Industry
Real Estate / Data Operations
Company Size
Withheld
Engagement
Agentic Data Aggregation and Filtering
Timeline
Withheld
The Challenge
BEFORE STATE
Hundreds of Data Sources. Individual Judgment at Every Step. No Path to Scale.
The client's operations depended on a manual data aggregation process requiring staff to interact with hundreds of disparate data sources in varying formats and levels of reliability. There was no single system capable of performing the end-to-end function: data retrieval, filtering, and output were all handled piecemeal, requiring significant human inference at every step just to make sense of the fragmented information.
The consequences were significant: high cognitive load on staff, repetitive manual steps, inconsistent outcomes, and poor visibility into process health. Because filtering decisions were made through individual human judgment rather than standardized rules, results varied depending on who performed the work and when. The process was also difficult to reproduce or scale, creating a ceiling on how much the organization could grow without adding headcount.
The Approach
PHASE 1
Discovery and Process Mapping
Axiant documented the pre-solution state in detail: a five-step manual workflow in which staff retrieved data from hundreds of small platforms, reconciled incomplete or inconsistent records, made filtering determinations through further manual research, and manually assembled the finalized output. This discovery surfaced the core problem: no part of the process was standardized or machine-executable as designed.
The mapping output established the full scope of manual effort at each stage, identified where human judgment was being applied to work that could be governed by explicit rules, and drew a clear line between what required automation and what required redesign before automation was viable.
PHASE 2
Standardization and Design
Before any automation was built, Axiant defined the filtering and inference rule sets that would govern the solution. This included establishing clear, documentable criteria for property qualification and ownership classification, along with specific inference logic to handle edge cases that fell outside standard patterns.
The human-in-the-loop conditions were defined with equal precision: escalation would be intentional and explainable, not a fallback for poorly defined automation. The design phase produced the specification the build phase executed against, ensuring that every decision the system would make had a defined rule behind it before a single component was configured.
PHASE 3
Build and Configuration
The solution was built around two core system roles: an Orchestrator that manages workflow state and sequencing, and an Inference Agent that interprets data intent and completeness. The architecture incorporated a workflow orchestration engine, a large language model for inference, a database and data query layer, role-based identity and access management, a content delivery stack, and a logging and observability stack.
Finalized data sets were designed to be universally consumable by the client's internal processes, eliminating the manual assembly step that had previously required staff to format and reconcile outputs before they could be used downstream. Every automated decision produces a traceable rationale, and the full process history is reconstructible for any run.
PHASE 4
Testing, Training and Go-Live
Details for this phase — including the parallel-run period, change management approach, staff training, and time from build to go-live — are being finalized and will be added prior to full distribution.
PHASE 5
Optimization and Handoff
Post-launch optimization, monitoring configuration, and knowledge transfer details will be documented here following the completion of the handoff period.
The Results
AFTER STATE
96% Faster. Under 48 Hours. Consistent Outputs Where None Existed.
Average processing time dropped from a variable, multi-day manual cycle to 48 hours or less, a reduction of 96%. Data accuracy on finalized outputs reached 90% or greater, replacing a process in which accuracy was inconsistent and unmeasured. Human escalation auditability, which had not existed in the manual process, is now fully traceable for every case the system handles.
| Metric | Before | After |
|---|---|---|
| Average processing time | Multi-day, variable | 48 hours or less |
| Data accuracy on finalized outputs | Inconsistent / unmeasured | 90%+ accuracy rate |
| Human escalation auditability | Not present | Full rationale traceable |
What Is Next
Expansion planning details will be added as the next phase of the engagement is confirmed.