Sold by: Sigmodata
Deployed on AWS
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Send a ticket, log line, document or JSON object plus typed questions and get back a choice, a yes/no probability or a score level, never generated text. Runs inside your AWS account on a SageMaker endpoint, so no text is sent to a third-party API.
Overview
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Python predict
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POST /invocations
Runs inside your own AWS account: your data is processed on a SageMaker endpoint you control, inside your VPC, and is never sent to a third-party API.
Ask typed questions about unstructured input and get answers your code can act on, instead of free text to parse. Send one "state" (text, or any JSON object or array) and up to 64 questions. Every answer is one of the options you supplied:
- choice: one of your named options, with a probability for each
- noul: the probability that a yes/no question is true
- score: a level on a rubric you define, with the most likely level and the expected level
Typical uses are routing support tickets, flagging churn, fraud or abuse signals, classifying log lines by severity, and scoring reviews. Every answer carries a confidence, so you can act automatically on confident answers and send the rest to a person. Questions about one state are evaluated together, so adding a question is cheap.
The request and response shapes follow the public Jev System One API, so existing Jev clients work unchanged. Real-time endpoints and SageMaker batch transform (JSON Lines, one request per line) are supported. Use a GPU instance (ml.g4dn.xlarge or ml.g5.xlarge) for production speed; CPU instances (ml.m5.xlarge) suit low volume. Batch transform is validated on ml.m5.xlarge and ml.g4dn.xlarge.
We welcome your feedback at support@sigmodata.com .
Known limitations
- English-centric. Other languages are not evaluated.
- The context window is 4096 tokens. Your questions and options are kept intact first, and a long state is truncated from the end.
- One evaluation runs at a time per instance. Add instances for more throughput.
- Score questions are the least accurate type. Act on "level" and check "confidence".
- Your own questions are not covered by the figures below. Test on your own data before relying on an answer.
Model and training data
Strands Decider 2B from AWS (Apache-2.0), a LoRA fine-tune of Qwen3.5-2B, served as published. The authors' training data is described in the model card: https://huggingface.co/StrandsAgents/strands-decider-2B-hobson-v19 . The container runs in network isolation, so no data leaves your account.
Measured performance
- Accuracy of 94 percent (32 of 34) on an internal test set of 34 hand-labelled requests: choice 13 of 13, noul 12 of 12, score 7 of 9. All 19 answers with confidence of 0.8 or more were correct. The test set is small, so evaluate on your own data.
- Measured on the published model package. Accuracy is the same on GPU and CPU.
- One question, median and 95th percentile: ml.g4dn.xlarge 451 ms and 959 ms; ml.g5.xlarge 389 ms and 994 ms; ml.m5.xlarge 2.2 seconds.
- Twelve questions in one request: ml.g5.xlarge 0.6 seconds (about 18 questions per second); ml.g4dn.xlarge 1.6 seconds (about 7 per second); ml.m5.xlarge 14.1 seconds.
Highlights
- Typed answers, not generated text: pick one of your options, a yes/no probability or a rubric level, each with calibrated confidence
- Runs inside your own AWS account and VPC - your text is never sent to a third-party API
- 94 percent accuracy (32 of 34) on a small internal corpus; about 0.4 s per question on a GPU instance
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Pricing
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Try this product free for 5 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g4dn.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g4dn.xlarge instance type, real-time mode | $0.30 |
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $0.30 |
ml.g4dn.2xlarge Inference (Real-Time) | Model inference on the ml.g4dn.2xlarge instance type, real-time mode | $0.30 |
ml.g4dn.xlarge Inference (Batch) | Model inference on the ml.g4dn.xlarge instance type, batch mode | $0.30 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $0.30 |
ml.g5.xlarge Inference (Batch) | Model inference on the ml.g5.xlarge instance type, batch mode | $0.30 |
ml.g5.xlarge Inference (Real-Time) | Model inference on the ml.g5.xlarge instance type, real-time mode | $0.30 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.30 |
ml.m5.2xlarge Inference (Real-Time) | Model inference on the ml.m5.2xlarge instance type, real-time mode | $0.30 |
ml.m5.xlarge Inference (Real-Time) | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.30 |
Dimensions summary
You pay by the hour for each instance that runs the model, measured in host hours. Pricing splits across three instance families: ml.m5 (CPU), ml.g4dn, and ml.g5 (GPU). Within each family, you choose a size, either xlarge or 2xlarge. Each option also has a mode: real-time inference serves live requests, while batch inference processes grouped jobs. You pick the instance and mode that match your workload, and billing scales with the hours those instances run. The model deploys in your own AWS account.
Top-of-mind questions for buyers
A host hour is one hour that a single instance runs your model. Billing counts each running instance by the hour. If you run two instances for one hour, that counts as two host hours. Charges accrue only while an instance is active.
Both bill per host hour on the chosen instance. Real-time mode keeps the instance running to serve live requests, so it bills for continuous uptime. Batch mode runs the instance only while processing grouped jobs, so hours accrue during those job runs. Pick the mode that matches your request pattern.
You choose one instance family: ml.m5 is CPU-based, while ml.g4dn and ml.g5 are GPU-based. Within each, you pick xlarge or 2xlarge sizing. Each instance bills separately by the hour. GPU families suit heavier compute; CPU suits lighter workloads. Your cost follows the family, size, and hours run.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Initial release.
- Strands Decider 2B typed decisions: choice, noul and score
- JSON requests with up to 64 questions per state
- Real-time endpoints and SageMaker batch transform (JSON Lines)
- GPU recommended (ml.g4dn.xlarge, ml.g5.xlarge); runs on GPU with default settings
Additional details
Inputs
- Summary
A JSON object with "state" (text, or any JSON object or array) and "questions" (a map of question name to a typed question). For batch transform, one such object per line (JSON Lines).
- Input MIME type
- application/json, application/jsonlines
{"state": "Ticket #4411: I was charged twice for my March invoice and support has not replied in 3 days. Fix this or I am cancelling.", "questions": {"route": {"type": "choice", "instructions": "Which team should handle this ticket?", "criteria": {"billing": "payments, invoices, refunds", "technical": "bugs, outages, errors", "sales": "new purchases, upgrades"}}, "churn": {"type": "noul", "instructions": "Is the customer threatening to cancel?"}, "urgency": {"type": "score", "instructions": "How urgent is this ticket?", "criteria": ["low", "medium", "high", "critical"]}}}
{"state": "Ticket #4411: I was charged twice for my March invoice and support has not replied in 3 days. Fix this or I am cancelling.", "questions": {"route": {"type": "choice", "instructions": "Which team should handle this ticket?", "criteria": {"billing": "payments, invoices, refunds", "technical": "bugs, outages, errors", "sales": "new purchases, upgrades"}}, "churn": {"type": "noul", "instructions": "Is the customer threatening to cancel?"}, "urgency": {"type": "score", "instructions": "How urgent is this ticket?", "criteria": ["low", "medium", "high", "critical"]}}}
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
state | The input to decide about, as text or any JSON object or array | - | Yes |
questions | Map of question name to a typed question (choice, noul or score), up to 64 per request | - | Yes |
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Vendor support
Support contact: Email: support@sigmodata.com Support URL: https://www.sigmodata.com/contact
Support description: Email support@sigmodata.com or https://www.sigmodata.com/contact . We reply within one business day, Monday through Friday, US Pacific time.
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