Relevance AI consultants

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

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About Relevance AI

Relevance AI is an AI workforce platform that lets businesses build, deploy, and manage AI agents and AI-powered tools without writing code. It provides a visual interface for creating AI workflows that can perform tasks like data analysis, document processing, content generation, and customer communication autonomously or with human oversight.

The problem Relevance AI solves is the gap between wanting to use AI and having the engineering resources to build custom AI solutions. Most businesses know AI could help with tasks like classifying support tickets, extracting data from documents, or generating personalised content, but building these capabilities from scratch requires ML engineers and months of development. Relevance AI provides the building blocks (LLM connections, data transformations, tool integrations) in a no-code environment so that non-technical teams can create functional AI agents.

At Osher, we use Relevance AI alongside our own AI agent development services for clients who need AI agents deployed quickly. For simpler use cases, Relevance AI’s no-code platform can be configured directly. For more complex requirements, we build custom agents that extend beyond what no-code platforms offer. Our AI consulting team helps clients assess which approach fits their specific needs and budget. We’ve built AI agents for tasks ranging from medical document classification to application processing for talent marketplaces, and can advise on when a platform like Relevance AI is the right fit versus a custom build.

Relevance AI FAQs

Frequently Asked Questions

Common questions about how Relevance AI consultants can help with integration and implementation

What can you actually build with Relevance AI?

Relevance AI lets you create AI agents that perform specific tasks: classifying documents, extracting structured data from unstructured text, answering questions from a knowledge base, generating content based on templates, processing form submissions, and routing customer inquiries. Each agent is built by chaining together steps (LLM calls, data lookups, API calls, conditional logic) in a visual workflow builder. The agents can run autonomously on a schedule or be triggered by events.

How does Relevance AI compare to building custom AI agents from scratch?

Relevance AI is faster to get started with because you don’t need to write code, manage LLM API connections, or build infrastructure. You can have a working agent in hours rather than weeks. The tradeoff is flexibility: custom-built agents can be tailored to exact specifications, handle edge cases more precisely, and integrate deeply with proprietary systems. For straightforward use cases, Relevance AI is often sufficient. For complex, business-critical workflows, custom development may be the better path.

Which LLMs and AI models does Relevance AI support?

Relevance AI supports connections to major LLM providers including OpenAI (GPT-4), Anthropic (Claude), and others. You can choose which model powers each step in your workflow, so you could use a faster, cheaper model for simple classification and a more capable model for complex reasoning tasks within the same agent. This flexibility helps balance cost and performance based on the specific task requirements.

Can Relevance AI agents integrate with our existing business tools?

Yes. Relevance AI supports integrations with common business tools and provides API connectivity for custom integrations. Agents can read from and write to CRMs, project management tools, databases, and communication platforms. For integrations not natively supported, we connect Relevance AI agents to your broader automation infrastructure using tools like n8n through our system integration services.

Is Relevance AI suitable for processing sensitive data?

Relevance AI provides data handling controls and you should review their security documentation against your compliance requirements. For industries with strict data handling requirements (healthcare, finance, legal), you need to verify that Relevance AI’s data processing meets your regulatory obligations. In some cases, a self-hosted custom AI solution may be necessary for full data sovereignty. Our AI consulting team can help assess this.

What kind of ongoing maintenance do Relevance AI agents need?

AI agents built on Relevance AI need periodic monitoring and adjustment. LLM outputs can drift over time as models are updated, edge cases may surface that the original workflow doesn’t handle well, and business requirements change. We recommend reviewing agent performance regularly, monitoring output quality, and refining prompts and logic as needed. This is similar to maintaining any automation, just with the added variable of AI model behaviour.

How it works

We work hand-in-hand with you to implement Relevance AI

As Relevance AI 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 Relevance AI with integrate and automate 800+ tools.

Step 1

Process Audit

We review your current workflows to identify tasks that are good candidates for AI agent automation. This means looking at repetitive, rule-based tasks that currently require human judgment but follow predictable patterns: document classification, data extraction, content generation, inquiry routing, and similar operations. We assess volume, complexity, and accuracy requirements for each candidate task.

Step 2

Identify Automation Opportunities

Based on the audit, we rank the candidate tasks by automation potential. We consider factors like task volume (higher volume means more time saved), complexity (simpler tasks are easier to automate reliably), error tolerance (some tasks need near-perfect accuracy, others allow for human review), and data availability. We also assess whether Relevance AI’s no-code platform can handle each use case or whether custom development is needed.

Step 3

Design Workflows

For each selected task, we design the AI agent workflow: what inputs it receives, which LLM model handles each step, what logic gates and error handling are needed, and where outputs are delivered. We define the integration points with your existing systems, establish quality thresholds for automated decisions, and plan the human review process for cases where the agent’s confidence is below threshold.

Step 4

Implementation

We build and configure the AI agents in Relevance AI, connecting them to your data sources and output destinations. Each agent is tested with real data during development, and prompts are iteratively refined to improve output quality. Integration connections to CRM, project management, and communication tools are deployed, and trigger mechanisms (scheduled, event-based, or manual) are configured.

Step 5

Quality Assurance Review

We run each agent against a representative sample of real data and compare outputs against expected results. This includes measuring accuracy rates, identifying failure modes, testing edge cases, and verifying that integration endpoints receive correctly formatted data. Agents that don’t meet accuracy thresholds are refined through prompt engineering and workflow adjustments until they perform reliably.

Step 6

Support and Maintenance

After deployment, we monitor agent performance metrics including accuracy rates, processing times, and error frequencies. We adjust prompts and workflow logic as edge cases surface, update integrations when connected systems change, and adapt agents when business requirements evolve. Periodic quality audits ensure that agent outputs remain accurate as underlying LLM models are updated by their providers.

Transform your business with Relevance AI

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

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