Impact Measurement Platform

Part of the worlds-biggest-software-project initiative.

An open-source, AI-native platform that solves the evidence continuity problem for nonprofits -- linking baseline, programme, and outcome data under persistent participant identities so organisations can demonstrate real impact, not just count activities.

Nonprofits and social enterprises face mounting pressure from funders, boards, and regulators to prove their programmes produce measurable outcomes. Yet fewer than a third do it effectively, because baseline assessments, participation records, and follow-up surveys typically live in separate tools with incompatible identifiers. The Impact Measurement Platform unifies the full evidence lifecycle -- from theory of change design through data collection to funder-ready reporting -- in a single system purpose-built for outcome measurement.

Why Impact Measurement Platform?

Evidence continuity is broken. Most organisations collect baseline and follow-up data in different tools, making it impossible to link what changed for individual participants. Existing solutions like Salesforce Nonprofit Cloud require extensive customisation to achieve even basic outcome tracking, and platforms like Bonterra Apricot tightly couple impact measurement to case management workflows that may not fit every organisation.
Qualitative evidence goes unread. The majority of evidence generated by human-services nonprofits is qualitative -- open-ended survey responses, case notes, interview transcripts -- yet no incumbent analyses it systematically at scale. Manual thematic coding takes months.
Incumbent pricing excludes smaller organisations. Commercial platforms like Sopact, Bonterra, and Salesforce carry significant licensing and consulting costs. The open-source data collection tools that nonprofits can afford (KoboToolbox, ODK) lack outcome aggregation, reporting, and analysis layers entirely.
Framework alignment is manual. Mapping internal programme indicators to external frameworks like IRIS+, SDGs, or funder-specific templates is a repetitive, error-prone exercise that most platforms either ignore or implement superficially.
No platform addresses causal attribution. None of the reviewed incumbents provide tools for estimating whether outcome changes are attributable to programme activities versus external factors.

Key Features

Theory of Change and Programme Design

Visual theory of change builder mapping inputs, activities, outputs, outcomes, and long-term impact
Pre-built logic model templates for common programme types (workforce development, housing, youth mentoring, health)
Linkage of each stage to measurable indicators and data collection instruments
Adaptive management feedback loops that surface whether theory of change assumptions hold true

Data Collection and Participant Tracking

Configurable survey and assessment forms deployed at baseline, mid-point, and endline
Persistent participant identifiers linking records across potentially years-long programme cycles
Mobile-friendly data collection with offline capability for field environments
360-degree feedback surveys for multi-perspective assessment (self, peer, supervisor)
Probabilistic participant matching and deduplication for re-enrolled participants

Outcome Measurement and Analysis

Outcome indicator definition with target-setting and progress tracking
Roll-up of individual outcome records to programme and organisation levels
Distance Travelled statistical change calculations
AI-powered qualitative data analysis: clustering, thematic coding, and sentiment analysis of open-ended responses and case notes
Real-time anomaly detection flagging unexpected outcome trends across cohorts

Framework Alignment and Reporting

Mapping of internal indicators to external frameworks including IRIS+, SDGs, and funder-specific templates
Automated generation of impact reports in funder-specified formats
Combined narrative and quantitative outputs with organisation branding
Export to PDF, Word, and Excel formats
Natural language query interface for non-technical staff to generate visualisations

Integration and Data Interoperability

Integration with existing case management systems (Apricot, Salesforce, Efforts to Outcomes) via APIs
Batch import/export for large-scale programmes
CSV import and open API support
Composable data models allowing organisations to define outcome hierarchies without software customisation

AI-Native Advantage

The platform uses large language models and machine learning throughout the evidence lifecycle, not as bolt-on features. Qualitative data -- open-ended survey responses, case notes, interview transcripts -- is automatically clustered, themed, and summarised, turning months of manual coding into minutes of automated analysis. Probabilistic record linkage uses name, date-of-birth, and location embeddings to match and deduplicate participants across programme cycles. Time-series models provide early warning when cohort-level outcome metrics deviate from expected trajectories, and natural language report generation combines quantitative outcomes with qualitative evidence into funder-tailored narratives.

Tech Stack & Deployment

The platform targets self-hosted and cloud deployment modes to accommodate nonprofits with varying infrastructure and data sovereignty requirements. Offline-first mobile data collection (progressive web app or native app) supports field environments with unreliable connectivity, syncing automatically when a connection is restored. The data model supports pseudonymisation, consent management, and data subject access request workflows compliant with GDPR and US state-level privacy laws. Integration with external systems is achieved through REST APIs and webhook support, following patterns established by KoboToolbox and ODK in the nonprofit data collection ecosystem.

Market Context

The global impact investing market exceeded USD 1.5 trillion in assets under management by 2025, and the accountability requirements flowing from that capital increasingly reach grantees and investees. Government funders at federal, state, and local levels now require evidence-based programming, making outcome data a prerequisite for funding rather than a differentiator. Primary buyers are programme managers and evaluation directors at mid-sized nonprofits and social enterprises -- organisations large enough to need structured impact measurement but too resource-constrained for enterprise platforms like Salesforce or dedicated consulting engagements.

Project Status

This project is in the research and specification phase. Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.