Survey quality control
Design and run agent-assisted checks for fieldwork quality, enumerator patterns, paradata, comments, and post-intervention follow-up.
Development data | Official statistics | Agentic AI
Impact Engines helps development organisations, national statistics offices, and survey teams turn AI from a general-purpose chat tool into practical workflows for survey quality, data cleaning, evidence, and statistical production.
What we do
We work where AI meets the practical realities of development and statistical production: CAPI platforms, messy datasets, fieldwork pressure, sensitive microdata, reproducible scripts, and reviewable outputs.
Design and run agent-assisted checks for fieldwork quality, enumerator patterns, paradata, comments, and post-intervention follow-up.
Use AI agents to prepare review logs, detect issues, translate and code open ends, and generate reproducible Stata or Python workflows.
Support national statistics and development data teams with standards-aware drafting, tabulation, documentation, and controlled review workflows.
Move from advice to working systems with privacy-aware, human-in-the-loop designs suitable for sensitive development and statistical data.
Why this niche is different
National statistics offices, donor-funded projects, and survey teams work with sensitive data, complex questionnaires, multilingual fieldwork, and a high bar for transparency. AI can help, but only if it respects those realities.
Samoa Agriculture Survey 2025 cleaning pilot: reproducible Stata pipeline, review logs, and human-in-the-loop decisions.
Survey quality control workflows for enumerator anomalies, straight-lining, heaping, GPS duplication, and comment synthesis.
Open-source and prototype tools that show practical AI implementation rather than only advisory slides.
Open-source demonstrations
Impact Engines is not trying to sell another SaaS subscription. The tools and prototypes are proof points: examples of how practical AI can support development and statistics workflows in inspectable ways.
Latest thinking
Survey data cleaning is high-value, messy, and under pressure. Agentic workflows can prepare issue logs, callback sheets, open-end coding suggestions, and reproducible Stata implementation while keeping human decisions visible.
Fieldwork QCGood fieldwork quality control is not just a dashboard. A dedicated agent can review questionnaires, write checks, compare enumerator patterns, analyse comments, and keep monitoring after intervention.
Agentic AIOpenClaw points to a different kind of AI adoption for development teams: local workspaces, durable tasks, files, scripts, and agents that can keep working beyond a single prompt.