Train from labeled examples
Upload previously categorized text rows or labeled image folders so the model learns the categories and decisions your team already uses.
StayCharted
StayCharted trains private text and image classification models from your team’s examples to categorize records, route requests, map data, and flag uncertain predictions for review.
AI models trained on your team’s categories
StayCharted is a business-specific classification and data-mapping platform that trains models on examples your team has already categorized. It supports recurring decisions involving support tickets, product catalogs, invoices, contracts, supplier information, requests, documents, and images.
Teams can use the resulting models to categorize new records, route work, standardize labels, map incoming values to internal categories, or flag possible matches. Predictions include confidence information, allowing straightforward results to continue through a file or system while less certain results go to human review.
Core capabilities
Use different model types
Choose among word-based Classifier Models, pre-trained AI Classifier Models that recognize meaning, and Dedicated AI Models trained on your examples for more complex text distinctions.
Run predictions through files, apps, and API
Fill CSV or Excel files, call models through the API on Essentials and higher plans, or use text models from Claude and ChatGPT. Image models accept ZIP uploads or sheets of picture links.
Route uncertain results to review
Every result includes a confidence value. Results below a chosen threshold enter a Review Queue, where reviewers can confirm or correct them before later training.
Check and mask sensitive text
Before text training, the product identifies several types of personal information and secrets and lets users mask, exclude, or retain affected values. Masking is applied to new text read by models trained on masked examples.
Evaluate performance before use
Test models on rows or pictures held out from training, view accuracy and category confusion, and estimate how many results may require human review.
Practical use cases
Support and request routing
Use previously categorized tickets to suggest the appropriate queue or team for incoming customer requests, while sending ambiguous requests to review.
Product and inventory categorization
Apply an existing catalog taxonomy to new product descriptions or photos, helping retail, e-commerce, and inventory teams categorize incoming items consistently.
Expense and invoice classification
Learn from approved expense or invoice examples to suggest consistent expense codes and labels for new financial records.
Supplier data and record mapping
Standardize supplier terminology, translate source codes or descriptions into internal categories, and support record matching using approved examples.
Documents, CRM, and visual triage
Classify contract clauses, business documents, CRM inquiries, inspection photos, or damage images into organization-specific categories before a person completes the workflow.
Pros and Cons
Pros
- Learns from existing labeled examples rather than requiring a team to define every rule from scratch.
- Supports both text and image classification, with model options for word-based categories, semantic text classification, and dedicated models trained on business examples.
- Includes held-out testing, category-confusion information, confidence scores, and a Review Queue to support supervised workflows.
- Offers file-based workflows on the Free plan and API access on Essentials and higher plans, with workspace plans that allow unlimited team members.
- Provides pre-training checks for duplicate examples, overlapping categories, sparse categories, and several types of sensitive text.
Cons
- Detailed end-to-end workflow and integration information is limited in the available product material beyond file workflows, API access on paid plans, and Claude or ChatGPT use.
- Confidence scores help identify uncertain predictions, but the product states that confident predictions can still be wrong; evaluation and review remain necessary.
- Sensitive-data checks do not currently detect names, street addresses, or free-text medical details, which users must remove themselves.
FAQ
How do I train a model in StayCharted?
Upload a spreadsheet containing text and the category assigned to each row, or upload labeled pictures in category folders. StayCharted checks examples for duplicate rows, overlapping categories, too few examples, and detectable sensitive values before training. Unlabeled rows can also be grouped so a team can name the categories.
How does StayCharted handle uncertain predictions?
Each prediction includes a category and confidence score. Predictions below the confidence level you choose are sent to a Review Queue, where a person can confirm or correct them. Corrections can be used by the next model version.
How can I use a trained model?
Text models can be used by filling CSV or Excel files, through the API on Essentials and higher plans, or from Claude and ChatGPT. Image models can categorize new pictures through ZIP files or sheets of picture links, and can also be used from a customer’s own apps.
What sensitive-data checks are available?
Text examples are checked for recognizable email addresses, phone and payment card numbers, IBANs, US Social Security numbers, IP addresses, passwords, API keys, and credentials in links. You can mask detected values, exclude affected rows, or keep them. Names, street addresses, and free-text medical details are not currently detected by these checks and must be removed manually.
What plans does StayCharted offer?
A Free workspace is available. Paid workspaces are priced monthly per workspace: Essentials is listed at $49 per month and Business at $99 per month, while Enterprise has custom pricing. The Vision add-on is available for Essentials and Business, with pricing listed separately by plan.
Quick Facts
- Product category
- Business AI classification and data mapping
- Inputs
- Categorized text rows, CSV/Excel files, labeled photos, ZIP folders, and picture links
- Outputs
- Categories, mappings, matches or routing suggestions with confidence information
- Review workflow
- Low-confidence results can enter a human Review Queue
- Plans
- Free; Essentials at $49/month; Business at $99/month; Enterprise custom pricing
- Billing basis
- Monthly pricing per workspace; paid plans allow unlimited team members
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