Sylvera is a fast-growing climate-tech company that evaluates and rates carbon offset projects worldwide. Their mission is to bring scientific rigor, transparency, and trust to the carbon markets by assessing whether carbon projects truly deliver the environmental impact they claim.
With more than 20,000 projects in the global voluntary carbon market, and intense scrutiny around accuracy, Sylvera’s ratings enable organizations to invest in high-quality, verifiable climate solutions.
Stefan, Sylvera’s Continuous Improvement Senior Associate, oversees the complex, multi-team operational pipeline that transforms raw project data into published carbon ratings used by global enterprises.
The Challenge: Scaling a Highly Complex, High-Stakes Rating Pipeline
Before Process Street, Sylvera’s project pipeline was smaller and manageable, but rapidly increasing demand highlighted major operational challenges:
- Rating a project involves seven different teams, each responsible for different scientific, analytical, or operational components.
- Different project types require completely different evaluation paths, meaning processes were complicated and easily derailed.
- No unified system existed to ensure steps were followed consistently, which presented risks in accuracy, compliance, and stakeholder trust.
- With over 20,000+ potential projects in the backlog, the team needed a system that ensured repeatability, accuracy, and visibility at scale.
What challenges did Sylvera face as rating volume and process complexity increased?
“All the steps needed to complete a rating are incredibly complex, and we had no system in place to ensure they were all followed through. We needed a platform that allowed us to build workflows we could repeat endlessly, and adapt as fast as the market evolves.”
Sylvera recognized that without standardization, metadata tracking, and automation, they wouldn’t be able to meet demand, or scale their offerings beyond traditional ratings.
The Solution: A Centralized, Automated Workflow Engine Built in Process Street
Sylvera adopted Process Street early in their operational development, using it to architect a robust, dynamic process framework that supports both scientific accuracy and high-volume production.
Building a Structured, Repeatable Multi-Team Workflow
Sylvera built interconnected workflows that guide a rating from initial request to final publication. Each stage of the pipeline, developer outreach, screening, boundary analysis, data extraction, commentary creation, and final publishing, is governed by a structured Process Street workflow.
Projects move through seven workflow-triggered phases, where:
- Each team is assigned tasks automatically.
- Logic and conditional pathways adapt workflows to the project type.
- Data collected is exported directly to Snowflake for instant modeling.
- Outputs from one workflow feed seamlessly into the next.
How does Process Street support Sylvera in managing different project types and evolving methodologies?
“Every new project type can be immediately adapted into the workflow. Once something new is discovered, the workflow is updated and instantly applies to everything new coming in.”
Powering Scientific Accuracy with Metadata Tracking
Many workflows contain 150–200 tasks, with conditional logic determining which sections teams see.
Sylvera uses metadata to:
- Determine which steps are used most often
- Identify bottlenecks and inefficiencies
- Analyze reviewer feedback patterns
- Decide which processes to deprecate or optimize
- This data proved transformational for assessing efficiency and cost-benefit decisions.
Automating Screening With Instant Data Export
Process Street runs feed directly into Sylvera’s Snowflake environment, allowing rating outputs to:
- Trigger instant PDF generation
- Feed into models automatically
- Publish high-level screening assessments within a single day
“This is why we’re able to achieve such quick turnaround times; everything is automated directly from the workflow.”
Replacing Standups With the “End of Day” Report
To manage rapid scale, Sylvera replaced standups with a daily automated reporting workflow:
- Every analyst receives an end-of-day workflow
- They report progress, risks, blockers, and forecasts
- All data is pushed into a centralized sheet for leadership review
- “It’s a massive quality-of-life improvement. No more long meetings, just fast, clear updates that give leadership everything they need.”
Key Process Improvements
Boundary Analysis: 80%+ Faster and Backed by Data
One of Sylvera’s most labor-intensive tasks (boundary analysis) saw dramatic improvements:
- Manual extraction: 7.5 hours
- Developer-provided data (supported by Process Street): 1–1.5 hours
This represents an 80%–86.7% reduction in effort.
Process Street’s metadata enabled Sylvera to demonstrate the efficiency difference using real historical project data, informing strategic decisions.
Scaling Project Output from Hundreds to Thousands
Last year, Sylvera produced 300–400 project ratings.
This year, they plan to deliver 3,000–5,000 ratings—a 650% to 1,566% increase in production capacity.
Stefan attributes this scalability directly to repeatable workflows, automation, and Process Street’s ability to unify multi-team execution.
Compliance, Accuracy & Review Structure
Sylvera built highly structured review stages into workflows:
- Peer review
- Committee review
- Commentary review
- Approval tasks with structured feedback categories
They track the type and quantity of feedback to detect training gaps or tooling issues.
“We rely heavily on approvals and conditional logic. It ensures nothing gets missed and gives us quality metrics we couldn’t see before.”
The Results: Faster Ratings, Scalable Operations & Higher Confidence in Carbon Markets
With Process Street, Sylvera:
- Cut boundary analysis time by 80–86.7%
- Scaled rating production from 300–400 to an estimated 3,000–5,000 per year
- Standardized processes across seven cross-functional teams
- Eliminated manual tracking and replaced standups with automated reporting
- Improved data quality, compliance, and scientific rigor
- Enabled instant data export to Snowflake for rapid scoring and publication
- Built consistent, repeatable workflows for 20,000+ potential projects
“We rely on Process Street to keep every project consistent, compliant, and moving. With hundreds of tasks and seven teams involved, it’s become the backbone of our entire rating operation.”
From Emerging Climate-Tech Leader to Industry Standard
Sylvera’s mission depends on accuracy, transparency, and trust—qualities that require precise, repeatable, and well-governed operations.
Process Street has enabled Sylvera to:
- Scale rapidly without sacrificing scientific rigor
- Improve efficiency with real operational metrics
- Maintain compliance with strict data-handling requirements
- Support future expansion into new project types, methodologies, and markets
Sylvera’s story shows how the right operational foundation empowers climate-tech companies to move faster, produce better science, and bring clarity to one of the world’s most important environmental markets.
Efficiency isn’t optional in climate tech.
Unlock instant visibility and accuracy with Process Street.
Note: Our solution is powered by AWS and uses these AWS services: Amazon Bedrock, Lambda, SQS, API Gateway, S3, CloudFront, Elastic Beanstalk, WAF, ELB, RDS Multi-AZ, EC2 and ElastiCache for Redis. Process Street serves as the intelligent orchestration layer, where AWS AI services power the advanced reasoning and data processing that turns policies into workflows, and then enables workflow automations, intelligent document processing, and reasoning. Specifically, ProcessStreet’s GenAI capabilities are used to ensure that customers’ policies align with their workflows and that change governance mechanisms control how tasks are updated to achieve compliance in an automated manner. This creates a closed-loop system where AI directly triggers and guides actionable workflows.
The post Inside Sylvera’s 80% Efficiency Leap in Carbon Assessments first appeared on Process Street | Compliance Operations Platform.
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