Technology

SINAI Launches Utility Automation, Turning Utility Bills into Audit-Ready Emissions Data Across Scopes 1, 2, and 3

Published

3 hours ago

June 11, 2026

New capability integrates SINAI directly with utility providers, replacing manual collection with AI-powered emission-factor mapping.

SAN FRANCISCO, June 11, 2026 /PRNewswire-PRWeb/ — SINAI, the enterprise platform for AI-powered carbon management, today announced the launch of Utility Automation. This capability connects SINAI directly to utility providers, pulling electricity, gas, water, and waste data into emissions inventories automatically and converting it into audit-ready activity data, without waiting for teams to gather, clean, and manually upload bills.

SINAI’s Utility Automation transforms utility bills into defensible emissions data, with full visibility into how every number is calculated. It removes a major operational bottleneck so teams can move from data collection to decarbonization action. Jess Waldeck, CEO, SINAI Technologies, Inc.

Utility consumption data forms the foundation of an organization’s emissions inventory, and it rarely maps to a single scope. Purchased electricity drives Scope 2; on-site fuel such as natural gas falls under Scope 1; and waste and water typically fall under Scope 3. Because SINAI ingests all of these bill types, a single workflow feeds activity data across all three scopes. Collecting and categorizing it by hand is among the most time-consuming, error-prone steps in the cycle.

How SINAI’s Utility Automation Works

Most tools can read a utility bill once it is in hand. SINAI connects directly to providers, so the data arrives at the source before anyone gathers a single bill. Teams connect through one of two paths to produce audit-ready activity data with a clear record from the original bill to the final emissions figure.

Direct provider integration pulls consumption data from utility providers on a recurring basis, with no manual downloads or bill chasing.PDF bill upload covers cases where a provider cannot yet be integrated, or for one-off submissions. Either way, each bill is collected, normalized, and mapped to the correct facility and emissions category, regardless of format, layout, or language.

SINAI’s AI Emissions Match engine and Data Match Agents handle the work that typically falls to analysts: reading each line item, selecting the most appropriate emission factor, and assigning it to the correct business entity, turning unstructured bills into structured, calculation-ready data. All mappings are transparent and editable, with a clear audit trail from the original bill to the final emissions output.

According to Jess Waldeck, CEO of SINAI Technologies, Inc., “Utility data is where decarbonization quietly stalls. Teams lose weeks every quarter turning bills into defensible numbers, and Utility Automation does that work for them while showing exactly how every figure was derived, so the focus shifts from collecting data to acting on it.”

Built for Enterprise Emissions

Built for enterprise complexity, Utility Automation operates across many facilities, billing accounts, and reporting entities. It supports multiple commodity types and maps every consumption record to the correct organizational entity within SINAI’s multi-layer inventory, so teams keep accurate, current data without rebuilding their collection process each cycle. The capability is already deployed at scale: a leading enterprise customer has connected more than 1,500 meters, maintaining full audit traceability from each original bill to the final emissions record.

Why Utility Data Quality Matters for Compliance and Reporting

The regulatory picture is shifting, with the EU’s Omnibus package narrowing CSRD’s scope even as assurance expectations hold steady, and California’s SB 253 extending disclosure across the US. Across all of it, one thing is constant: the data behind an inventory has to be accurate, traceable, and defensible under third-party assurance. That risk is highest for utility data, which spans Scopes 1, 2, and 3. Teams that rely on manual collection face compounding risks: delayed data, missing site coverage, inconsistent unit handling, and limited visibility into changes between reporting periods.

Utility Automation is built to address these risks at the source. By automating the collection and normalization of utility data, SINAI helps enterprise teams move from reactive data management to a controlled, repeatable process that scales with the business and meets the traceability standards required by auditors and regulators.

See Utility Automation in Action

Utility Automation is available to SINAI customers today. Tour the platform or request a demo.

About SINAI Technologies

SINAI is the AI-powered Carbon Management and Decarbonization Platform that empowers organizations to measure, disclose, manage, and mitigate carbon emissions with confidence. Designed for sustainability, finance, and operations teams, SINAI provides a data-driven approach to decarbonization, enabling businesses to align with climate goals, comply with regulations, and unlock long-term value. Visit us at SINAI.com and follow us on LinkedIn.

Media Contact
Mia Farber, SINAI Technologies Inc., 1 4124147500, media@sinai.com, sinai.com

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SOURCE SINAI Technologies Inc.

Technology

SmartOrg releases Version 10.x of its Decision Intelligence platform

Published

11 minutes ago

June 11, 2026

New release expands SmartOrg’s Decision Intelligence platform with a cleaner, more configurable experience and new product capabilities.

PALO ALTO, Calif., June 11, 2026 /PRNewswire-PRWeb/ — SmartOrg, Inc., a Palo Alto-based provider of Decision Intelligence solutions, today announced the release of version 10.x of its software platform, with its standard products Innovation Navigator® and Portfolio Navigator® now deployed by configuring modules on a newly integrated platform.

“This release is really about giving customers a better experience and more flexibility in how they apply the platform to their specific decision challenges.”

Combined in other ways, these modules support deployments optimized for other contexts.

“This release is really about giving customers a better experience and more flexibility in how they apply the platform to their specific decision challenges,” said David Matheson, President and CEO of SmartOrg. “We’re delighted to see customers using SmartOrg’s Decision Intelligence Platform in more targeted ways, for example in Technology Assessment, Energy Exploration and Drug Discovery. Version 10.x gives them a cleaner, more configurable foundation for doing that work.”

The release includes major updates to Innovation Navigator®, including flexible brainstorming canvases, enhanced Learning Plan modules, and upgraded Discovery Grid visualizations. These enhancements help innovation teams capture ideas, organize learning activities, and focus attention on the uncertainties that matter most.

Portfolio Navigator® also receives significant new capabilities, including a Maturity Matrix, Probability of Success calculator, and portfolio goal analysis. These features help leaders better assess projects, compare them, and support portfolio decisions.

“Version 10.x is part of our ongoing commitment to keeping SmartOrg’s software current, useful, and easier for customers to apply,” said Dave Wachenschwanz, Director of Development at SmartOrg. “The platform has significant upgrades in its user interface, improving consistency and usability.”

To see highlights of the new release, SmartOrg has published a short video overview from its development team that you can watch here: https://bit.ly/4xdA7QY

About SmartOrg

SmartOrg helps innovation, R&D, and strategy teams convert uncertainty to opportunity. Its software products—Innovation Navigator® and Portfolio Navigator®—apply quantitative methods to prioritize learning, build confidence in the business case, and guide investment decisions at both the project and portfolio levels to help organizations meet their growth objectives.

Media Contact

Doug Williams, SmartOrg, Inc., 1 3399270834, dwilliams@smartorg.com, https://smartorg.com

View original content to download multimedia:https://www.prweb.com/releases/smartorg-releases-version-10x-of-its-decision-intelligence-platform-302796404.html

SOURCE SmartOrg, Inc.

Technology

Cathay Financial Holdings Leverages Open-Source Small Language Models to Identify Customer Intent

Published

11 minutes ago

June 11, 2026

Fine-tuning Small Language Models to Better Understand Local Financial Service Contexts, Domain Terminology, and Ambiguous Customer Queries

TAIPEI, June 12, 2026 /PRNewswire/ — To enhance operational efficiency and customer experience, Cathay Financial Holdings (Cathay FHC) continues to advance the application of generative AI in financial services through its generative AI technical framework, GAIA, and AI-as-a-Service (AIaaS) strategy. Building on last year’s validation of large language models (LLMs) for financial applications, Cathay FHC recently unveiled its latest AI research findings at NVIDIA GTC Taipei 2026, demonstrating how open-source small language models (SLMs) can be fine-tuned for customer intent classification and applied to future financial service scenarios.

The study evaluated several leading open-source models from Meta, TAIDE, TAME, NVIDIA and OpenAI. Preliminary results showed that, under the testing framework, fine-tuned SLMs may reduce dependence on complex prompt engineering and vector retrieval modules, potentially simplifying system architecture while lowering future operational and maintenance complexity.

The findings indicated that, when combined with carefully designed financial-domain datasets and targeted model fine-tuning, SLMs can further improve model stability, inference efficiency, and deployment controllability. In customer intent classification, the fine-tuned SLM achieved performance close to mainstream closed-source LLMs, approaching that of leading proprietary LLMs, providing enterprises with a practical reference for evaluating AI model training and deployment strategies.

From a data governance and privacy perspective, the study adopted a fully synthetic data approach, ensuring that no real customer information was used during model training. Through techniques including service-function clustering, single-intent and multi-intent dataset design, Taiwan-context localization, and keyword expansion, Cathay FHC strengthened the model’s ability to understand local financial service contexts, industry-specific terminology, and ambiguous customer queries.

Potential future applications include mortgage balance inquiries, credit card payment assistance, and branch service navigation, laying the groundwork for intelligent search, service routing, and next-generation customer engagement experiences.

From a technical architecture standpoint, Cathay FHC integrated NVIDIA AI tools—including NVIDIA NeMo Customizer, NVIDIA NeMo Curator, and NVIDIA TensorRT-LLM—together with NVIDIA Hopper architecture computing resources to support data generation, model fine-tuning, inference optimization, and experimental evaluation. Leveraging NVIDIA AI ecosystem, Cathay FHC continues to strengthen its capabilities in financial-domain model development, data governance, and application validation.

In recent years, Cathay FHC has steadily expanded AI innovation across a wide range of financial scenarios, building scalable technological foundations spanning internal process optimization, customer service enhancement, financial knowledge understanding, and model governance. As financial institutions navigate an increasingly regulated environment, stringent data governance requirements, and rapidly evolving customer expectations, Cathay FHC remains committed to advancing AI research in a compliant, secure, and resilient manner.

Looking ahead, Cathay FHC will continue exploring long-context classification, advanced financial document understanding, and cross-scenario AI applications. By developing model training and deployment approaches tailored to the financial sector, the company aims to accelerate innovation and create more intelligent, efficient, and customer-centric financial services.

View original content to download multimedia:https://www.prnewswire.com/apac/news-releases/cathay-financial-holdings-leverages-open-source-small-language-models-to-identify-customer-intent-302798631.html

SOURCE Cathay Financial Holdings

Technology

First Advantage Set to Join S&P SmallCap 600

Published

11 minutes ago

June 11, 2026

NEW YORK, June 11, 2026 /PRNewswire/ — First Advantage Corporation (NASD: FA) will replace Kennedy-Wilson Holdings Inc. (NYSE: KW) in the S&P SmallCap 600 effective prior to the opening of trading on Tuesday, June 16. A consortium led by KW’s CEO with Fairfax Financial Holdings Limited (TSE: FFH) is acquiring Kennedy-Wilson Holdings in a deal expected to close soon, pending final closing conditions.

Following is a summary of the changes that will take place prior to the open of trading on the effective date:

Effective Date

Index Name

Action

Company Name

Ticker

GICS Sector

June 16, 2026

S&P SmallCap 600

Addition

First Advantage

Industrials

June 16, 2026

S&P SmallCap 600

Deletion

Kennedy-Wilson Holdings

Real Estate

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S&P Dow Jones Indices is a division of S&P Global (NYSE: SPGI), which provides essential intelligence for individuals, companies, and governments to make decisions with confidence. For more information, visit www.spglobal.com/spdji/en/.

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