Implementation BlueprintExecution layer

Employbl AI Research Retainer Setup (5-7 days)

A delivery playbook for productizing Employbl's MCP-compatible API into a client-facing research and recruitment intelligence retainer, where agencies configure AI assistants to query company, job, and funding data for their clients. Time: 5-7 days.

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Blueprint

Employbl AI Research Retainer Setup (5-7 days)

A delivery playbook for productizing Employbl's MCP-compatible API into a client-facing research and recruitment intelligence retainer, where agencies configure AI assistants to query company, job, and funding data for their clients.

Prerequisites
  • Employbl account with API access and a valid token for the $25 monthly authentication plan
  • Client's preferred AI assistant (ChatGPT, Claude, Cursor, or VS Code) with MCP support enabled
  • A pilot client use case, such as target company list generation or job market analysis
  • Access to Employbl's documentation for MCP server setup and tool endpoints
  • Internal testing environment with the free tier's 100 calls to validate queries
Execution Timeline
  • 1.Create Employbl account and generate API token under the $25 monthly authentication plan
  • 2.Set up MCP server connection in a test AI workstation (e.g., Claude Desktop or Cursor)
  • 3.Run 10-20 test queries against company search and job search tools to verify response quality
  • 1.Configure client's AI assistant with Employbl MCP server using the client's API token
  • 2.Define natural-language query templates for company research, filtering by sector, funding, and tech stack
  • 3.Test job search queries with filters for title, location, remote status, seniority, salary, and posting recency
  • 1.Build a saved workflow for generating AI-summarized company profiles using Employbl's company comparison and summary tools
  • 2.Create a job market analysis template that pulls 75,000+ active listings and aggregates by industry or company stage
  • 3.Document query examples for funding round tracking, using the 75,000+ funding rounds dataset
  • 1.Train client team on crafting natural-language prompts for daily research tasks
  • 2.Set up a feedback loop to refine query templates based on client's specific needs (e.g., investor filters, tech stack)
  • 3.Deliver a standard operating procedure document for using Employbl within the client's AI assistant
  • 1.Run a pilot week with the client, monitoring API usage and rate limits under the $25 plan
  • 2.Adjust query templates and filters based on pilot results, such as adding salary thresholds or remote-only filters
  • 3.Prepare a handoff report summarizing query performance and recommended next steps
  • 1.Finalize retainer scope, including monthly query volume and support level
  • 2.Set up billing and invoicing for the retainer, factoring in Employbl's $25 monthly cost
  • 3.Create a client-facing dashboard or report template that showcases insights derived from Employbl data
  • 1.Conduct a close-out meeting with the client to review pilot outcomes and gather feedback
  • 2.Document lessons learned and update internal playbook for future Employbl deployments
  • 3.Transition to ongoing support, including periodic query optimization and data refresh checks
$25 to $205 per month (Employbl authentication and job search plans) plus agency setup effort5-7 days
ROI Logic

An agency can charge a $2,500 setup fee for a 20-hour engagement, while Employbl's monthly cost is only $25 for authentication and $180 for job search access. This yields a high margin on the initial setup, and ongoing retainers can be priced at $500-$1,000 per month, covering the tool cost and generating significant profit.

Deliverables
  • Configured MCP server connection in client's AI assistant (ChatGPT or Claude)
  • 5 templated natural-language query workflows for job search and company lookups
  • Standard operating procedures document for daily research tasks
  • Pilot report with query performance metrics and usage analytics
  • Client handoff guide with prompt examples and troubleshooting steps
Definition of Done

Client's team is independently running Employbl-powered research queries in their AI assistant, and the agency has delivered a documented retainer workflow with measurable usage and client sign-off.

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