CRM and sales decision

Kula

A capable engineer can build a useful, smaller AI-augmented ATS and basic workflows (resume parsing, scoring, scheduling, notes) using open-source components, but reproducing Kula’s full product (enterprise compliance, 100+ integrations, audited fairness, and turnkey migration/support) is not realistic for one person to match.

Visit website
You pay

$426.67/mo

$5,120/yr

Read off the official pricing page.

You’d pay instead

$100one-off70 h to build

$100/mo6 h/mo upkeep

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

No open-source build does this yet

Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.

What a replacement has to do

  • Ingest candidate applications, parse resumes and enrich profiles, score and shortlist candidates, schedule interviews, capture interview notes/transcripts, and query hiring analytics.

What it still won’t have

  • SOC 2 Type II compliance and formal GDPR-ready enterprise security attestation
  • 100+ built integrations ready out of the box
  • Tailored onboarding, migration services, and dedicated Slack/in-app support
  • Ongoing fairness and bias audits (Warden AI) and enterprise compliance reporting
  • Polished, production-hardened UI and cross-team collaboration features

What remains hard

  • Compliance and regulationSOC 2 Type II compliance and GDPR-ready data protection
Read the build prompt

First-year cost

Keep paying

Paying is—cheaper 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 ATS using PostgreSQL, FastAPI (Python), React, and Redis: implement candidate ingestion (resume upload + text extraction), a resume parser to extract name/email/experience (use existing NLP libraries), a candidates/jobs pipeline stored in Postgres with REST APIs, a simple scoring pipeline using embeddings (OpenAI or open-source) and rule-based weights, Google Calendar integration for interview scheduling, audio recording + transcription integration, and a lightweight queryable analytics endpoint (question → SQL). Out of scope: enterprise SOC2 certification, 100+ third-party integrations, guided migration service. Provide CI, basic auth/SSO hooks, error handling, and unit + integration tests.
How we checked2 sources · 3/3 runs agreed · evidence score 57

How the score was reached

  • Partly verdict base52
  • 2 cited sources+1
  • Price verified on pricing page+3
  • 3/3 assessment runs agreed+4
  • Hard moats found in the evidence-3
  • Evidence score57

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 · 2

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 quoted from the page