finance_ops · finance · workflow
ClimateAligned builds a RAG-based climate finance assessment system from zero to first users
Assessing the climate credentials of financial instruments required expert analysts to manually comb through large volumes of unstructured and highly variable company documents—a slow, expensive process incapable of scaling to the large number of companies producing relevant data.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Document ingestion and storage
Documents are consumed from the internet and stored in a structured dataset within the system.
Tools used
RAGPostgresOpenAIGPT-4BM25RRFPythonvector database
Outcome
The RAG system achieved 85% accuracy on its own; with expert human-in-the-loop review, effective accuracy reached 99% while per-company assessment time fell from about 2 hours to 20 minutes. Adding a traditional ML classifier to route only items needing review delivered roughly another order of magnitude increase in throughput.
Results
Time savedfrom about 2 hours to 20 minutes
Volume85%
Grounding & classification
Source type: technical build writeup
38 fields verified against source quotes, 1 dropped as unverifiable.
data extractiondocument aiknowledge searchragknowledge basepolicy documentbuilder submittedhuman review describedmetric backednamed customerproduction runtime claimedsource backedtools describedworkflow describedfinancial servicessoftwareaccuracy improvementemployee productivitythroughput increasetime savedtechnical build writeupcompliance monitoringfinance opsai draft human approvalhuman review queuerag answering