A Leading National Environmental Services Provider
AI-Powered Waste Profiling Cuts Rework by Half and Accelerates Approvals for a Leading National Environmental Services Provider
A leading national environmental services provider processed thousands of hazardous waste profiles monthly through a manual workflow that averaged 3.7 days per approval — and 5.5 days when rework was required. With 23% of profiles kicked back for corrections and accuracy hovering at 75–80%, delays frustrated customers, burdened agents, and increased regulatory risk. Allata embedded an AI engineering team alongside the client's domain experts to build an intelligent document processing and validation platform on AWS. The solution targets 98.5%+ extraction accuracy and a 50% reduction in downstream corrections from day one of production.
OVERVIEW
A leading national environmental services provider with over $16 billion in revenue and more than 42,000 employees engaged Allata to transform how its environmental services division prices and approves hazardous waste disposal jobs. The engagement paired Allata’s AI and AWS cloud engineers with the client’s hazardous waste specialists in a blended, embedded team model.
The platform combines Intelligent Document Processing (IDP) for lab reports and safety data sheets with a Core AI validation engine that predicts and pre-fills over 30 profile fields. Together, these systems compress manual review cycles and catch errors before they trigger rework.
- Extraction accuracy target of 98.5%+ for lab documents, up from 75–80% manual baseline
- Projected 50% reduction in downstream profile corrections at production launch
- Approval timeline reduction from 5.5 days (with rework) toward the 3.2-day no-rework baseline
THE CHALLENGE
When the client takes on an environmental cleanup project, its pricing depends on the exact chemical composition of the waste involved. Lab reports and safety data sheets must be reviewed, validated, and translated into a formal waste profile before a price can be quoted. This process historically took five to six days — and that timeline stretched further whenever information was incomplete or inaccurate.
Seventy percent of profiles flowed through customer service agents working in a legacy PowerBuilder application. Agents manually extracted data from lab documents, populated over 200 fields across multiple profile sections, and routed completed forms to approvers. When errors surfaced — and they did frequently — the profile bounced back through the chain, often requiring the customer to resubmit information.
Nearly one in four profiles required rework, adding an average of 2.4 days of unproductive effort to an already lengthy cycle. Accuracy with manual entry sat between 75% and 80%, with RCRA federal waste codes being the most error-prone category.
- 23% of profiles required rework, adding ~2.4 days per cycle
- 5.5-day average approval when rework was needed vs. 3.2 days without
- 75–80% accuracy on manual profile completion
- Multiple back-and-forth exchanges between customers, agents, and approvers before a single profile could be approved
OUR SOLUTION
Allata designed and built a two-part platform: an Intelligent Document Processing (IDP) service for extracting structured data from lab reports and safety data sheets, and a Core AI validation engine that predicts field values and flags insufficient submissions before they reach an approver.
When a customer service agent attaches a lab document in the legacy system and clicks “Analyze,” the file uploads to Amazon S3 via a pre-signed URL and triggers a processing pipeline. AWS Textract extracts text from the first page to identify which of the five major labs produced the report which handle roughly 80–90% of the client’s volume. Once classified, the document routes through a lab-specific pipeline tuned to that labs formatting, achieving 97–99%+ extraction accuracy. AWS Textract alone delivered 95% accuracy before any custom tuning; the lab-specific pipelines push that to 98.5% or higher.
For safety data sheets, where formats vary across thousands of manufacturers, the team built an adaptive pipeline. Documents are processed against a canonical data model, and any fields that fall outside the known schema are captured in an additional extraction layer. Amazon CloudWatch alerts notify the team when recurring unknown fields appear, enabling the model to evolve over time.
- Lab-specific pipelines route documents through format-aware extraction paths for each of the five major labs, maximizing accuracy per document type
- Adaptive SDS handling unpredictable document formats against a canonical model while flagging and learning from unrecognized fields
- Core AI validation uses AWS Bedrock LLMs to analyze profile submissions, predict missing values, and assess whether provided data meets regulatory thresholds — reducing the chance a profile is kicked back during approval
The AI services connect to the client’s legacy application through an asynchronous API layer, presenting suggestions as non-intrusive recommendations within the agent’s existing workflow. No changes to the PowerBuilder interface were required. Agents maintain full control; the AI accelerates their judgment rather than replacing it.
THE RESULT
The IDP system processes lab documents from the five primary labs at 97–99%+ accuracy, with a floor target of 98.5%. Documents that previously required line-by-line manual review now return structured, validated data within seconds. The Core AI engine pre-fills and validates over 30 profile fields, catching the types of errors that previously triggered 23% of profiles to be kicked back for rework.
By reducing rework and compressing the review cycle, the platform directly addresses the 2.4-day delay that accompanied every rejected profile. Agents spend less time on data entry and correction; approvers receive cleaner submissions; customers get quoted faster.
- 98.5%+ extraction accuracy target achieved for primary lab documents, up from 75–80% manual baseline
- 50% projected reduction in downstream corrections upon production launch
- Rework-driven delays of ~2.4 days per affected profile targeted for elimination Agent workflow unchanged — AI recommendations surface within the existing legacy interface
- Agent workflow unchanged — AI recommendations surface within the existing legacy interface
technology
Allata built the solution as a cloud-native platform on AWS, leveraging managed AI and serverless services to minimize operational overhead and maximize scalability.
- AWS Textract — Primary text extraction and document classification engine
- AWS Bedrock / Bedrock Data Automation (BDA) — LLM-powered field prediction, validation, and document processing orchestration
- AWS Lambda (TypeScript) — Serverless processing functions for metadata extraction, pipeline routing, and file analysis
- Amazon S3 — Document storage with pre-signed URL upload for secure, credential-free file ingestion
- Amazon API Gateway — RESTful integration endpoints for legacy and external systems
- Amazon DynamoDB — Data storage for extracted canonical models and processing metadata
- Amazon CloudWatch — Monitoring, alerting, and analytics for extraction accuracy and pipeline health
- AWS CDK — Infrastructure as code for deployment pipeline management
- Snowflake — External analytics integration for reporting and business intelligence
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