KobiGPT vs Claude
Long context or retrieval? As your archive grows, the two approaches produce very different cost curves.
TL;DR
Choose KobiGPT when you need specialist assistants with citations from your files, TR/EN support, and predictable Kobi Kredi usage. Claude may win on ecosystem lock-in—validate with your IT checklist.
Quick facts
- Citations
- KobiGPT: document snippets
- Deployment
- SaaS + self-hosted path
- Pricing model
- Published SME tiers + Kobi Kredi
- Languages
- Turkish and English
Why teams choose KobiGPT
- Compare total cost including indexing and support hours.
- Test answer quality on your own PDFs and policies.
- Check data residency and KVKK alignment.
- Use our compliance checklist tool before rollout.
Product facts
- Ücretsiz plan
- 100 doküman · 2 departman · 120 Kobi/ay(PLAN_CONFIG)
- Starter
- 1000 doküman · 5 departman · 1000 Kobi/ay(PLAN_CONFIG)
- Pro
- 12500 doküman · 25 departman · 12500 Kobi/ay(PLAN_CONFIG)
- Doküman yükleme maliyeti
- 0.1 Kobi(packages/types/src/plan-config.ts → TOKEN_COSTS.DOCUMENT_UPLOAD)
- AI mesajı asgari maliyeti
- 0.1 Kobi(packages/types/src/plan-config.ts → KOBI_MIN_TURN)
Long context windows do not replace RAG
Context windows in modern models have grown markedly, which invites the thought "I can just put all the documents in context now". For small archives that is true; feeding a few hundred pages directly into context is a practical approach.
As the archive grows, two problems appear. First, cost: sending the entire archive on every query is many times more expensive than sending only the relevant passages, and that cost repeats on every message. Second, precision: as irrelevant text grows in a wide context, it becomes harder for the model to weight the right passage.
What the retrieval approach does
KobiGPT splits documents into chunks, converts each chunk to a vector with a multilingual embedding model, and retrieves only the most relevant chunks at query time. Even across a 12,500-document archive, every query then runs with a small, bounded context.
This approach has its own cost: if the retrieval step picks the wrong chunk, the answer is incomplete. That is why citation is a mandatory design element — the user sees which chunk produced the answer and can notice a bad retrieval.
The product layer: scope, roles, metering
Beyond any model comparison, the real difference is again the product layer. Department-level scope separation, role-based access, per-company vector isolation, and usage metering in Kobi Kredi are not things a chat interface provides.
The plan tiers make this concrete: the Pro plan covers 12500 documents, 25 assistants, and 50 users. At that scale document management becomes a problem in its own right, and a chat interface does not solve it.
FAQ
Which is faster to pilot?
KobiGPT targets same-week pilots with direct document upload.
Can we use both?
Some teams keep Copilot for M365 and KobiGPT for SME docs.
Hallucinations?
RAG reduces but does not eliminate—always verify citations.
Enterprise SLA?
Contact sales for enterprise; cite public pages only.
We have a small document set — do we need RAG?
For a handful of documents, pasting them into context may be enough. RAG's value appears as the archive grows and as multiple users and scope separation become necessary.
How do we know an answer is incomplete?
Every answer shows which document chunks produced it; if the source you expected is not listed, the retrieval step did not select that chunk.
Comparison
| Feature | KobiGPT | Alternative |
|---|---|---|
| Document RAG | Core | Varies |
| Assistant templates | Yes | Varies |
| SME pricing page | Public | Often sales-led |
| Self-hosted | Optional | Varies |