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WRITER, styled in capitals by the company, is an enterprise software company that builds generative AI for large organizations. It was founded in 2020 by May Habib, its chief executive, and Waseem AlShikh, who had earlier co-founded Qordoba, a company that helped businesses localize products for new markets, according to TechCrunch. Its platform now centers on building AI agents, programs that use a language model to plan and carry out multi-step tasks with tools. Its pitch is a full stack: WRITER trains its own models, runs its own retrieval system and provides the application layer, so a company buys one integrated system rather than assembling parts from several vendors.

The model layer is the Palmyra family of large language models, launched in 2023. A large language model (LLM) is a neural network trained on vast amounts of text to predict the next token, a word or word fragment, and then tuned to follow instructions. In October 2024 WRITER released Palmyra X 004, which it said was trained almost entirely on synthetic data, text generated by other models rather than collected from people, for about $700,000 in compute. In April 2025 it released Palmyra X5, an adaptive reasoning model with a 1 million token context window, meaning it can take in hundreds of thousands of words in one prompt. WRITER said X5 was trained with synthetic data for about $1 million in GPUs, supports tool calling, image inputs and more than 30 languages, and scored 19.1% on OpenAI's MRCR 8-needle long-context test. WRITER's developer documentation now lists Palmyra X6, also with a 1 million token window, for agentic work, and schedules X4 and X5 for deprecation on December 14, 2026.

The retrieval layer is Knowledge Graph. Retrieval-augmented generation (RAG) is the technique of fetching relevant passages from a company's own documents and placing them in the model's prompt so answers are grounded in that material. Most RAG systems store text chunks as vectors, lists of numbers that represent meaning, and fetch the chunks whose vectors are most similar to the question. WRITER describes Knowledge Graph as graph-based RAG, which it claims is more accurate than vector-based retrieval. In a graph approach, entities such as products, people and policies and the relationships between them are stored as nodes and edges, so retrieval can follow connections rather than relying only on similarity. The documentation shows the workflow: create a graph with a name and a description of its purpose, upload files or add website URLs, scope it to particular teams, and attach it to an agent or query it through the API.

The application layer is AI HQ, launched on April 10, 2025. It combines a low-code, drag-and-drop Agent Builder for IT and business teams, observability tools that monitor agent activity in real time and flag anomalies, and a library of more than 100 prebuilt agents for finance, healthcare, retail and other functions. WRITER says the platform is powered by Palmyra models and graph-based RAG, and that agents can be grounded through Knowledge Graph and document uploads. Customers named in WRITER and AWS materials include Accenture, Intuit, Mars, Marriott, Prudential, Qualcomm, Salesforce, Uber and Vanguard.

The company's funding reflects investor interest in this approach. In November 2024 it raised a $200 million Series C at a $1.9 billion valuation, co-led by Premji Invest, Radical Ventures and ICONIQ Growth, bringing total funding to $326 million, according to TechCrunch. Participants included the venture arms of Salesforce, Adobe, IBM, Workday and Citi, as well as Accenture. TechCrunch reported that WRITER said it serves hundreds of clients but did not disclose revenue.

Compared with the alternatives, WRITER sits between two common enterprise choices. One is buying a general assistant from a frontier lab, such as an enterprise chatbot plan, and connecting it to company data. The other is building in-house on a cloud platform with open or proprietary models plus a separate vector database and orchestration framework. WRITER's argument is that owning the model, retrieval and agent layers together gives better accuracy and control, as in its claim that graph-based retrieval beats vector retrieval. Those are vendor claims. Independent comparisons of graph-based and vector-based RAG depend heavily on the data and questions, so buyers should test on their own documents.

Limitations follow from the category. Like every LLM, Palmyra models can produce fluent but wrong answers, and RAG reduces but does not eliminate this when retrieval misses the right passage or the source documents are themselves out of date. A 19.1% score on an 8-needle long-context test shows that a 1 million token window does not mean reliable recall of every detail in it. Agents that take actions in business systems add risks of wrong or unintended actions, which is why observability and permission scoping matter. Model churn is real too: several specialized Palmyra models, including Palmyra Med and Palmyra Fin, were deprecated on July 13, 2026, so integrations must plan for migrations.

In practice, adopting WRITER means an enterprise contract for the platform, or API access billed per token. The developer documentation lists Palmyra X6 at $2 per million input tokens and $8 per million output tokens, and X5 at $0.60 and $6. WRITER's security page states that it does not train its models on customer data, inputs or outputs, describes a zero data retention approach with configurable deletion schedules, offers dedicated private cloud deployment, and lists SOC 2 Type II and ISO/IEC 27001, 27701 and 42001 certifications. Data governance teams should still review current terms, since available controls depend on contract and configuration.