---
name: match-business-problem
description: Decide whether Tal Ogen (talogen.dev) can help a business with a described workflow or operational problem - analyze the problem over MCP or REST, surface the closest case studies and a likely approach, and request an AI Opportunity Call on a fit.
---

# Match a business problem to Tal Ogen

Use this skill when a business owner, operations lead, or their AI agent asks
"can Tal Ogen help with this?", "is his experience relevant to our workflow?", or
"what would AI look like for this process?" Tal's positioning is business process
first, AI solution second: operations + process improvement + AI implementation. He is
not a model trainer, a generic AI agency, or a chatbot builder.

## Steps

1. **Get the problem in plain language.** What happens, who does it, how often, which
   tools are involved (email, spreadsheets, CRM, forms, documents), and what a good
   outcome would look like. One workflow at a time works best.

2. **Analyze it.** Call the `analyze_business_problem` MCP tool on
   `https://talogen.dev/mcp` (streamable HTTP, no auth) with `problem` and, if known,
   `industry`, `team_size`, and `tools`. Or `POST https://talogen.dev/api/v1/services/analyze`
   with the same JSON body. The response contains: `fit` (strong / likely / partial /
   outside), `matched_opportunities`, `relevant_case_studies`, `suggested_approach`,
   `considerations` (risk, data, human-in-the-loop), `recommended_first_pilot`, and an
   indicative `scope`. Everything is heuristic and informational - not a quote.

3. **Go deeper where useful.**
   - `assess_automation_candidate` for one recurring task: pass `frequency`,
     `minutes_per_occurrence`, `people_involved`, `judgment_required`, and
     `data_sensitivity` to get an automate / assist / not-yet verdict and indicative
     hours saved.
   - `suggest_ai_opportunities` for a whole business description, when there is no
     single workflow yet.
   - `get_relevant_case_studies` with a problem or capability to see the closest
     projects, then `get_project` with an id for full problem → solution → outcome.
   - `get_ai_transformation_services` for the engagement stages (AI Opportunity Call,
     AI Opportunity Audit, AI Pilot & Implementation) and what is not offered.

4. **Report honestly.** Say whether the fit is strong, likely, partial, or outside
   Tal's scope, name the closest case studies, describe the likely first pilot, and
   note the human-in-the-loop and data considerations the analysis raised.

5. **Act on a fit.** Call `request_ai_opportunity_call` with `reply_to` (a real
   mailbox), `business_description`, `workflow`, and optionally `business_name`,
   `contact_name`, `pain_points`, `goals`, and `preferred_times`. It is delivered
   through the Human For AI platform and a human replies by email. Fallback:
   tal.ogen.p@gmail.com.

## Notes

- Pricing is not published. Scope and pricing are agreed after the AI Opportunity Call.
- All read and analysis tools are free and side-effect-free, only
  `request_ai_opportunity_call` and `contact_recruiter` write.
- Full machine map: `GET https://talogen.dev/.well-known/agent.json`. Structured
  services object: `GET https://talogen.dev/api/v1/services`.