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Fiverr Pro
English
$
USD
I will transform your data to be connected and ai queryable
Uzynice
About this gig
Our Knowledge Graph Studio stands out by bridging the gap between structured/unstructured enterprise data, high-performance search, and secure, non-hallucinating AI for various BI use cases. Unlike standard search tools or basic chatbots, our solution fuses Apache Jena Fuseki RDF triplestores, Neo4j property graphs, and Lucene-based search clusters like Elasticsearch and Solr into a single unified cockpit. This architecture lets us index data at a granular, attribute-level scale, giving administrators full visibility and lightning-fast search with exact database schema mapping.
Crucially, our context-grounded AI agent eliminates hallucinations. By anchoring every conversational answer strictly to verified graph triplets and indexed document properties, we guarantee complete factual traceability. This means teams can query, visualize, and chat with complex enterprise datasets using natural language, all while maintaining rigorous role-based access control and live telemetry. Backed by our rigorous methodology, we deliver an elegant, production-ready system optimized for containerized cloud deployment.
Get to know Uzynice
Uzynice
Linked Data and Backend Engineer
- FromGermany
- Member sinceFeb 2020
- Avg. response time2 hours
Languages
English
I'm Uzoma, a backend developer and linked data engineer skilled in Python, Java, C#, SQL, RDF, OWL, and SPARQL. I build clean, scalable systems and automate semantic pipelines for knowledge graphs, linked data integration, and intelligent systems.
Whether digitizing legacy data or designing ontologies, I deliver smart, structured solutions that turn complex data into actionable insight using RDF, OWL, SHACL, and SPARQL. My work bridges backend engineering with semantic clarity—ensuring data is not just stored, but understood.
Let’s build something meaningful and turn your data into insight.
FAQ
Does our data ever leave our servers?
No. The whole platform is self-hosted and runs on your infrastructure. Even the AI layer can run fully offline via Ollama, so you're not required to send anything to a third-party LLM API if you don't want to.
What happens if we lose internet access — does it stop working?
It keeps working. License verification is done offline: your key is signed (Ed25519) and checked locally against your hardware ID — there's no live phone-home to a license server. Paired with a local LLM, the whole system can run fully air-gapped.
Are we locked into one database vendor?
No—you choose Neo4j (property graph/Cipher) or Apache Jena Fuseki (RDF/SPARQL) at deployment time; it's the same product either way.
How many people and servers does our license actually cover?
Each tier caps both users and instances (e.g., Basic = 5 users / 2 instances), and the backend enforces that cap at registration time—not a soft UI limit someone could bypass.
If we don't fit a standard tier, can pricing flex to what we actually need?
Yes — custom licenses with an arbitrary user/instance count are a first-class, explicitly supported path in how licenses get issued, not a workaround.
How do we know the AI chatbot isn't just making things up about our data?
Every answer is grounded in a real, inspectable query — the agent generates (and self-heals) its own Cypher/SPARQL against your actual graph rather than free-associating from an LLM's memory.
What kinds of source data can we actually bring in?
Excel/CSV, PDF, DOCX, free text, JSON/XML, and live SQL sources — all extracted into the knowledge graph through a managed ingestion queue, so concurrent uploads or scheduled imports don't silently get dropped.
Can we restrict what different employees are allowed to see or do?
Yes — role-based access (Admin / Power User / Basic User), enforced on the backend API itself, not just hidden in the UI.
If someone updates the underlying data, will our dashboards show stale numbers?
No, and this is enforced, not just hoped for — every ingestion run stamps a freshness marker, and any dashboard widget computed before the most recent ingestion gets flagged for refresh rather than silently showing outdated numbers.
Are we forced into one AI provider?
No — swappable per deployment between a fully offline local model (Ollama) and Gemini, OpenAI, Anthropic, Azure OpenAI, or Groq, with no code changes needed to switch.
