Org creation and collaborations
- Org creation, org admin invitation
- Admin settings
- Whitelisting domains for Collaborations
- Role-based permissions and data controls
User management
- User onboarding, Role and permission assignment
- SSO migration and compliance
- Clear activation and deactivation flows
- Partnered with PMs and other designers to audit legacy tools, gather internal feedback, and align with SOC/regulatory needs
Design system contributions
- Reviewed with the platform design system team before implementation
- Created hand-off assets like annotation cards
- Created custom UI elements like onboarding components, date pickers, and icons when system didn’t have the desired component
Context
Many of Altus' users clients relied on outdated tools, manual processes, and disconnected systems. Lease data was copied over by copying and pasting, or reviewed cell by cell.
The challenge wasn’t just to design a better experience, it was to bridge old workflows with a modern interface, ensuring users could adopt improvements without breaking their processes or systems.
Business need:
- Reduce manual time spent by appraisers doing manual tasks (time savings - aprox $)
- Jump start the appraisal process for ARGUS Enterprise users and Aquasition users
We conducted in-depth research, interviewing appraisers and analyst to understand their workflow around lease ingestion, sending our surveys, and gathering examples of the type of documentation they receive and what how it is used on ARGUS Enterprise.
Key insights:
- Appraisers rarely start with a brand new AE model, 98% of the time they received a previous model without key assumptions, which meant we didn't just need to build a lease ingestion tool, but a comparisson tool
- Comparing lease changes cell-by-cell was tedious and often delegated to junior analysts
- Lease documentation came in various formats but mainly PDF and Excel)
- Acquisition teams needed speed over depth, a quick pulse on value, not full AE model fidelity
Gathering examples of leases, we reviewed layouts, common data points and file types, setting up a Figjam with a birds eye view of all the documentation examples, noting commonalities and anomalies. This helped us articulate to the development team what we should prioritize, and what the system should look out for.
The goal of the lease ingestion project was to streamline how users uploaded and mapped lease data into the Altus Intelligence Platform, reducing the need for manual entry, improving data accuracy, and enabling faster modelling into ARGUS Enterprise.
We identified three core user needs:
“Once I receive the files, I just need to review the data is correct...row by row.”
– Consultant
“Sometimes different clients have a different name for this”
– Analyst
“I want to make sure the data I'm uploading is reviewed before I override all my model data.”
– Analyst
We also had to work within a few constraints:
- The system only supported table-based formats (like Excel), not PDF parsing or data integrations
- Header matching relied on a keyword dictionary, not AI/OCR
- Models were stored and updated in ARGUS Enterprise, so the workflow needed to fit legacy tech and data flow requirements
Given the constraints set by the engineering and product team, we explored a wizard-style flow that supported guided upload, semi-automated field mapping and data validation prior to submitting data to a new, or existing AE model.
Design goals:
- Break a complex process into manageable, linear steps
- Provide clear orientation at each stage (e.g., where the user is in the process and what comes next)
- Minimize user errors by guiding them through a structured, predictable flow
Design goals:
- Build trust in automation by showing how the system made its matches
- Empower user control so they can easily fix incorrect mappings
Design goals:
- Surface issues early so users don’t waste time progressing with broken inputs
- Help users self-correct with clear, contextual messaging (unmapped fields, formatting issues)
- Prevent data quality issues before lease information is submitted to AE
The goal was not to reinvent the ingestion process, but to optimize clarity and trust within a technically constrained system, helping users confidently prepare data for downstream use in ARGUS Enterprise.
Given the technical complexity of integrating with ARGUS Enterprise and handling structured data files, close collaboration with engineers was critical.
- Provided alternatives for edge cases not covered in early specs
- Annotating each step of the Figma flow with interaction notes and edge case guidance (e.g., duplicate fields, unsupported formats)
- Meeting with the lead front-end developer to ensure component logic matched user expectations
- Testing early staging builds and flagging visual inconsistancies and bugs
We conducted early walkthroughs of the ingestion flow with internal subject matter experts and a few trusted client advisors. While the ingestion flow improved accuracy and efficiency for valuation workflows, acquisition analysts, especially those underwriting new deals, needed a faster way to assess viability without having to prep detailed lease tables.
To address this, I created a quick prototype using Claude AI to demostrate this:
- Quick property-level IRR calculations
- The option to upload lease tables for refined estimates
- A simplified UX flow geared toward speed and directionality, not completeness or legacy tool reliance
This prototype explores how we might serve acquisition teams earlier in their funnel, while still offering a path to upload detailed lease documentation.
