Industries
ESG data automation, sentiment classification, and reporting at the scale ratings teams need.
We build for teams doing the kind of work Moody's, MSCI, and Sustainalytics do daily, from a consumer-facing ESG scoring engine to a financial analytics platform we have run for years. The pipelines behind them normalize fragmented sources and reproduce reports on demand.
The Challenge
What slows ESG & Data Analytics teams down
Fragmented ESG sources
Environmental, social, and governance signals come from filings, disclosures, news, and third-party datasets, each with its own schema and cadence. Normalizing them is the bulk of the work and the main source of errors.
Scoring that reflects different priorities
A single generic score rarely matches how an investor, a consumer, or a research desk weighs environmental against governance factors. Scoring engines need configurable weights and traceable components.
Classification at news volume
Sentiment and topic classification over a news stream has to keep pace with the stream, stay accurate as language shifts, and show its reasoning when an analyst questions a label.
Pipelines that must be reproducible
A report regenerated next quarter has to match the one issued this quarter for the same inputs. That requires versioned data, deterministic pipelines, and infrastructure that scales for the batch without manual intervention.
How We Help
Use cases we have shipped in ESG & Data Analytics
ESG data integration and personalized scoring
Sagebuyer.ai identifies brands and parent companies from web page metadata in real time, connects them to an ESG data ecosystem, and scores them against user-defined sustainability priorities, delivered as a Chrome extension inside the shopping workflow.
Sentiment and topic classification
NLP pipelines that classify news and text at volume with TensorFlow-based models and Python data tooling, with evaluation sets, drift monitoring, and analyst review loops so labels stay trustworthy.
Report generation at scale
Automated, reproducible reporting from versioned inputs with rule-based validation, so the same data produces the same report every time it is run.
Analytics platform infrastructure
Multi-year infrastructure partnership with Chaikin Analytics: application performance optimization, migration from a data center to AWS, and proactive monitoring for a platform serving institutional investors.
Security for data platforms
Penetration testing of the APIs that serve market and ESG data, access control and audit logging design for licensed datasets, and readiness programs for data partner security requirements.
Case Studies
ESG & Data Analytics work we can talk about
ESG & Data Analytics FAQ
Data and analytics teams that collect, score, classify, and report on ESG and market signals: research providers, fintech products, and platforms serving investors. The work has ranged from a consumer-facing ESG scoring engine to running the infrastructure behind a financial analytics platform for several years.
Every model ships with a held-out evaluation set, a drift monitor on input distributions and label confidence, and a review queue where analysts correct low-confidence labels. Corrections feed the next training cycle, and every model version is tied to the data version it was trained on.
Yes. Sagebuyer.ai connects identified companies to external ESG data sources, and the same integration pattern applies to licensed vendor feeds. We build the normalization layer, handle entitlement and access logging for licensed data, and keep vendor-specific logic isolated so a feed can be swapped without a rewrite.
Yes. Scores are computed from named components with user- or desk-defined weights, so any score can be traced back to its inputs. Classification labels carry confidence and the evidence spans that drove them. Both are stored with the data version, which is what makes the reports reproducible.
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