Open - Source AI
For Information Extraction
Knowledgator, powered by open-source, aims to make scientific and organizational knowledge more accessible.
*Try our models in seconds.
[ Our Principles ]
(The foundation for our actions and decisions)
Our core is open-source. We believe in advancing AI through transparency and shared innovation, inviting you to join us in shaping technology for the common good.
We don't follow mainstream technological methods. We build and research faster, more reliable, and accurate technologies to make human and organizational knowledge more accessible.
Access and tailor open ML solutions with unparalleled ease, ensuring our models integrate smoothly into your workflow.
[ Inference Speed ]
(Real-time results, not minutes of waiting)
Our non-generative approach delivers answers in milliseconds, while LLMs take seconds to even begin streaming a response.
Inference Latency
NER
KnowledgatorFast
~120 ms
GPT-4oSlow
~3,200 ms
Classification
KnowledgatorFast
~85 ms
GPT-4oSlow
~2,800 ms
Relation Extraction
KnowledgatorFast
~280 ms
GPT-4oSlow
~4,500 ms
faster on average - Knowledgator completes full inference before LLMs finish their first token generation pass.
[ Core Capabilities ]
(Multiple tasks, one unified pipeline)
Knowledgator provides purpose-built models for every stage of information extraction, from identifying entities to producing clean, structured data.
Named Entity Recognition
Detect and classify entities like people, organizations, locations, dates, and custom types with near-perfect accuracy.
Prof. James CarterPER published a study at the Stanford Biomedical Research InstituteORG in CaliforniaLOC, securing $4.7 millionMON in funding by June 2024DATE.
Relation Extraction
Map how entities are connected — like which gene regulates another gene — and build knowledge graphs automatically.
Classification
Categorize documents, paragraphs, or sentences into any label set — zero-shot, no fine-tuning required.
"The FDA approved a new treatment for patients with advanced melanoma, marking a breakthrough in immunotherapy."
Text → Structured JSON
Convert any free-text into clean, schema-conforming JSON ready for databases, APIs, and downstream systems.
"Invoice #4821 from Acme Corp, dated Jan 12 2025, total €14,200. Payment due within 30 days. Contact: example@gmail.com"
[ Perfomance ]
(Get reliable results validated in the real world)
Designed and tailored to information extraction needs, our technologies demonstrate better precision than LLMs.
F1 Score - PII Detection
Character-level performance on Electronic Health Records (N=376)
Source: Micro-average character-level PII detection in EHR
[ Composable APIs ]
(Chain, compose, solve)
Naturally providing structured outputs, our APIs can be chained to solve complex end-to-end information extraction tasks.
Pipeline Composition
Chain NER → Relation Extraction → Classification in a single request flow.
Structured I/O
Every model outputs clean JSON - ready to feed directly into the next stage.
Sub-Second Latency
Full multi-step pipelines complete faster than a single LLM call.
[ Private Deployment ]
(Your data never leaves your walls)
Our models run efficiently even on standard CPUs. Deploy on-premise or in your private cloud, with full control over your data and infrastructure.
CPU-Native Inference
Optimized for x86 and ARM processors. No expensive GPU clusters needed.
Air-Gapped Ready
Run fully offline in air-gapped environments. Zero data leaves your network.
Full Data Sovereignty
HIPAA, GDPR, and SOC 2 compatible. Your infrastructure, your rules.
[ Few-Shot Learning ]
(Less data, better results)
Our models can be fine-tuned with just a handful of examples to achieve results on par with, or exceeding, supervised models that require 10-100x more labeled data.
Fine-tuned
Knowledgator
SetFit
Real-world classification project with 100+ labels, comparing Knowledgator fine-tuned model to SetFit
[ Structured Output ]
(From raw text to knowledge, instantly)
Our models aggregate entities, relationships, and metadata from any source — and return clean, structured output ready for downstream systems.
Unstructured Input
Acme CorporationORG reported quarterly revenue of $4.2 billionMON for Q3 2024DATE, a 12% increase year-over-year. CEO Sarah MitchellPER announced the acquisition of DataStream Inc.ORG for $890 millionMON, in San Francisco, CALOC, expected to close by January 2025DATE. Net income reached $680 millionMON.
Structured Output
[ Knowledgator Platform ]
The all-in-one platform for optimizing AI models for edge deployment
Fine-tune open-source models, optimize them for any hardware, and deploy to your custom environments - all from a single platform.
- Domain-specific models;
- CPU-optimized models;
- End-to-end information extraction;
- Optimized for multiple hardware;
- Easy to deploy;
knowledgator-pipeline
Select Model
Choose from our library of task-specific models
Fine-Tune
Adapt models to your domain with minimal data
Optimize
Compress and accelerate for target hardware
Deploy
Ship to any environment with one click
[ Open-Source Models ]
(We develop our technologies in an open and collaborative way)
We have more than 5M downloads
[ Industries ]
(Reliable information extraction, adapted to industry-specific needs)
- /01Life Sciences & Healthcare
Unlock knowledge that saves lives
- /02Finance & Insurance
Make wise decisions in milliseconds
- /03Data Protection & Privacy
Protecting private information is more important than ever before
- /04Manufacturing & Engineering
Turn complex technical data into actionable intelligence
- /05Retail & E-Commerce
Transform product data into a competitive edge
[ Contact form ]
Let's build the future of open information together
Have questions, feedback, or collaboration ideas? Fill out the form - we'll get back to you soon.
[ Q&A ]
(Your questions. Our algorithms.)
Get started by signing up on our Platform. You'll receive free credits to explore the full pipeline, from model selection through fine-tuning to local deployment. Check our documentation for step-by-step integration guides.
Our models cover a wide range of information-extraction tasks, including named entity recognition, text classification, and structuring data into formats such as JSON. They are designed to be efficient, interpretable, and accurate, even in zero-shot settings, and can be fine-tuned to your specific domain.
Absolutely. Local deployment is at the core of what we do. All our models are open-source and optimized for on-premise, private cloud, and edge environments. You fine-tune them through our platform or using our open-source frameworks, then export and run them anywhere, your data never leaves your infrastructure.
You can find our pricing information here. We offer several plans to suit different needs, from individual developers to enterprise-scale deployments. For high-volume or specialized requirements, please contact us for a customized plan.
Yes. Our main models support many languages, including English, Portuguese, French, German, Spanish, Arabic, Chinese, Ukrainian, Russian, and others.
If you need help with deployment, fine-tuning, or any platform-related issue, you can reach our support team through the channels listed here. We're ready to assist you promptly through your preferred contact method.
You have two options: use our open-source fine-tuning frameworks directly in your own environment, or fine-tune through the Platform with a guided UI. Either way, upload your labelled data, run the training pipeline, and the resulting model is yours to deploy locally on any supported hardware. See our documentation for detailed walkthroughs.
Of course. We're always happy to learn about your goals and discuss how our models and platform can help. You can schedule a consultation with our team here.