Swiftask integrates with Relevance AI to provide search based on meaning rather than keywords. Your AI agents finally access the true value of your data.
Result:
Turn massive volumes of documents into precise and instant answers for your teams.
Keyword search limits your AI's effectiveness
Traditional search fails when language is nuanced. If a user asks a question without using the exact terms found in your documents, they get irrelevant results. This technological friction prevents your teams from accessing the right information at the right time.
Main negative impacts:
Through the Swiftask and Relevance AI integration, your agents use vector search to understand the intent behind every query. They navigate your data to extract the most relevant answer, even if the vocabulary differs.
BEFORE / AFTER
What changes with Swiftask
Limits of classic search
An employee searches for 'refund policy'. If the document mentions 'return conditions', the classic engine returns nothing. The employee must try several variations, losing time and patience.
Power of semantic search
The AI agent understands that 'refund' and 'return' are semantically linked. It instantly accesses the correct section of your knowledge base and synthesizes the answer for the user.
Deploying semantic search in 4 steps
STEP 1 : Index your data in Relevance AI
Connect your document sources to Relevance AI to create your vector indexes. The platform transforms your raw data into semantic vectors.
STEP 2 : Connect Relevance AI to Swiftask
Integrate Relevance AI as a primary knowledge source in your Swiftask agent via our native connector.
STEP 3 : Configure the intelligent agent
Define the agent's behavior: how it should query the index and format responses based on extracted data.
STEP 4 : Go live and refine
Activate your agent. Use user feedback in Swiftask to refine response accuracy over time.
Vector search capabilities
The agent analyzes the semantic distance between the user's question and your indexed document segments, ensuring maximum relevance.
Each action is contextualized and executed automatically at the right time.
Each Swiftask agent uses a dedicated identity (e.g. agent-relevance-ai@swiftask.ai ). You keep full visibility on every action and every sent message.
Key takeaway: The agent automates repetitive decisions and leaves high-value actions to your teams.
Strategic advantages for the company
1. Increased precision
Get answers based on the overall meaning of the query, eliminating noise from irrelevant results.
2. Improved employee experience
Give your teams immediate access to company knowledge, reducing frustration and wasted time.
3. Knowledge scalability
Manage millions of documents without degradation in search speed or accuracy.
4. Reduced support costs
Automate answers to repetitive questions with a knowledge base finally exploitable by AI.
5. Contextual adaptability
The agent adapts to your industry's specific jargon thanks to Relevance AI's learning capabilities.
Data security and indexing
Swiftask applies enterprise-grade security standards for your relevance ai automations.
To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.
RESULTS
Performance of your AI search engine
| Metric | Before | After |
|---|---|---|
| Result relevance | Low (keywords only) | Very high (semantic understanding) |
| Search time | Several minutes | A few seconds |
| Answer success rate | Random | Optimized and measurable |
| Volume of data processed | Limited | Virtually unlimited |
Take action with relevance ai
Turn massive volumes of documents into precise and instant answers for your teams.