Context should compound,
not fragment.
GammaLex conducts research on frontier model context boundaries while engineering specialized models for state reconciliation. We build Tauss, the context engine providing a continuously reconciled state layer across systems and over time. Grounded in distributed systems research, Tauss gives autonomous agents a verified foundation to build on instead of starting over.
Models reason. Context breaks.
Frontier models advance every month, but the records they reason over have not kept pace. Institutional knowledge sits fragmented across isolated systems, outdated, conflicting, and resetting to zero with every new prompt.
A stronger model cannot fix bad context. Tauss reconciles facts and state before execution, turning ephemeral prompts into compounding institutional understanding. How Tauss works.
Intelligence that compounds
Understanding outlives models. What is established carries forward across requests, allowing agents to recognize what is established, what changed, and what remains unknown.
Trained on reconciled truth. The domain models we build learn from context Tauss has already reconciled, not raw, unverified records.
Model independent by design. Tauss works with any system, whether running our domain-specific models or third-party frontier APIs.
Tested where mistakes carry real cost
We evaluate context integrity across fundamentally different domains to prove Tauss solves universal failure modes, whether human life, scientific validity, or public trust is on the line. Each asks the same question: can an intelligent system tell whether its understanding is complete, current, and traceable to its sources?
What guides our work
Context is part of intelligence. The same question has a different answer when the facts change. We build Tauss so models reason from what an organization knows today, with the historical context needed to understand how it got there.
Reliability comes first. Systems that organizations rely on must be resilient, secure, and straightforward to operate. We build for longevity through shifting models, evolving data sources, and changing teams.
Uncertainty must remain visible. Records conflict, details go stale, and critical ones are missing. We build so teams can spot those gaps instantly and trace every answer back to its source.
AI should work with people
Make room for human judgment. People bring expertise that rarely fits in a prompt. AI should make it easy to add a missing detail, correct an assumption, or question how a conclusion was reached.
Adapt to how people work. Physicians, scientists, and public servants work with different records, standards, and responsibilities. We build for that variety so people can bring AI into their work on their own terms.
Research should travel
Share what others can build on. We believe research gets better when people can examine the methods, challenge the findings, and try them in their own domains. We want to share what we learn, including where our approaches fall short.
We publish our findings as research notes and release tools openly, starting with OpenContextually, which helps coding agents isolate situational context before taking action.
Build with us
We partner with researchers, engineers, and institutions solving complex context problems. Tauss is available to select partner organizations.