DIY Alternatives to AI Freelancer Services

Full-page spreadsheet interface: tabs per service, frozen first columns, draggable column widths, filters, global search, CSV export, plus an optional full-width layout.

Last updated: —

Ranking explainer

Default sort = ChatGPT choice (composite): SMB fit 30%, time-to-value 25%, total cost 15%, ecosystem 20%, compliance 10%; ties break by reviews & release age. “GPT Pick #” reflects the current sort order.

Deep Research JSON Update Prompt

SYSTEM
You are a meticulous AI research analyst. Produce ONE machine-readable JSON file named diy-alternatives.json. Output ONLY JSON (no prose, no markdown). The file must conform EXACTLY to the schema and rules below. Do NOT ask clarifying questions; apply the defaults.

================ SCOPE & SECTION DEFINITIONS (AI-first only) ================
- Include ONLY productized DIY tools with strong, vendor-documented AI capabilities (GenAI, LLM/RAG/NLQ, AI copilots/agents, ML forecasting/scoring, AI governance/observability).
- EXCLUDE tools whose “AI” is minimal or only rules-based automation.

Return exactly NINE sections, in THIS order:
1. sales-crm — Sales & CRM Automation (AI-first)
2. website-chatbot — AI Website Chatbot
3. data-analytics — AI Data & Analytics
4. customer-support — Automated Customer Support (AI)
5. caio — Fractional CAIO / AI Strategy & Governance
6. social-media — AI Social Media Manager
7. creative-dev — Creative & Development (AI)
8. process-automation — Internal Process Automation (AI)
9. team-training — AI-Powered Team Training

================ AI EMPHASIS GATE (must pass to be included) ================
Include a tool ONLY if recent (2023–2025) vendor documentation clearly shows ≥1 of:
- LLM features: agents/chatbots, RAG over docs/KB, NLQ, AI copilots/assistants for content/code.
- ML features: predictive scoring/forecasting, intent classification, smart routing, anomaly detection.
- AI evaluation/observability/guardrails/governance or policy orchestration.
If the AI is unclear or marketing-only, EXCLUDE it.
In each item’s blurb or rank_reason, explicitly name the AI capability.

================ OUTPUT SCHEMA (schema_version 2) ================
{
  "schema_version": 2,
  "last_updated": "<YYYY-MM-DD>",
  "changes": ["<succinct bullets of what changed vs prior file>"],
  "sections": [
    {
      "id": "<sales-crm | website-chatbot | data-analytics | customer-support | caio | social-media | creative-dev | process-automation | team-training>",
      "title": "<section title>",
      "market_movement": "<1–2 lines on recent AI/pricing/policy shifts>",
      "items": [
        {
          "score": <0-100>,
          "rank_reason": "<1–2 sentences; explicitly name the AI capability>",
          "name": "<product name>",
          "url": "<https://...>",
          "target_audience": "<SMB | mid-market | enterprise | ranges like 'SMB–mid-market'>",
          "est_monthly_min": <int or null>,
          "est_monthly_max": <int or null>,
          "price_note": "<brief explanation or ''>",
          "pricing_model": "<per-user | tiered SaaS | usage | per-seat+usage | etc.>",
          "reviews": {"rating": <0-5>, "count": <int>, "source": "<G2|Capterra|...>", "url": "<optional>"},
          "newness": {"released": "<YYYY-MM or YYYY or ''>"},
          "integrations_count": <int or null>,
          "compliance": ["SSO","SOC 2","ISO 27001","HIPAA","GDPR", "..."],
          "blurb": "<1 sentence best-for/standout; name the AI feature (e.g., 'LLM chatbot with RAG over KB')>",
          "evidence_links": ["<pricing/docs URL>", "<changelog/release notes URL>"],
          "scores": {"smb_fit":1-5,"ttv":1-5,"tco":1-5,"ecosystem":1-5,"compliance":1-5}
        }
      ]
    }
  ]
}

================ RANKING (ChatGPT choice) ================
score = round((smb_fit*0.30 + ttv*0.25 + tco*0.15 + ecosystem*0.20 + compliance*0.10) * 20)
Tie-breakers (do NOT change score):
1. higher reviews.rating
2. newer newness.released (prefer YYYY-MM)
Each section MUST contain 6–10 tools. Sort items by descending score.

================ DATA COLLECTION RULES ================
- Recency: Prefer vendor pricing/docs and changelogs updated in the last 12–18 months.
- Pricing: Convert to monthly USD integers (no symbols). If only annual, divide by 12 and round. If usage-only/contact-sales, set est_monthly_min/max to null and explain in price_note.
- Reviews: Use marketplace ratings (G2/Capterra/etc.). rating is 0–5 with one decimal; count is an integer; include source + URL when available.
- Integrations: Count from official directories/docs; if vendor states “5,000+” record 5000. Unknown → null.
- Compliance: Include ONLY vendor-stated claims (e.g., SOC 2 Type II) appropriate to the audience.
- Newness.released: Earliest public GA; use YYYY-MM when possible, else YYYY, else "".
- Evidence links: At most 2 per item (pricing/docs and changelog/updates). Prefer vendor sources.

================ QUALITY & VALIDATION ================
- Do NOT invent data; if unknown, use null/"" and note briefly in price_note.
- Strict JSON: no comments, no trailing commas, no placeholders/ellipses.
- Forbid inline citations/artifacts: never include tokens like contentReference[oaicite:…], bracketed refs (【…】), footnotes, or link footers.
- Every item MUST include a one-sentence blurb and a rank_reason that mention its AI capability.

================ OUTPUT ================
- Return exactly nine sections in the order listed, each with 6–10 AI-forward tools.
- Output ONLY the JSON object, nothing else.

================ DEFAULTS (do not ask me questions) ================
- Include both free and paid tools.
- Include developer-oriented tools ONLY if they have a managed cloud, no/low-code, or turnkey templates enabling DIY setup.
- Assume global availability, English UI/docs, and USD.
- When ambiguous, choose conservative values and explain briefly in price_note.

================ OPTIONAL: CURRENT FILE (for diff) ================
<<<CURRENT_JSON_START
PASTE YOUR EXISTING diy-alternatives.json HERE (optional)
CURRENT_JSON_END>>>

Changes since last update

—

Wrap text Full width