Services
Reproducible Data Pipelines
We design and implement reproducible data pipelines that turn raw data into dependable, analysis-ready assets. Reproducibility ensures that your analyses and reports can be recreated consistently, by anyone, at any time, building long-term trust in your results.
Our pipeline development focuses on four key pillars:
- Automation: Streamline data cleaning, transformation, and reporting to eliminate manual errors.
- Version Control: Track every change in code and data for transparency, accountability, and rollback capability.
- Documentation: Provide clear, accessible documentation so pipelines are easy to maintain and extend.
- Scalability: Build infrastructure that grows with your data volume and organizational needs.
R | Python | SQL | Cloud | Quarto | R Markdown | Dashboards | Data Visualization
Causal Inference & Impact Evaluation
Move beyond correlation and uncover what truly drives your outcomes. We help organizations design, implement, and analyze studies that measure the causal impact of programs, policies, or interventions.
Our expertise includes:
- Experimental Design: Plan and analyze experiments, such as A/B tests or randomized trials, that rigorously test intervention effects.
- Quasi-Experimental Methods: Apply designs such as difference-in-differences, instrumental variables, and regression discontinuity when experiments aren’t feasible.
- Observational Causal Inference: Use approaches like propensity score matching or coarsened exact matching to estimate causal effects from real-world data.
Causal Inference | Interrupted Time Series | Regression Discontinuity | Propensity Scores
Statistical Software Development
We build custom statistical software in R and Python that empowers teams to work faster, more accurately, and more reproducibly. Whether you need internal tools or production-grade packages, we can help you scale your analytical workflows.
Our work includes:
- R Package Development: Consolidate your analytical code into reusable, professional-grade R packages.
- Python Library Development: Create modular, documented libraries for modeling, automation, or data engineering.
- Workflow Automation: Replace repetitive manual processes with robust, maintainable scripts and functions.
- APIs & Web Applications: Wrap models and tools into user-friendly interfaces for broader team access.
R | Python | Package Development | Automation | Documentation
Machine Learning & Predictive Analytics
We develop machine learning solutions that turn data into actionable insights and predictive capabilities. Our consulting process guides you from exploration through deployment, ensuring your models generate measurable value.
Key areas include:
- Predictive Modeling: Forecast outcomes like churn, demand, or risk to drive proactive decision-making.
- Classification: Build models for detecting fraud, tagging content, or identifying customer segments.
- Natural Language Processing (NLP): Extract meaning from text through sentiment analysis, topic modeling, or generative AI.
Random Forest | XGBoost | Clustering | Deep Learning | Natural Language Processing
Evidence Synthesis
We conduct systematic and quantitative evidence synthesis to provide clear, actionable insights drawn from the best available research. Our process helps organizations make data-driven decisions grounded in the full body of evidence.
We specialize in:
- Systematic Reviews: Comprehensive, transparent reviews addressing targeted questions with scientific rigor.
- Meta-Analysis: Combine and statistically analyze findings across studies to derive stronger conclusions.
- Scoping Reviews: Map the research landscape to identify knowledge gaps and inform strategy or future research.
Research Synthesis | Meta-Analysis | Literature Review | Scoping Review | Systematic Review
AI & LLM Development
We help teams evaluate, design, and implement AI systems and large language models (LLMs) that add real value without unnecessary complexity. Our approach balances innovation with ethics, transparency, and human oversight.
Our services include:
- AI Strategy & Assessment: Identify high-impact opportunities and determine whether to use existing tools or build custom solutions.
- Custom LLM Development: Train or fine-tune models for summarization, retrieval, or domain-specific use cases.
- Human-in-the-Loop Design: Build systems that keep people in control while AI accelerates decision-making.
- Evaluation & Optimization: Benchmark model performance, monitor drift, and enhance fairness and explainability.
AI | Large Language Models | Fine-Tuning | Prompt Engineering | AI Agents | Natural Language Processing
Contact Us
Get in touch to discuss how we can support your data and analytics goals.