Automation
Reduce manual workflow friction
We use agents, automation, and orchestration patterns to reduce manual handoffs, simplify business processes, and help teams move work forward with less friction.
Agents, machine learning, and production delivery
We use agents and machine learning to improve business processes, reporting, and operational decisions
CraftingData combines research-oriented work with production delivery to build better solutions for organizations dealing with fragmented workflows, hard-to-use data, and decisions that need stronger analytical support. The focus is practical: use agents, ML, analytics, and reporting systems where they create better operating outcomes.
- Use agents and ML to streamline business processes
- Build reporting and semantic models that support clearer decisions
- Connect research depth to production systems teams can actually use
What we do
We build better solutions by applying agents, machine learning, analytics, and production engineering to real business problems
CraftingData is a consulting and solution design practice. We help organizations turn fragmented workflows and disconnected data into systems that are easier to operate, easier to report on, and easier to use for decision-making. The core focus is how we leverage agents and ML, alongside research and production work, to deliver stronger solutions.
Typical starting point
Most engagements begin with one of three problems: too much manual coordination, too little visibility across systems, or a decision process that needs stronger analytical support.
Analytics
Turn raw information into operating clarity
We unify siloed data into reporting, dashboards, and semantic models that help operators and leaders understand what is happening, what changed, and what needs action next.
Applied AI
Use AI where it improves the business process
We apply agents, LLMs, classic ML, and time-series methods when they improve prediction, reasoning, summarization, or decision support in a measurable way and can be carried into practical production use.
Why clients hire us
Research and production work are both in service of better decisions
The point is not AI for its own sake. The work is designed to use agents, ML, reporting, and production delivery to improve throughput, reduce manual effort, increase visibility, and make operational decisions easier to trust.
Operational efficiency
Less manual coordination, fewer hidden steps
In prior CVO and enrollment work, automation was used to ingest operational data and feed a semantic model in Power BI so teams could move from raw status information to clearer decisions.
Decision quality
Better visibility for teams and leadership
When workflow data is fragmented, people make slower and weaker decisions. We build systems that expose state, outliers, and progress clearly enough for managers and delivery teams to act.
Featured proof
Healthcare automation and reporting case study
See how EDI and HL7 transaction flows were ingested, modeled, and surfaced for billing, finance, and executive visibility.