Services

MEL and data work, from design to handover.

Each service can stand alone or run together across a programme’s life. AI supports the work where it helps; people make the methodological calls and check every result.

MEL frameworks and programme design

Problem analysis and theory of change as the starting point, so indicators measure what the programme actually expects to change.

  • Problem analysis and theory of change facilitation
  • Results frameworks, logframes and indicator reference sheets
  • MEL plans, indicator selection and target setting
  • MEL sections and logframes for proposals

MIS and data platforms

Management information systems built on standard, documented structures, with validation at the point of entry so data is clean before anyone analyses it.

AI supportAI-assisted scripts handle data integration, designed and checked by a person.

  • ActivityInfo design: registries, forms, validation logic, roles and permissions
  • Platform selection, pilots and migration
  • Mobile data collection on KoboToolbox and ODK
  • ETL pipelines and Salesforce configuration

Survey design and sampling

Instruments and samples that hold up to scrutiny, documented well enough to be reproduced.

  • Baseline, midline, endline and annual outcome surveys
  • Stratified sampling with design weights and reserves
  • Adaptive sampling and sentinel monitoring
  • Reproducible draw records and bias diagnostics

Evaluation and research

Process and outcome evaluation with equity designed in and honest limits set in advance.

AI supportAI speech-to-text, translation and coding support for bilingual qualitative work, with every finding checked against the source.

  • Mixed-methods process and outcome evaluation
  • Focus groups, key informant interviews and thematic coding
  • A/B tests with research partners
  • Prevalence research with hidden populations
  • Terms of reference for external evaluations

Dashboards and analytics

Dashboards connected to the source system, with a written definition for every measure, so teams can check progress without waiting for a report.

  • Power BI (Power Query, DAX, star-schema models) and Tableau
  • Automated refresh through platform APIs
  • Correlation and outcome analysis in Python and R
  • Modelling for programme re-scoping

Data quality and funder reporting

Data quality built into the workflow, and reporting that stays honest under pressure.

  • Plausibility bounds applied on every refresh
  • Data quality worklists fixed at source
  • DQA plans and checks across survey years
  • Funder indicator reporting and corrected analysis

Data protection and governance

Standards for people whose data is collected, and for the organizations that hold it.

  • Do No Harm, informed consent and personal-data standards
  • Data-sharing agreements between partners
  • Scoped, read-only data access
  • Rules for AI use on client data

Capacity building

Training by doing, so the in-country team can run and extend the work.

  • Hands-on training in ActivityInfo and Power BI
  • Handover guides a new analyst can pick up
  • Onboarding and one-to-one support
  • Cross-programme learning calls

See the services in practice

Short case studies from current engagements and earlier roles.