Data cleaning by people who know the real estate data model.

This is the work we do most, and the work that decides whether everything else succeeds. Agency data is never clean — twenty years of duplicate companies, four spellings of the same tenant, phone numbers in five formats, addresses that match nothing official, and contacts nobody has touched since 2014.

Cleaning it is not a generic data exercise. It requires knowing the real estate data model well enough to tell which records are genuinely duplicates and which only look alike — that two entries at the same address are a landlord and a tenant, not one company entered twice; that a company name change after a sale is the same entity; that Suite 4 and Unit 4 are the same tenancy but Level 4 is not.

Get a general data vendor to do this and they optimise for row counts. They merge records that shouldn't be merged, flatten relationships that carry the commercial meaning, and hand back something tidy that has quietly lost the information your business runs on.

Why we can do it and others can't

We built and delivered ElevateIQ and rePrecinct. That means we designed the real estate data schema ourselves — entities, properties, tenancies, leases, campaigns and evidence, and the relationships between them. We clean against a model we understand rather than a spreadsheet we were handed.

We are cleaning agency data daily. This isn't a service line we added — it's the work underneath every product we've shipped.

What we actually do

Deduplication and entity resolution

Matching records that refer to the same real-world thing across inconsistent spellings, trading names, legal entity changes, mergers and manual entry error. Companies, contacts, properties and tenancies each resolve differently, and treating them the same is how data gets damaged.

Address standardisation and property matching

Normalising addresses to a consistent, verifiable format and matching them to the actual property — including the unit, suite and level distinctions that generic address tools flatten. This is what lets you group everything you hold about a single building.

Contact validation and enrichment

Email and phone validation, role and title normalisation, removal of decayed contacts, and enrichment where a record is worth keeping but incomplete. A database of 40,000 contacts where 12,000 bounce is worse than one of 28,000 that works.

Relationship repair

Reconnecting contacts to companies, companies to tenancies, tenancies to properties and properties to evidence. This is the layer that generic cleaning destroys and the layer that makes your data actually useful.

Migration

Field mapping and structured migration into whatever CRM you're moving to, with reconciliation so you can prove nothing was lost.

Specialist data sets

Transaction data cleaning

Real estate transactions leave traces across multiple systems — CRM deals, trust accounting ledgers, campaign registers and market evidence. We clean sales, leasing, property management and campaign records so buyer, seller, landlord, tenant, property and deal data resolve to the same entities, dates and amounts. The result is reliable deal history, pipeline and comparable evidence you can query without losing transactions in duplicates.

Financial records data cleaning

Commissions, rent rolls, invoices, disbursements and trust accounting entries often have mismatched property references, inconsistent entity names and legacy codes that no longer match your chart of accounts. We reconcile financial records back to the properties, tenancies and people they belong to, standardise descriptions and amounts, and flag where the source cannot be reconciled so your finance team can make decisions.

Lease and sales documentation data cleaning

Lease schedules, sale contracts, vendor statements, tenancy schedules and property files are usually stored as PDFs, spreadsheets or filenames that contain the only structured data. We extract and clean key fields — lease start and expiry, rent review dates, options, outgoings, purchaser and vendor details — and link them back to the right properties, tenancies and contacts so documents become searchable, not orphaned.

Centralisation

Cleaning is only useful if the data ends up somewhere consistent. We centralise cleaned records into a single source of truth — whether that is a CRM, a data warehouse, an internal database or a structured feed for AI platforms — with defined schemas, unique identifiers, audit trails and rules that stop the same mess from re-forming.

Why this is the prerequisite for AI

Language models don't fail loudly on bad data — they produce fluent, confident, wrong answers. If your CRM holds four versions of the same tenant, an agent asked to find your industrial occupiers in a corridor will return three of them and miss the rest, and nobody will notice until it costs something.

Clean, resolved, well-structured data is what makes retrieval reliable, makes agent output traceable back to a source record, and makes AI worth deploying at all. It's also the part every vendor skips, because it's slower and less impressive than a demo.

How an engagement runs

We start with a paid audit of your actual data — volumes, duplication rate, decay, structural problems, and what it can and can't currently support. You get that assessment in writing whether or not you continue.

From there we clean in stages, starting with the records that matter most to your current work, so value arrives early rather than at the end. You get the cleaned data and the pipeline, so it stays clean rather than decaying back to where it started within a year.