Twigg: The AI workspace build for long horizon projects.
Most enterprise AI use today is informal, fragmented, and shallow. People open a chat, get an output, copy it somewhere else, and start again tomorrow, with no memory of what came before. For short tasks, that is fine. For complex, high-stakes work that runs over weeks, it breaks down fast. Twigg is built around addressing the the common problems with long horizon LLM use.
At the core of Twigg is a tree-based context management system. Every prompt and response is a node you can branch from, navigate back to, or build on. This means the AI always receives precise, relevant context rather than an unwieldy scroll of everything that came before. For the user, it is an organised project. For the AI, it is a clean, navigable structure. Both matter for work that runs over weeks, not hours.
LLMs cannot read your documents properly. Most AI tools mangle complex formatting, lose table structures, and misread long PDFs. Every document brought into the workspace is parsed and normalized into a clean, structured format. This allows the AI to reason over large documents the same way a human would, surfacing relevant passages and leaving irrelevant information out.
Once information is analysed and a plan created, Twigg can act on suggestions. Our Workspace allows for the creation of documents and editing of drafts natively by the LLM. You and the AI work on the same material together. The copy-paste step between “AI output” and “actual work” disappears.
With these capabilities, do however come risks. Twigg sits deliberately in between autonomous and manual extremes. It affords the AI only gated access to actions that you can control depending on preferences. This keeps you in the loop on complex tasks, whilst allowing AI to to the heavy lifting on menial tasks.
Unfortunately, hallucinations come hand in hand with LLM use. Given this fundamental limitation, Twigg hosts a propriety traceability system. Every fact, statistic, or claim the AI surfaces from your documents are linked back to the exact source passage with a single click. Line-level provenance means any output produced in Twigg can be verified and defended in seconds. No more hunting through PDFs. No more wondering if something was hallucinated.
Your data should not be training someone else’s model. Documents and chats encrypted at rest with unique per-artifact keys. Twigg holds enterprise-tier agreements with OpenAI, Anthropic, and Google, carrying contractual no-training guarantees. And because no single model is best at everything, Twigg lets you switch models mid-project based on what the task actually demands.
Long horizon work deserves a tool built for it. Twigg gives enterprise teams the structure, transparency, and control that should come as standard with enterprise solutions.
