Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Manual data collection and spreadsheet-based calculations create unsustainable overhead and audit risk.
Traditional carbon accounting is a high-cost, low-accuracy process. Teams waste months manually aggregating data from hundreds of sources: utility bills, travel logs, procurement systems, and supplier spreadsheets. This leads to:
Manual processes cannot scale under accelerating global mandates like CSRD and SEC climate rules, creating direct financial and reputational liability.
Inference Systems engineers automated, AI-powered platforms that eliminate this bottleneck. We build systems that:
ERP, SCM, and utility APIs.GHG Protocol.This transforms sustainability from a compliance cost center into a strategic, data-driven operation. Explore our related service for Generative AI for Sustainability Report Authoring to automate disclosure drafting.
For enterprises needing to secure this sensitive environmental data, our expertise in Confidential Computing for AI Workloads ensures calculations occur in hardware-secured enclaves, protecting proprietary operational data.
Move beyond manual spreadsheets and generic software. Our engineered AI platforms deliver measurable financial, operational, and compliance advantages by automating the most complex emissions calculations.
A phased roadmap for developing a production-ready AI-powered carbon accounting platform with Inference Systems. This timeline outlines key deliverables, technical milestones, and integration points for automated Scope 1, 2, and 3 emissions calculation.
| Phase & Key Activities | Weeks 1-3 | Weeks 4-8 | Weeks 9-12 |
|---|---|---|---|
Phase Name | Discovery & Architecture | Core Development & Integration | Testing, Deployment & Handoff |
Primary Deliverables | Technical specification document Data source integration plan Security & compliance architecture | Core emissions calculation engine Data ingestion pipelines (ERP, utility, procurement) POC dashboard with initial metrics | Staging environment deployment Penetration testing & security audit Production deployment & knowledge transfer |
Key Technical Milestones | Finalize model selection for spend-based & activity-based calculation Design vector database schema for document intelligence (e.g., utility bills) Define API contracts for third-party data providers | Deploy initial ML models for automated data classification Integrate with 2+ core data sources (e.g., SAP, Oracle) Implement baseline RAG system for regulatory document querying | Achieve >95% accuracy in automated emissions factor matching Complete load testing for 1M+ data points Finalize 99.9% uptime SLA and monitoring dashboard |
AI/ML Component Focus | Data strategy & pipeline design Model evaluation for time-series forecasting | Development of custom models for Scope 3 category allocation Integration of computer vision for invoice/PDF parsing | Model performance validation & fine-tuning Bias auditing for supplier scoring algorithms |
Compliance & Reporting | Gap analysis against CSRD, SEC, TCFD Data lineage mapping design | Implementation of audit trail logging Automated data validation checks | Generation of first compliance-ready data export Internal control testing documentation |
Team Involvement | Joint workshops with your technical & sustainability teams | Weekly sprint reviews & integration checkpoints Bi-weekly demos of developed features | User acceptance testing (UAT) with key stakeholders Comprehensive operational runbooks delivered |
Outcome / Goal | Blueprint for a scalable, compliant platform | Functional minimum viable product (MVP) with core automation | Fully operational platform ready for internal rollout and auditor review |
We engineer robust, scalable, and compliant AI platforms for carbon accounting. Our methodology is built on enterprise-grade security, regulatory-first design, and rapid deployment to deliver measurable business outcomes.
We design from the ground up for CSRD, SEC, and SFDR compliance. Our systems enforce data lineage, audit trails, and algorithmic fairness by design, ensuring your platform meets current and emerging global standards.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Common questions from CTOs and Product Managers evaluating custom AI solutions for automated emissions tracking and regulatory compliance.
For a standard deployment covering Scope 1 & 2 emissions with basic Scope 3 estimation, we deliver a production-ready Minimum Viable Product (MVP) in 6-8 weeks. Complex, enterprise-grade platforms with full Scope 3 supplier integration, multi-modal data pipelines, and custom reporting dashboards typically take 12-16 weeks. Our phased approach ensures you have a functional core for internal validation within the first month. Learn more about our structured delivery process in our guide to AI development methodologies.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
