Ship lease, deal, and property management software without production surprises
We test real estate and PropTech platforms — deal pipelines, lease administration, property management, and the AI assistants built into them — so QA-to-production parity gaps, flaky automation, and bad data migrations get caught before your brokers and property managers do.
- US, Canada & UK overlapWorking hours planned around your team, not just ours.
- One point of contactA named lead owns the engagement end to end — no ticket queue.
- NDA on every engagementSigned before we see a single ticket or line of test data.
- Scale up or down monthlyNo multi-year lock-in — resize the team as the release calendar changes.
Who this is for
What you get
- Production automation stabilization: we trace failures to real QA-vs-production configuration and UI differences, not just rerun flaky tests, until pass rates hold in production
- Scenario-based testing for in-app AI agents and chatbots — validating both the action taken and the data returned against your system of record
- Structured test summary reports for every ticket in a release: scope, test cases with screenshot evidence, defects logged, and a clear sign-off recommendation, traced strictly to the ticket
- Business-rule and date-logic validation — at-risk status thresholds, backward date calculations from a completion stage, and cross-record linking across different navigation paths
- Lease and property dataset audits with Python and pandas before migration — blank-field detection, cross-column failure patterns, and a precise fix-before-import list
- Brand theming and WCAG AA contrast QA for multi-tenant onboarding, with a post-setup checklist for hover states and logo rendering
- User guides and knowledge-base articles generated from screen recordings for onboarding and admin workflows
Timeline & process
Requirements & environment audit
We read every ticket end to end, flag ambiguity before testing starts, and map where QA and production configurations are likely to diverge.
Test design
Positive, negative, and edge-case test cases are written, each traceable to a line in the ticket or business rule.
Execution & automation stabilization
We run tests in the target environment, compare against production where it matters, and root-cause automation failures instead of rerunning them.
AI agent & data validation
Scenario-based testing for any in-app AI assistant, plus a dataset audit for records heading into a migration.
Reporting & sign-off
Captioned screenshot evidence, defect reports, and a plain pass, fail, or conditional-pass recommendation for every ticket.
What we need from you
- Access to your QA/staging and, where relevant, production environments with realistic (or masked) test data
- The list of tickets, modules, or tenants in scope, or 30 minutes with the engineer who built them
- Read access to your issue tracker (Jira or equivalent) so test cases can trace back to the ticket
- A named stakeholder available for sign-off decisions on each release
Proof, not promises
Not in scope, so there are no surprises
- Building or configuring the platform, its workflows, or its AI agent — we test what is built, your engineering team remediates
- Full penetration testing or a formal security audit — UI and access checks are not a substitute for a dedicated security engagement
- Coverage for modules, tenants, or integrations not confirmed in scope at kickoff — added scope goes through a change order
- Hypercare or production support beyond the agreed post-release window
- Legal or compliance certification — we test against the business rules and requirements you provide, we don't issue regulatory sign-off