Clean 50,000 Messy Records for $0.80
The Challenge: The "Dirty Data" Tax
Every data team has a pipeline that starts with "just clean up the data." Fixing inconsistent company names ("MSFT" vs "Microsoft Corp") or messy addresses traditionally requires weeks of writing fragile regex rules or paying contractors for manual review.
The Doubleword Unlock
Doubleword provides a high-throughput async inference engine built for massive ETL pipelines.
The Result: Treat LLM-powered data cleaning as a standard, daily pipeline stage rather than an expensive special case. Complete multi-stage workloads in hours, and drop processing costs by 97%.
The Economics of Data Processing
Dataset Cleaning Workload: Clean, enrich, and deduplicate 50,000 highly inconsistent public company records (SEC EDGAR dataset).
Pipeline Structure: 3-stage ETL (Normalize → Classify Industry → Adjudicate Duplicates)
50,000
Records Processed
5.6M
Total Tokens
99.2%
Success Rate
42%
Fewer False Duplicates
| Provider | Total Cost |
|---|---|
| Doubleword | $0.80 |
| Doubleword | $1.56 |
| OpenAI | $27.40 |
| Anthropic | $38.15 |
The Result: LLM-powered data processing turns a multi-day manual effort into an automated pipeline you can run for under a dollar. At $0.80 for 50,000 records, you can afford to run pipelines iteratively: clean the data, inspect, adjust your prompts, and re-run the entire async workload in the same afternoon.
Hybrid Workloads & Strict Schemas
When inference is practically free, you can integrate LLMs directly into your data engineering architecture. Our recommended approach for ETL workloads utilizes two core patterns:
Strict Structured Outputs
Data pipelines break when JSON is malformed. Doubleword supports strict JSON schemas for async workloads. The model's output is guaranteed to exactly match your target database schema (e.g., extracting street, city, state, zip_code into pristine JSON). No fuzzy parsing, no retry logic.
Hybrid Map-Reduce (Deduplication)
Don't use LLMs for tasks traditional algorithms do better. For deduplication, run cheap, local fuzzy matching (like Levenshtein distance) to generate candidate duplicate pairs. Then, enqueue only those candidates to Doubleword's async queue. The LLM acts as the final judge, easily recognizing that "First National Bank of Chicago" and "First National Bank of Charlotte" are not duplicates, despite their text similarity.
How the Async ETL Pipeline Works
Instead of locking up your application server with 50,000 sequential API calls, you orchestrate the pipeline entirely in the background:
Enqueue Pass 1 (Normalize)
Submit your raw database dump and target JSON schema to Doubleword's high-throughput API.
Decouple
Your pipeline orchestrator (like Airflow, Dagster, or Snowflake) pauses the task.
Webhook Trigger
Doubleword processes the 50,000 records in parallel and hits your webhook upon completion.
Auto-Trigger Pass 2 & 3
Your system immediately ingests the clean data and dispatches the next stages (Enrichment and Deduplication) back to Doubleword's queue.
By utilizing high-throughput async queues, latency doesn't compound. A massive 3-stage pipeline completes in hours, not days.