Keyword search was manual, subjective,
and you never knew what you missed.
So in 2018 we built context search from scratch. Eight years later, three pillars carry the technology: our own unified data index, our own algorithms, our own servers in Braunschweig. No third-party LLMs in the critical path. No rented infrastructure. We own the entire value chain.
How one query becomes a ranked answer
No query syntax, no boolean trees. Four steps, every one runs on our own infrastructure.
Your input
Abstract. Claim draft. Product brief. Competitor paragraph. Plain language in any supported language.
Kwintely embedding
Our own model, trained on the 295M-document corpus since 2018, not a third-party API. Your query maps to a vector in our semantic space.
Context search
Against all 1B+ indexed vectors. Deterministic. Every result is a real document, linkable to its source. Nothing generated, nothing hallucinated.
Ranked by similarity
FTO · prior art · landscape · competitor monitoring · tech surveillance · de-duplication · foresight. Same engine, seven workflows. < 90 seconds.
The entire value chain, in-house
Every other IP-AI tool on the market rents at least one of these pillars, from OpenAI, from third-party data providers, from someone else's cloud. Kwintely owns all three.
Our unified data index
Patents, scientific papers, and clinical trials, one index, one semantic space.
- 295 million documents indexed
- 320,000 new documents added per week
- 100+ patent offices · global science · clinical registries
- Cross-language: a Japanese filing, a Chinese utility model, a German conference paper, searchable from the same query
- Curated and normalised in-house, not a bulk download from a third-party data vendor
Our algorithms
Neural networks and fine-tuned language models, trained on our corpus since 2018.
- Kwintely embedding model, our own, not an OpenAI / Cohere / third-party API
- Eight years of training-data curation and ranking-model iteration
- Deterministic ranking, not generative output, no hallucinations
- Learned the linguistic nuances of patents, papers, and trials, not the average of the internet
- Your queries never become training data for anyone else's model
Our servers
Everything runs on hardware we own, in Braunschweig, Germany.
- No third-party LLM APIs in the critical path
- No third-party cloud holding your queries or results
- Sovereign by construction, not by audit checklist
- On-prem deployment available for enterprise contracts with sovereign requirements
- Queries, results, exports, all stay on Kwintely infrastructure until you choose to download them
Context search ≠ LLM-RAG
The 2026 evaluator's real question isn’t "keyword vs AI", that’s covered on the homepage. The real question is: how is this different from RAG with an LLM underneath?
| Dimension | Generic LLM-RAG | Kwintely context search |
|---|---|---|
| Data corpus | Foundation model + whatever documents you dumped into a vector store last month. | 295M+ patents, papers and clinical trials, curated and updated weekly since 2018. |
| Model ownership | Fine-tuned OpenAI / Cohere / Anthropic via API. You rent inference. | Our own embedding + ranking models, trained on the corpus. No third-party API in the critical path. |
| Output guarantee | Generative. Can hallucinate citations, invent patent numbers, miscount claims. | Deterministic. Every result is a real document with a source link. |
| Data sovereignty | Your query goes to a third-party API. Logged, often used for model improvement. | Queries never leave Braunschweig. Your invention disclosure never trains anyone else's model. |
| Domain fluency | General-purpose. Struggles with niche technical terminology. | Trained on the linguistic nuances of patents, papers and trials. |
| Track record | Usually 12–18 months. Post-ChatGPT startup. | Eight years of operational iteration. Shipped before the category existed. |
The short version
We own the entire value chain.
Every other IP-AI tool rents at least one pillar, from OpenAI, from third-party data
providers, from someone else’s cloud. That rent shows up as hallucinated citations,
missing filings in niche domains, or silent data leakage to third-party APIs.
Kwintely
rents none of it.
See context search on one of your real questions.
A fifteen-minute clarity call with a founder who built the engine.