Customer support decision

Groove

A capable engineer can build a narrow subset (inbox + RAG-driven drafts + routing) cheaply, but reproducing Helply's full platform (many integrations, ROI attribution, enterprise onboarding, and production polish) is a much larger effort better suited to buying.

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Subscription$250/month ✓ verified
Initial build80 hours
Monthly upkeep20 hours + $200
Evidence3/3 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 messages, assemble account+KB context, generate AI draft or automated resolution, apply routing/escalation and record ticket outcomes

What it still won’t have

  • Per-ticket billing, enterprise contracts and onboarding handled by vendor
  • Pre-built integrations and connectors to many B2B systems
  • Vendor-provided ROI tracking, SLAs, and hands-on onboarding
  • Polish, monitoring, and reliability of a production SaaS platform

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

Keep paying

Paying ischeaper in year one.

On cash alone, building overtakes the subscription at 1 seat.

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-native B2B support system using Node.js (Express), Postgres, and a retrieval-augmented LLM (OpenAI or compatible). In scope: (1) ingest email and a web chat webhook into a unified inbox; (2) fetch and attach account context from a mock CRM (Postgres table) and KB (vector index using pgvector); (3) implement RAG + prompt templates to produce reply drafts and a confidence score via the LLM API; (4) apply simple routing rules to assign or escalate tickets and tag churn/upsell signals; (5) record tickets, replies, and signals in Postgres and provide a small web UI to review drafts and send replies. Out of scope: enterprise billing, per-ticket metering UI, dozens of third-party connectors, advanced analytics dashboards. Include error handling, retries for external APIs, and unit tests for ingestion, RAG retrieval, and routing logic.
How we checked5 sources · 3/3 runs agreed · evidence score 67

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
  • 3/3 assessment runs agreed+4
  • Evidence score67

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✓ 3 independent runs, one answer✓ Citations limited to fetched pages! 1 moat recorded