Customer support decision

Tidio

A single developer can build a limited AI chat + RAG helpdesk that covers basic workflows, but replicating Tidio’s integrations, managed Lyro optimizations, enterprise compliance, scale, and polished analytics is costly—keep paying if you need those features.

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Subscription$24.17/month ✓ verified
Initial build30 hours
Monthly upkeep10 hours + $100
Evidence2/3 runs agree

Open-source builds that already do this

Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need — the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship.

What a replacement has to do

  • Receive customer message → look up support knowledge (KB/FAQ/webpage/PDF) → generate reply with an LLM → send reply via chat channel and optionally create/update ticket / escalate to human.

What it still won’t have

  • Managed Lyro AI optimizations and guaranteed resolution SLAs
  • Built-in 120+ third-party integrations and platform-native connectors
  • Hosted analytics, reporting dashboards, and advanced Copilot features
  • Compliance and managed security guarantees (SOC2/HIPAA) and premium support
  • Scale, reliability, and polished multi-channel routing out of the box

What remains hard

  • Compliance and regulationSOC 2 & HIPAA compliance
  • Brand trustTrusted by 300,000+ businesses:
Read the build prompt

First-year cost

Keep paying

Paying ischeaper in year one.

On cash alone, building overtakes the subscription at 5 seats.

Paid seatsseats

Money you would actually spend

Keep paying

Subscription price × seats × 12

Build it

AI build APIs + hosting

Time you would spend

What you would spend

What we assumed

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet
Build a minimal AI customer-support stack using Node.js (Express) backend, Postgres for storage, a simple React web chat widget, and OpenAI API for responses. In scope: persistent conversation storage, ingesting FAQ/PDF/URL documents into an embeddings index (use open-source vector DB like SQLite+FAISS or pgvector), retrieval-augmented generation prompts, human handoff to an admin UI, and a basic flows/auto-reply rule engine. Out of scope: multi-channel native integrations (WhatsApp/Instagram), enterprise compliance (SOC2/HIPAA), advanced analytics dashboards, and managed scaling. Include error handling, rate-limit/backoff for the LLM calls, authentication for the admin UI, and unit/integration tests for the core flows.
How we checked5 sources · 2/3 runs agreed · evidence score 60

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 5 cited sources+3
  • Price verified on pricing page+3
  • Hard moats found in the evidence-3
  • Evidence score60

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time — so the same evidence always produces the same number.

How scoring works →

Cited sources · 5

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ Price read off the page! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 2 moats quoted from the page