Research
Alston Analytics develops applied systems for environments where data is fragmented, inconsistently defined, and operationally unreliable.
Our work focuses on building systems that detect inconsistencies, generate explanations, and support decision-making without requiring clean or centralized data inputs.
We have developed initial prototypes across four areas.
1. Data Reconciliation Systems
Adaptive Data Consistency Engine (ADCE)
We have developed a prototype system that ingests multiple datasets representing the same underlying metrics and identifies inconsistencies across them.
The system compares aggregations, detects divergence patterns, and generates structured explanations of discrepancies, including likely causes such as definition mismatch, filtering differences, or data lag.
In early testing, the system successfully:
- Identified conflicting metric definitions across sources
- Surfaced inconsistencies that would not be visible in a single dataset
- Produced human-readable explanations tied to underlying data transformations
This work demonstrates the feasibility of automated reconciliation and explanation in multi-source reporting environments.
2. AI-Assisted Analytical Interfaces
Explainable Insight Layer (EIL)
We have built a prototype interface that generates structured explanations of changes in key metrics within analytical dashboards.
The system translates raw data into narrative insights while maintaining traceability to the underlying data model.
Current capabilities include:
- Identifying significant changes in metrics over time
- Generating explanations grounded in available dimensions
- Responding to natural language queries over structured data
The prototype prioritizes explainability and auditability, ensuring that generated insights can be traced back to specific data inputs and transformations.
3. Agentic Workflow Systems
Autonomous Reporting Agent (ARA)
We have developed an initial agent-based workflow system that executes recurring analytical tasks and monitors data pipelines for anomalies.
The system is designed to:
- Automate multi-step reporting workflows
- Detect failures or anomalies in data inputs
- Trigger validation and reconciliation processes
- Escalate uncertain cases for human review
Early implementations demonstrate that semi-autonomous systems can reduce manual effort while preserving transparency and control.
4. Learning Systems
PiqCue Adaptive Model
We have developed an initial adaptive learning prototype that adjusts explanations based on inferred user understanding.
The system tracks user interaction patterns and modifies explanation depth and sequencing in response to observed behavior.
This work extends our research into how AI systems can support comprehension, not just information delivery.
Research Direction
Current work is focused on validating these systems in real-world conditions and refining their ability to operate under incomplete, inconsistent, and evolving data environments.
Across all projects, our approach follows three principles:
Operate under real conditions
Systems are designed for environments with incomplete, inconsistent, and evolving data.
Prioritize explainability
Outputs must be traceable, interpretable, and defensible.
Integrate with existing workflows
Solutions are built to augment current systems rather than replace them entirely.
Alston Analytics is an eligible small business for federal SBIR and STTR programs. We are actively developing Phase I proposals and building research partnerships with academic institutions.
Organizations interested in research collaboration, pilot partnerships, or SBIR subaward arrangements are invited to contact us directly.
will@alstonanalytics.com