all of it, or it becomes a liability the first time someone asks a question they should not have been able to ask.

We map your permission structure early, before anything gets built on top of it, and we log activity so you can see usage patterns, audit access, and spot anything that looks wrong.

Company-wide document search

Ask for a lease clause, owner report, vendor contract, insurance policy, rent schedule, or historical invoice and get the answer with a source link.

Daily briefings

Summarize what changed across deals, properties, maintenance, owner communications, or sales activity.

Financial Review

Gather monthly financials, calculate budget variances, flag anomalies against historical context, and hand your team a review that's ready to analyze.