A complete exposition of the methodology, rating system, evidence architecture, framework integrations, sector calibration, market observatory, and calibration governance that underpin every intelligence output from the AESA platform. This page is the public-facing statement of how African ESG intelligence is built, scored, and governed.
Every intelligence output on the AESA platform — whether a sector aggregate, company rating, evidence score, or gap register — is produced by a system governed by five non-negotiable principles. These principles are not aspirational; they are enforced at the pipeline level. Intelligence that cannot satisfy these principles is withheld from output.
AESA ESG Ratings use a seven-band scale from A+ (Leading) to D (Insufficient) derived from a composite score on a 0–100 range. The composite aggregates four source layers — Evidence, Transformation, Assessment, and Disclosure — with defined weights. A confidence ceiling mechanism can constrain ratings where underlying data quality does not support a higher band.
The composite score is the weighted average of four source layers. Each layer contributes a 0–100 score derived from its own sub-indicators. If a layer has insufficient data, its weight is redistributed proportionally across the remaining available layers — the Evidence Layer is always required as the minimum composition.
Four rules govern confidence suppression. When a rule is triggered, the rating band is capped or penalised irrespective of the raw composite score. This prevents high-band ratings being assigned to companies with thin, stale, or narrowly-sourced evidence. Suppression reasons are recorded and visible to registered platform users.
Intelligence is built through a six-stage pipeline in which each stage has defined quality criteria, governance protocols, and human review requirements before evidence advances. The pipeline is not a data flow — it is a governance chain. Material that does not clear a stage does not progress until its deficiency is resolved or it is explicitly classified as a known gap.
The AESA platform maps intelligence against 14 ESG frameworks spanning global consolidation standards, universal voluntary standards, African regulatory requirements, and international reference frameworks. The mapping is not mechanical — each framework is analysed for its materiality implications in African market conditions, and framework gaps are identified at the indicator level rather than at the reporting section level.
Each of the 150 MIL indicators is calibrated for the specific African operating reality of its sector. ESG materiality is not universal — what is critical to an oil and gas operator is structurally different from what is material to a retail bank. The platform does not apply a single indicator set across all sectors and blend the result. Sector calibration is a foundational design decision.
Aggregated sector observations, pillar maturity readings, and intelligence signals derived from the AESA monitoring system. All outputs shown here are sector-level aggregates — anonymised, publication-governed, and free of company-specific data. Company-level evidence, scores, ratings, gap registers, and transformation roadmaps remain inside governed platform workspaces.
AESA ESG intelligence is produced by 57 documented platform functions — the analytical processes, scoring algorithms, inference rules, and governance protocols that convert raw evidence into rated intelligence output. Each function is formally documented, versioned, and assigned a calibration category that describes the strength of its methodological basis. This public statement of calibration maturity is a structural differentiator: most ESG intelligence providers do not publish the calibration basis of their scoring functions.
The Calibration Trigger Monitor governs when functions require formal review. Triggers are defined in advance and fire automatically or manually depending on condition type. When a trigger fires, the affected functions enter a mandatory review cycle before the next production run.