Evidence services
AI-assisted monitoring, evaluation, and reporting
Connect monitoring records, evaluations, and field evidence to source-linked analysis and report drafts, with validation, human review, and learning built into the workflow.
The client problem
Connect the records to the decisions they need to inform.
Monitoring data, evaluations, field observations, and reporting requirements often sit in separate systems. Teams need to understand what has changed, check the evidence, and prepare reporting that keeps context and uncertainty visible. The work should also inform what to ask and assess next.
Who the service is for
Monitoring, evaluation, and learning teams
Bring indicator records, evaluation findings, and quality checks together for interpretation and learning.
Programme and portfolio teams
Review emerging signals alongside local context, agree follow-up actions, and track the questions those actions raise.
Reporting and evidence owners
Prepare source-linked reporting drafts against agreed requirements, with named reviewers and approval before use.
Inputs and source material
Start with the records, definitions, and context.
Agree what material can be used and what it represents. The scope can combine structured records with documents and local knowledge, with permissions and gaps kept visible.
- Monitoring and indicator records
- Agreed indicators, definitions, baselines, reporting periods, and programme or operational records.
- Evaluations and project documents
- Evaluation findings, implementation reports, plans, and supporting source material authorised for the task.
- Field and contextual knowledge
- Local observations, stakeholder perspectives, and delivery constraints shared with appropriate permission.
- Reporting and review requirements
- The questions, indicator rules, report structure, review criteria, and responsibilities agreed with your team.
The workflow
From agreed evidence to reviewed reporting and action.
The workflow connects source preparation, checks, analysis, drafting, and human decisions. Its scope and acceptance criteria are agreed for each engagement.
Frame the monitoring question
Agree intended users, decisions, indicators, definitions, periods, and reporting requirements. Set source permissions and review responsibilities before analysis.
Connect and structure the evidence
Bring the agreed records together, retaining source references, dates, units, and context. Flag missing material and inconsistent definitions.
Check, analyse, and draft
Apply agreed quality checks, compare signals in context, and prepare evidence-linked views or report drafts. Keep calculation checks, gaps, and uncertain interpretations visible.
Review, decide, and learn
Specialists check findings and drafts. The responsible team approves use, records actions and open questions, and feeds the learning into the next monitoring or evaluation cycle.
Expected outputs
Outputs that carry their sources and review status.
Select deliverables for the agreed task and audience. Outputs should make it possible to inspect the evidence, challenge the interpretation, and see what still needs review.
Reviewable monitoring view
A structured view of agreed indicators, periods, context, and unresolved data-quality flags.
Source-linked report drafts
Narratives and evidence summaries connected to supporting records and prepared for human review before release.
Validation and learning record
Definitions, checks, limitations, review decisions, follow-up actions, and questions for the next assessment.
Validation steps
Check the evidence before it becomes a finding.
Validation connects data quality, calculation checks, source verification, and specialist judgement. The checks and acceptance criteria are agreed for the workflow.
Check records and definitions
Review missing values, duplicates, dates, units, and indicator definitions. Resolve or flag inconsistencies before comparing periods or programmes.
Verify calculations and source links
Check aggregates and extracted findings against the original records and passages. A citation does not establish that the calculation or interpretation is correct.
Review interpretation and reporting fit
Assess context, uncertainty, evidence strength, and alignment with the agreed reporting requirements. Keep observed results separate from causal claims and unsupported conclusions.
Human responsibilities
People remain accountable for interpretation and release.
Agree who owns the records, who reviews the analysis, and who can approve a report or action before the workflow is used.
Evidence owners agree the boundaries
Confirm source permissions, indicator definitions, reporting periods, access needs, and who is responsible for correcting the records.
Specialists check the findings
Monitoring, evaluation, subject, and local experts review calculations, source links, context, and interpretations before findings are used.
The responsible team approves use
Named owners sign off reports and consequential actions. AI-generated text remains a draft until approved; it is not submitted or published automatically.
Monitoring, decisions, and future assessments
Let reviewed evidence inform the next cycle.
Review the signal
Ask what changed and whether data quality, timing, or context could explain the pattern. A monitoring signal does not establish impact.
Agree an action
The responsible team decides whether to investigate, adapt delivery, gather more evidence, or keep observing, with a named owner.
Record the reasoning
Keep the sources, assumptions, review decisions, intended action, and remaining uncertainty together so later reviewers can follow the decision.
Inform the next assessment
Use reviewed learning to refine questions, revisit indicators and outcome assumptions, and agree what evidence or validation the next assessment needs.
How this connects to OutcomeLine
Within OutcomeLine, M&E Studio connects collection, validation, monitoring, reporting, and learning. Reviewed performance and human corrections can inform later outcome definition, model testing, controls, and workflow improvements. AidInsight provides the workspace for workflows and review.
Explore AidInsight
An example from our work
Impact measurement for the ReCa Fund
The existing ReCa Fund case study describes translating a sustainability framework into monitoring, reporting, and verification tools, workflow templates, and verification guidance. It illustrates the measurement and review structure that an AI-assisted workflow needs.
Limitations
Keep the limits of the evidence in view.
- Missing, delayed, biased, or inconsistent records limit what can be reported or compared. Changes in indicator definitions, coverage, or context need explicit review.
- AI can omit evidence, misclassify a record, make a calculation error, or generate an unsupported narrative. Check source records, calculations, and drafts before use.
- Monitoring trends and associations do not establish causality or replace a formal impact evaluation. Evaluation questions require appropriate methods and specialist judgement.
- A reporting draft is not an independent assurance opinion or a guarantee of donor or regulatory compliance. Permissions, requirements, approval, and accountability remain with the responsible organisation.
Work with DevelopMetrics
Start with one monitoring or reporting question.
Tell us about the decision or report, the evidence available, and the people who need to review it. We can discuss a useful scope, expected outputs, and review responsibilities.