Real Estate Due Diligence
Real Estate risk sits scattered across reports, and gaps surface after signing.
Agents surface the missing pieces, expired certificates and breaches, with a first CAPEX read before commitment.
See how it worksHow we build
The app is the easy part. The foundations decide whether it holds.
An AI app is only as reliable as the data and processes under it. Every engagement runs through the same four steps.
01
Frame the job
We isolate the one bottleneck the app removes, and confirm the data and IT underneath can carry it.
02
Solid ground
We put the right tools in place and make the data reliable.
An agent on ungoverned data runs fast, confident, and wrong.
The app03
Build the app
One agent, one job, every answer sourced and checkable.
04
Run and extend
We operate it, measure the impact, and move to the next job.
Use Cases
One capability. Many jobs.
Each app takes on one operational bottleneck (readingdocument-heavy work, or optimizing decisions under your real-world constraints) and returns structured, checkable output. The job changes, the discipline does not.
Route & Field Optimization
Field routing built by hand by one planner, on constraints no off-the-shelf tool can model.
We build a custom AI tool that optimizes routes on your real constraints: skills, vehicles, zones, SLAs.
See how it worksData Room Analysis
Hundreds of deal documents to read by hand, against the clock.
Agents read the full room on set criteria and flag what matters, sourced to each document.
Lease & Portfolio Abstraction
Lease terms and break options buried in PDFs nobody reads across a portfolio.
Agents extract the key clauses into one consolidated view across assets.
Claims & Policy Analysis
Policies and claims files to review consistently, at volume, under regulation.
Agents triage on intake, cross-check terms against each claim, and flag gaps for the adjuster.
Compliance & Supply-Chain Monitoring
Certifications and supplier contracts spread across thousands of components.
Agents track specs against current standards and flag non-conformances early.
Executive AI, Data & IT Diagnostic
A mandate to "do AI" with no read on whether the data and IT can carry it.
A working session that maps current maturity and the questions to settle first, including against us.
Agentic Foundation
Coding agents generate faster than teams can review. Duplication and vulnerabilities slip into the codebase unseen.
We industrialize agentic AI on four guardrails (test coverage, readable architecture, CI/CD, human review) so velocity never costs you control.
Frequently asked questions
What teams should know before building an AI solution
Stratos builds applied AI around a defined operational problem, measurable baseline, usable data and clear human controls.
Which business problems are a good fit for AI?
The strongest candidates are repetitive or high-volume workflows with identifiable inputs, outputs and review rules. Examples include document analysis, classification, extraction, drafting, prioritisation and decision support. A vague ambition to “use AI” is not enough.
What data and systems are required?
A project normally requires representative examples, access to the systems that hold the source information, and a clear view of permissions and data quality. Integration may involve APIs, databases, document repositories, CRM or ERP systems.
How is the return on investment measured?
The baseline is defined before development: time spent, processing cost, error rate, throughput or conversion. The solution is then tested against that baseline on a controlled scope before wider deployment.
How are security and human oversight handled?
Access rights, logging, data boundaries, validation steps and escalation rules are designed with the workflow. High-impact outputs remain reviewable, traceable and subject to human approval where appropriate.
When is an AI solution not appropriate?
AI is usually a poor fit when the process is unstable, the volume is too low, no reliable examples exist, or an incorrect output cannot be reviewed safely. In those cases, process redesign or conventional automation may be the better investment.
It starts with one job to remove.
We map it, build the app on solid foundations, and keep it reliable.
Book a working session