USE CASES
Does your AI work for the people using it?
A system can perform well overall while failing in specific conversations.
The TableAI helps teams investigate how their AI handles AAVE and Black cultural context within a defined product, workflow, or dataset.
Start with one question. Get documented findings your team can act on.
Customer support that understands the request.
Customers should be able to ask for help in the language they use.
We evaluate whether your assistant understands the problem, applies the right information, and helps the customer reach an appropriate resolution.
An evaluation can examine whether the system:
- Preserves the customer’s meaning and intent.
- Answers accurately using your policies or knowledge base.
- Asks useful questions without creating unnecessary friction.
- Avoids unsolicited language correction and inappropriate assumptions.
- Provides consistent service across equivalent requests.
- Recognizes when human support is needed.
Moderation that accounts for context.
A word or phrase alone may not explain what a conversation means.
We evaluate how moderation systems handle dialect, surrounding conversation, quotation, humor, and other context relevant to the agreed policy.
The review examines both harmful content that passes through and harmless content that is incorrectly restricted.
Transcripts that preserve meaning.
A transcription error can become a different request, an inaccurate summary, or the wrong next action.
We scope reviews of speech recognition and downstream summaries to identify where meaning is lost or changed.
Depending on the project, the evaluation can examine omissions, substitutions, important details, speaker attribution, and whether the resulting summary reflects the conversation.
Annotation with reasoning behind the labels.
Useful labels require clear definitions and informed judgment.
We scope annotation projects around the distinctions your team needs to recognize, including intent, meaning, response quality, contextual appropriateness, and harmfulness.
Guidelines define how ambiguous cases and reviewer disagreements will be handled.
What your team receives:
Annotation guidelines, reviewed records, quality notes, and a documented handoff in an agreed format.
Responses that fit the situation.
Understanding AAVE does not automatically mean an assistant should use it.
We review generated responses for usefulness, tone, cultural context, unnecessary correction, stereotyping, and misplaced familiarity.
The question is whether the response serves the user and fits the setting.
What your team receives:
Annotated responses, explanations of concerns, suggested revisions, and product-specific guidance.
Keep evaluating as your product changes.
A new model, prompt, policy, or knowledge base can change the customer experience.
Following an initial engagement, The TableAI can scope recurring evaluations to compare releases, retest documented failures, and investigate new patterns.
Start with the decision you need to make.
Tell us what your system does, where you have concerns, and what your team needs to understand before its next release.
We’ll discuss the evaluation question, required expertise, deliverables, and project terms.