An innovative approach to knowledge retrieval
Knowledge Graph, our graph-based retrieval-augmented generation (RAG), achieves higher accuracy than traditional RAG approaches that use vector retrieval.
Richer semantic understanding
Knowledge Graph draws on a specialized LLM that’s trained to process data at scale and build valuable semantic relationships between data points. It stores data in a cost-effective, easy-to-update graph structure.
The alternative approach
By converting data into vector embeddings, traditional RAG can only define similarity by distance between data points but has no context on their semantic relationships. Vector databases are also difficult and costly to maintain and update.
Accurate retrieval methodology
Because graph structures retain semantic relationships, Knowledge Graph accurately retrieves relevant data for each query. Our retrieval-aware compression technique condenses data and indexes it with metadata, which gives it rich context.
The alternative approach
Traditional RAG converts the query into a vector embedding and uses a rough algorithm to find the closest data points to the query, without any understanding of the relationship between the data points. When data is dense, this method fails to return the most relevant data consistently.
State-of-the-art LLMs
To generate a response, Knowledge Graph sends relevant data to our Palmyra LLMs, which are top-ranked and trained with 1 trillion tokens of quality data. We apply advanced techniques to enhance performance and minimize hallucinations.
The alternative approach
The quality of the answer depends on the quality of the retrieval, and the level of hallucination depends on the quality of the underlying LLM and the techniques you employ.
Build digital assistants you can trust
Knowledge Graph anchors your generative AI solutions in your company knowledge. Create expert assistants for any use case and be confident that your people are getting the correct information.
“With Knowledge Graph, we’ve built digital assistants that enable salespeople in real time, giving them accurate, on-brand insights on objection handling, competitive differentiation, personas, and more.”
Anna Griffin
Chief Market Officer
“Writer is onto something amazing. Their full-stack platform, deep customer-centricity, and high-touch approach to services truly sets them apart. Post our due diligence, partnering with Writer was an easy decision for us.”
Ajay Dhaul
SVP of Data & Applied AI
“Writer enables my technology team to provide high quality generative AI applications, without compromising on our security needs or responsible AI guidelines. From digital assistants to editorial content, we’ve been able to build powerful, secure applications on the Writer full-stack platform.”
Nitin Tandon
CIO of Vanguard
“We simply do too much with content, with our own software, and with data to have an isolated tool. Things really became unblocked when we partnered with Writer.”
Simeon Simeonov
CTO
Designed to scale with enterprise data
Processes dense data
Whereas traditional RAG breaks down when data is highly concentrated, Knowledge Graph excels at retrieval with dense data.
Updates data efficiently
Unlike traditional RAG, adding or updating data to Knowledge Graph is easy, fast, and inexpensive.
Supports your file types
Knowledge Graph handles structured and unstructured data, including spreadsheets, docs, charts, presentations, PDFs, and more.
Deploy with confidence with integrated RAG
Rather than stitch together your own RAG, Writer Knowledge Graph makes it easy to build high-quality applications.
Maintain
efficient costs
when you use Writer instead of traditional RAG, with savings increasing as you scale.
Increase
your speed
Integrate data sources quickly. Our full-stack platform of LLMs, Knowledge Graph, AI guardrails, and a flexible application layer makes it easy to deploy in days.
Stay secure
and compliant
Secure your data and comply with privacy regulations. Our security-first architecture gives you control and our platform doesn’t persist or use your data.