Customer support decision

Hiver

A narrow replacement that auto-generates and serves a help center and reply drafts is realistic for a single developer in ~1 week, but reproducing Hiver’s enterprise compliance, broad omnichannel connectors, and mature agent/workflow features is impractical without substantial investment.

Visit website
You pay

$35/mo

$420/yr

Per seat. Read off the official pricing page.

You’d pay instead

$50one-off30 h to build

$100/mo8 h/mo upkeep

On cash alone, building overtakes the subscription at 3 seats.

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. All Hiver alternatives, with the arithmetic →

What a replacement has to do

  • Ingest conversations, extract Q&A pairs, publish searchable help articles, and surface suggested answers / drafts for agents using an LLM.

What it still won’t have

  • Enterprise compliance and attestation (SOC2/ISO/HIPAA) out of the box
  • Large catalog of native integrations and omnichannel connectors
  • Mature multi-agent workflows, routing, and admin tooling
  • Hosted voice/SMS and pooled minutes functionality
  • Hiver-managed security, uptime SLA, and vendor support

What remains hard

  • Compliance and regulationHiver is GDPR, SOC 2 Type II, ISO 27001, CCPA, and HIPAA compliant.
Read the build prompt

First-year cost

Keep paying

Paying is—cheaper in year one.

On cash alone, building overtakes the subscription at 3 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 single-tenant help center service using Node.js + Express, Postgres, and Elasticsearch; integrate with an LLM (OpenAI) for text cleaning and article drafting. In scope: ingest ticket text via a webhook endpoint, extract Q&A pairs and metadata, generate draft help-articles and suggested reply drafts via the LLM, store and index articles, provide a simple React UI to review/publish articles and to surface suggested replies inside a ticket view, and implement search API. Out of scope: hosted voice/SMS, enterprise SSO and compliance attestation, and building 1,000+ native integrations. Include error handling, rate-limit LLM calls, and unit/integration tests for ingestion, LLM pipeline, storage, and search.
How we checked5 sources · 3/3 runs agreed · evidence score 64

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
  • Hard moats found in the evidence-3
  • Evidence score64

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 →

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 quoted from the page