We develop the data and reporting foundations for research and analytics engagements. By connecting external information with customer, financial, and operational records, we help you investigate opportunities, understand performance, and evaluate business choices.
Data sourcing, enrichment, and dashboards are developed within the engagement to support the analysis, explain the findings, and give your team a consistent basis for ongoing review.
We assess which commercial, public, and internal sources can address the question, examining their relevance, coverage, and limitations.
We collect information through research, APIs, and web scraping, enrich company records, and combine sources into a coherent analytical dataset.
We use large language models (LLMs) to automate information extraction, classification, and enrichment from documents, websites, and existing records, with validation of the resulting data.
We reconcile identifiers, reporting periods, and metric definitions so that comparisons reflect the business conditions being examined.
We develop visualizations, performance views, and executive presentations that connect the analysis to its commercial implications.
We document workflows and refresh processes so your team can revisit findings, track relevant measures, and assess changes over time.
Examine what the available data can answer, identify missing evidence, and assess which internal or external sources can fill the gaps.
Assess where LLMs can extract, classify, and enrich information for the engagement, with validation to identify errors and outputs that need further review.
Bring consistent measures, documented workflows, and refreshable reporting into the engagement so your team can update the evidence and see how the findings change.
Examples of how we turn scattered records and difficult sources into data people can analyze and reuse.
For MIT Sloan, we built and validated a 13-year research panel covering 326 companies. Entity matching, source documentation, and checks on missing values made financial and innovation records comparable and provided an auditable foundation for research.
Read Case Study
We used optical character recognition and language-model-assisted cleaning to turn image-based court filings into structured analytical records. The resulting ten-year database supported geographic analysis, time-series research, and market sizing within a broader diligence workstream.
Read Case Study
We built refreshable Power Query workflows combining shipment, pricing, cost, and financial records. Standardized identifiers and documented allocation assumptions supported consistent reporting across company, plant, customer, and product views.
Read Case Study
Evaluate AI initiatives, estimate business outcomes, and understand patterns through statistical modeling, forecasting, segmentation, and impact analysis.
Understand customer needs, buying decisions, and product preferences through survey design, quantitative analysis, pricing research, and customer segmentation.
Estimate revenue potential, compare customer groups, and assess upsell and cross-sell opportunities through account-level modeling and customer analysis.
