Bill Sieve - AI Utility Analyzer Model
Utility Analyzer
by Neurometric📄 Document Intelligence
Bill-Sieve is a task-specific model that extracts usage data (kWh, gallons) and billing cycles from utility statements.
Bill Sieve is designed for Document Intelligence workflows where speed, consistency, and control matter more than generic creativity. Instead of relying on a broad model to guess intent each time, this task-specific SLM is optimized for utility analyzer and predictable output quality. That makes it easier to adopt in production pipelines where teams need reliable formatting, lower latency, and reduced hallucination risk.
Most teams integrate CRM records, internal docs, and workflow tools to automate repetitive language-heavy tasks with predictable outputs. This makes deployment practical for sales ops, customer success, legal, and operations teams that need answers grounded in their own data. Because the model is small and focused, teams can run it with efficient infrastructure while still meeting quality targets for the use case.
Compared with general-purpose models, focused SLMs like this one typically deliver better precision for narrow tasks, lower cost per request, and easier governance. You can start with the free tier, test the model against your business data, and scale usage through Neurometric APIs or ClawPack workflows when performance meets your benchmark.
Model: Qwen/Qwen3-4B-Instruct-2507
This task-specific SLM uses Qwen/Qwen3-4B-Instruct-2507 with a specialized system prompt. The model weights are identical to the base Qwen model — the task specialization comes from the system prompt and has been validated through our testing pipeline. Need better accuracy? We can fine-tune the model on your data for your specific use case.
Quickstart
Copy-paste code to use Bill-Sieve in your project
from openai import OpenAI
client = OpenAI(
base_url="https://api.neurometric.ai/v1",
api_key="YOUR_API_KEY"
)
resp = client.chat.completions.create(
model="neurometric/bill-sieve",
messages=[
{"role": "user",
"content": "{\n \"tables\": [\n {\n \"usage\": \"1,204 kWh\",\n \"period\": \"03/01/2025-03/31/2025\",\n \"service\": \"electric\"\n },\n {\n \"usage\": \"58 gallons equivalent\",\n \"period\": \"03/05/20..."}
]
)
print(resp.choices[0].message.content)Test Results
Example Input
Tables: - Usage: 1,204 kWh; Period: 03/01/2025-03/31/2025; Service: electric - Usage: 58 gallons equivalent; Period: 03/05/2025-04/04/2025; Service: gas Metadata: Account Id: 77889900 Statement Date: 2025-04-06 Statement Text: - Utility Provider: North Valley Energy - Customer #: 77889900 - Electric Meter Read Dates: 03/01/2025 - 03/31/2025 - Electric Usage This Period: 1,204 kWh - Gas Meter Read Dates: 03/05/2025 - 04/04/2025 - Natural Gas Usage: 58 gallons equivalent - Note: Amount due by 04/20/2025
Expected Output
Extractions:
Service: electric; Usage Kwh: 1204; Billing Cycle End: 2025-03-31; Billing Cycle Start: 2025-03-01
Service: gas; Usage Gallons: 58; Billing Cycle End: 2025-04-04; Billing Cycle Start: 2025-03-05
Actual Model Response
Here is the extracted usage data and billing cycle information from the utility statement:
---
Electricity Usage - Period: March 1, 2025 – March 31, 2025 - Usage: 1,204 kWh
Natural Gas Usage - Period: March 5, 2025 – April 4, 2025 - Usage: 58 gallons equivalent
Billing Information - Statement Date: April 6, 2025 - Account ID: 77889900 - Payment Due By: April 20, 2025
---
Let me know if you'd like this formatted for a report, email, or spreadsheet.
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Capabilities
Related models
Frequently asked questions
Does Bill Sieve work with CRM systems?
Yes. Bill Sieve is built to work with CRM systems, internal docs, and CSV/JSON pipelines. Most teams connect existing APIs or scheduled exports and map records into prompt-ready JSON.
What data format does this model accept?
You can send plain text or structured JSON. For utility analyzer, teams usually pass normalized records with field names, context notes, and output constraints.
Why use a task-specific SLM for utility analyzer?
Task-specific SLMs are tuned for one business outcome, so they are often more consistent and cost-efficient than general models for repetitive production workflows.
Try This Model
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Use via API
FreeView API settings and get a free key. Sign up when you want more keys or billing access.
Need better performance?
We can fine-tune this model on your data for higher accuracy on your specific use case.