Automation and integrations decision

TextCortex

A narrow self-hosted RAG + simple agent workflow is achievable by a small team, but reproducing TextCortex's enterprise-grade certifications, EU data-sovereignty guarantees, and broad connector/extension ecosystem would be expensive and slow—so build a focused replacement for core workflows and keep paying for full enterprise features.

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Subscription$29.99/month ✓ verified
Initial build80 hours
Monthly upkeep12 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

  • Provide retrieval-augmented generation over company data and let users create simple automations/agents that run model calls on retrieved context.

What it still won’t have

  • SOC 2 / ISO 27001 certified hosting and attestation
  • EU-only (GDPR-focused) hosting and data-sovereignty guarantees
  • Prebuilt model-hub integrations and variety of LLM providers
  • Large catalogue of enterprise connectors and polished UX/extension

What remains hard

  • Compliance and regulationSOC 2 & ISO 27001 Certified
  • Compliance and regulationBuilt for European data sovereignty. GDPR-compliant, ISO and SOC2 certified
Read the build prompt

First-year cost

Keep paying

Paying ischeaper in year one.

On cash alone, building overtakes the subscription at 7 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, self-hosted AI agent platform using FastAPI (Python) backend, PostgreSQL for relational data, Milvus (or Pinecone) for vector storage, React for a simple visual agent editor, and the OpenAI API for model calls. In scope: document ingestion (file upload and parsing), vector indexing, a RAG endpoint that returns retrieval + LLM result, a basic visual editor to define single-step agents (retrieve → call model → return result), scheduled job runner for recurring agents, user accounts with single-seat billing flag, and a simple web UI to invoke agents. Out of scope: SOC2/ISO certification, enterprise SSO/SCIM, 30k-app browser extension catalogue, advanced multi-tenant governance. Provide error handling, input validation, and unit tests for ingestion, retrieval, and the agent execution path.
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 →

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! 2 moats quoted from the page