Tisane Labs consultants

Q: What languages does Tisane Labs support for text analysis?

Tisane supports over 30 languages for core NLP tasks including sentiment analysis, entity extraction, and content classification. This makes it particularly useful for Australian businesses operating in multilingual markets or processing customer communications in languages other than English — common in sectors like tourism, education, and international trade.

Q: How accurate is Tisane Labs compared to building custom NLP models?

For general text analysis tasks, Tisane delivers solid accuracy out of the box without the cost and time of training custom models. Where custom models have the edge is domain-specific terminology — medical, legal, or industry jargon that general NLP tools may misinterpret. We help clients evaluate this trade-off and often start with Tisane, then fine-tune where needed.

Q: Can Tisane Labs be integrated into our existing data pipeline?

Yes. Tisane provides REST APIs that slot into any data pipeline. We typically integrate Tisane as a processing step within n8n workflows — text comes in from a source system, gets analysed by Tisane, and the structured output routes to your database, CRM, or dashboard. The API-based approach means it works with virtually any tech stack.

Q: What is the difference between Tisane Labs and using ChatGPT for text analysis?

ChatGPT is a generalist — it can do text analysis but was not optimised for it. Tisane is purpose-built for NLP tasks like entity extraction and sentiment scoring, which means faster processing, more consistent output structure, and lower cost per API call at volume. For production pipelines processing thousands of documents, that consistency and cost efficiency matters.

Q: What volume of text can Tisane Labs process and what does it cost?

Tisane offers tiered pricing based on API call volume. For most mid-sized deployments — say processing a few thousand documents per day — costs are modest compared to the manual labour they replace. We help clients estimate realistic costs based on their actual text volumes and processing needs during the scoping phase.

We can help you automate your business with Tisane Labs and hundreds of other systems to improve efficiency and productivity. Get in touch if you’d like to discuss implementing Tisane Labs.

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About Tisane Labs

Tisane Labs provides natural language processing (NLP) APIs that handle text analysis tasks like sentiment detection, entity extraction, language identification, and content moderation across dozens of languages. Unlike general-purpose AI models, Tisane’s APIs are purpose-built for parsing unstructured text at scale — making them useful for businesses that need to process customer feedback, support tickets, social media mentions, or regulatory documents automatically.

The practical value shows up when you have large volumes of text that humans cannot realistically review manually. A customer support operation dealing with thousands of tickets per week can use Tisane to automatically categorise issues, flag urgent sentiment, and route tickets to the right team. Content platforms can use it for moderation. Research teams can use it to extract structured data from messy document collections.

For Australian businesses building AI agent workflows, Tisane Labs fits neatly as a processing step within a larger pipeline. You might feed customer emails through Tisane for sentiment and entity extraction, then route the structured output into your CRM or ticketing system through n8n. We have built similar text processing pipelines for clients — our medical document classification case study shows what this looks like in practice.

If you are sitting on unstructured text data and wondering how to extract value from it, get in touch. We can assess whether Tisane Labs or an alternative NLP approach makes sense for your use case.

Tisane Labs FAQs

Frequently Asked Questions

Common questions about how Tisane Labs consultants can help with integration and implementation

What languages does Tisane Labs support for text analysis?

How accurate is Tisane Labs compared to building custom NLP models?

Can Tisane Labs be integrated into our existing data pipeline?

What is the difference between Tisane Labs and using ChatGPT for text analysis?

How does Tisane Labs handle content moderation for Australian compliance requirements?

Tisane’s moderation API detects abusive language, hate speech, and problematic content across multiple languages. For Australian businesses needing to comply with the Online Safety Act or industry-specific standards, Tisane provides a programmable layer you can tune to your specific moderation policies. We can integrate this into your content workflows with appropriate escalation rules.

What volume of text can Tisane Labs process and what does it cost?

How it works

We work hand-in-hand with you to implement Tisane Labs

As Tisane Labs consultants we work with you hand in hand build more efficient and effective operations. Here’s how we will work with you to automate your business and integrate Tisane Labs with integrate and automate 800+ tools.

Step 1

Define Your Text Processing Requirements

We start by understanding what text data you have, where it comes from, and what you need to extract or classify. Whether it is customer support tickets, survey responses, social media mentions, or regulatory documents — the use case shapes everything that follows.

Step 2

Evaluate Tisane Labs Against Your Data

We run sample datasets through Tisane’s APIs to validate accuracy for your specific content type and domain language. This proof-of-concept step tells us whether Tisane handles your data well out of the box or if adjustments are needed before committing to a full build.

Step 3

Design the Processing Pipeline Architecture

Based on the evaluation results, we design how Tisane fits into your broader data workflow. This covers data ingestion, API call orchestration through n8n, output formatting, error handling, and where the processed data lands — your database, CRM, analytics platform, or downstream AI agents.

Step 4

Build the Integration and Processing Workflows

We build out the pipeline — connecting your data sources to Tisane, handling the API calls at the right volume and frequency, transforming the output into structured formats your systems can use, and routing results to the appropriate destinations with full logging.

Step 5

Validate Accuracy and Handle Edge Cases

We test the pipeline against a broad range of real data, paying attention to edge cases — ambiguous language, mixed-language text, domain-specific terminology. Where accuracy drops, we adjust the pipeline logic or add post-processing rules to catch common misclassifications.

Step 6

Deploy and Establish Monitoring

We deploy the pipeline to production with monitoring dashboards that track processing volume, accuracy metrics, and API costs. Your team gets training on interpreting the outputs and managing the system day-to-day, with documentation covering common maintenance tasks.

Transform your business with Tisane Labs

Unlock hidden efficiencies, reduce errors, and position your business for scalable growth. Contact us to arrange a no-obligation Tisane Labs consultation.

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