You’ve read about the value of AI batch processing. You understand the economics. You know how to design human-AI workflows. Now the practical question: How do you actually build this?
This post is a hands-on guide to getting your first AI batch pipeline running. We’ll cover how to identify the right problem, what architecture to use, and the pitfalls that derail most first attempts.
Step 1: Identify the Right Problem
Not every problem is a good fit for your first AI pipeline. The ideal candidate has four characteristics.
High volume and repetitive. Look for workflows where humans process many similar items—categorizing incoming documents, extracting data from forms, classifying support tickets, parsing structured information from unstructured text. Volume matters because it amortizes implementation cost. Processing 50 documents per month? The automation cost may never pay off. Processing 5,000 per month? You’ll see ROI quickly.
Currently manual or semi-automated. The best candidates are processes where humans currently spend significant time on data entry, manual classification, copy-paste operations between applications, or spreadsheet-based transformations. If it’s already fully automated with traditional tools and working fine, AI probably isn’t the right solution.
Tolerant of occasional errors. Your first pipeline shouldn’t be in a domain where errors cause catastrophic harm. Good first candidates include internal data enrichment (not customer-facing), analytics preprocessing (not financial reporting), and content categorization (not compliance decisions). Start where you can afford to learn. Move to higher-stakes applications once you’ve proven the approach.
Clear success criteria. Before you build, define what success looks like. What accuracy is acceptable? What processing speed is required? What cost per document is viable? What human review rate is sustainable? If you can’t articulate these, you’re not ready to build.
Step 2: Start Small
Your first pipeline should be deliberately simple. The goal is to prove value and learn, not to build a complete solution.
Pick one document type. Don’t try to process “all invoices.” Pick invoices from a single vendor. Not “all support tickets”—tickets about a specific topic. Narrow scope lets you craft specific prompts, build accurate test sets, measure results clearly, and iterate quickly.
Pick one transformation. Extract one type of information. Perform one classification. Answer one question per document. Maybe you extract the total amount from invoices, or classify support tickets as technical vs. billing, or identify the counterparty from contracts. You can expand later. Start narrow.
Build a proof of concept. Before building production infrastructure, validate that AI can actually do the task. Collect 20-50 sample documents, manually create ground truth for what the correct output should be, then test with API calls using the OpenAI playground or simple scripts. Measure accuracy against your ground truth and iterate on prompts until accuracy is acceptable.
If you can’t hit acceptable accuracy in the POC phase, the task may not be a good fit—or may need a different approach. Don’t build production infrastructure hoping it will somehow work better at scale.
Step 3: Design Your Architecture
Once POC validates the approach, design a production pipeline. Here’s a pattern that works:
flowchart TD
A["<b>Input Sources</b><br/>File drops, API endpoints, email, scheduled pulls"]
B["<b>Input Queue</b><br/>Documents waiting for processing"]
C["<b>Processing Workers</b><br/>Pull from queue → Call LLM API → Parse responses<br/>Calculate confidence → Log results"]
D["<b>Routing Layer</b><br/>High confidence → Output<br/>Low confidence → Review"]
E["<b>Output Store</b><br/>Database, API, file"]
F["<b>Review Queue</b><br/>Human review interface"]
A --> B
B --> C
C --> D
D --> E
D --> F
Queue-based processing is the foundation. Use a queue (Redis, RabbitMQ, SQS, or even a database table) to decouple input from processing. This lets processing happen at its own pace, ensures failures don’t lose work since items retry from the queue, allows multiple workers to process in parallel, and handles backpressure naturally.
A simple queue item might look like:
{
"document_id": "doc_123",
"source_path": "/documents/invoice_001.pdf",
"document_type": "invoice",
"status": "pending",
"created_at": "2026-01-05T10:00:00Z",
"attempts": 0
}
Async workers should run independently and handle failures gracefully. The basic pattern: pull the next pending item, mark it as processing, call the LLM, route based on confidence, and handle failures with retries up to a maximum attempt count.
while True:
item = get_next_pending_item()
if not item:
sleep(5)
continue
mark_processing(item.id)
try:
result = process_document(item.source_path)
if result.confidence > THRESHOLD:
save_to_output(result)
mark_completed(item.id)
else:
add_to_review_queue(result)
mark_needs_review(item.id)
except Exception as e:
increment_attempts(item.id)
if item.attempts >= MAX_ATTEMPTS:
mark_failed(item.id, str(e))
else:
mark_pending(item.id) # Will retry
Log everything. Every LLM call should be logged with input, output, confidence scores, token usage and cost, duration, and success/failure status. This data is essential for debugging problems, tracking costs, identifying optimization opportunities, and auditing decisions. You can’t optimize what you don’t measure.
Step 4: Build the Processing Logic
The core of your pipeline is the LLM interaction. Good prompts make the difference between a working system and an expensive failure.
Be specific. Don’t ask “extract information from this invoice.” Ask “extract the vendor name, invoice number, invoice date, and total amount from this invoice.”
Define the output format. Use structured output (JSON) with explicit field names and clear instructions for handling missing data:
Extract the following fields from this invoice and return as JSON:
- vendor_name: The company issuing the invoice
- invoice_number: The unique invoice identifier
- invoice_date: Date in YYYY-MM-DD format
- total_amount: Total due as a number (no currency symbols)
If a field cannot be found, use null.
Include examples. Few-shot prompting improves accuracy significantly. Show the model what good output looks like.
Add confidence guidance. Ask the model to include a confidence score (0.0-1.0) for each field indicating how certain it is about the extraction.
Handle errors gracefully. LLM calls fail. Rate limits require exponential backoff. Timeouts need retries. Malformed responses need JSON validation. Some content triggers safety filters and needs logging. Build retry logic with sensible defaults—three attempts with exponential backoff handles most transient failures.
Step 5: Implement the Review Interface
For documents that need human review, build a simple interface. At minimum, reviewers need a list of items awaiting review, a view of the original document, the AI extraction with confidence highlighting, accept/edit/reject actions, and a notes field for edge cases.
This doesn’t need to be fancy. A simple web form or even a spreadsheet can work for POC. Polish later.
When reviewers edit AI output, capture the original AI output, the corrected output, which fields changed, and time spent on review. This feeds your improvement loop—patterns in corrections reveal where your prompts need work.
Step 6: Measure and Iterate
Track metrics from day one across three categories.
Accuracy metrics tell you how well the system performs: auto-approval rate (percentage processed without human intervention), correction rate (percentage of reviewed documents that needed changes), and error rate (errors found in auto-approved documents via sampling).
Efficiency metrics tell you how fast the system works: processing time from input to output, average review time per document, and queue depth showing how much work is waiting.
Cost metrics tell you whether the economics work: cost per document (total LLM cost divided by documents processed), cost per successful extraction including retries and failures, and human time cost (review time multiplied by labor rate).
Use these metrics to improve. If auto-approval rate is too low, you might need better prompts, lower confidence thresholds (if error rate is acceptable), or specialized handling for problematic document types. If cost per document is too high, consider switching to a smaller model, reducing prompt length, or batching more aggressively.
Common Pitfalls
Over-engineering the first version. Your first pipeline doesn’t need microservices architecture, Kubernetes deployment, real-time dashboards, or ML-based confidence calibration. It needs to work. Use the simplest architecture that processes documents reliably. Optimize later.
Not tracking costs from day one. When you realize costs are higher than expected, you need data to understand why. If you didn’t log token usage per document type, per prompt version, per model—you’re debugging blind. Build logging into the first version, not as an afterthought.
Expecting 100% accuracy immediately. Your first prompts won’t be perfect. Your first confidence thresholds will be wrong. Your first document handling will miss edge cases. Plan for iteration. Budget time for prompt refinement. Expect the first few weeks to include significant tuning.
Skipping the human review step. “We’ll add review later” means “we’ll ship errors now.” Even if you’re confident in accuracy, implement review from the start—even if it’s just spot-checking a sample.
Building before validating. Don’t build production infrastructure for a task AI can’t actually do well. Validate with a POC first. If you can’t hit 80% accuracy in POC with good prompts, production infrastructure won’t fix it.
Scaling Up
Once your first pipeline is working, expansion follows naturally.
Add document types by applying the same pattern with new prompts and validation. You already have the infrastructure.
Increase automation as you gather data on actual error rates. Raise auto-approval thresholds for well-performing document types, reduce sampling rates for high-accuracy processes, and automate downstream actions for high-confidence results.
Optimize costs with usage data. Identify expensive processing that could use cheaper models, batch more aggressively for non-urgent processing, and cache results for repeated document patterns.
Formalize operations as you move from POC to production. Add monitoring and alerting, implement proper deployment pipelines, document runbooks for common issues, and train additional team members.
What You’ve Learned
Over this series, we’ve covered the value proposition (AI may or may not be a bubble, but batch processing value is real and measurable), the core problem (dirty data that needs semantic understanding to clean), the economics (batch APIs and model selection make costs work), the workflow (human-AI hybrid systems that combine scale with quality), and the implementation (how to actually build your first pipeline).
The capability is here. The economics work. The organizations that act now build compounding advantages.
The question isn’t whether AI batch processing will become standard infrastructure—it will. The question is whether you’ll be ready when the hype settles and the real work remains.
This is the final post in the AI for batch data processing series. Read the complete series:
- Is AI a Bubble? Maybe. Here’s What Won’t Burst.
- The Dirty Data Problem AI Was Made For
- The Economics of AI Batch Processing
- Human-AI Workflows That Actually Work
Ready to build your first AI data pipeline? InFocus Data helps organizations design, implement, and operate batch processing systems that turn unstructured data into actionable information. From proof of concept to production, we’re here to help.