Thesis

Building a domain-specific AI system involves two things: defining how it should behave on the task, and optimizing it to actually behave that way.

Foundation models are trivializing the second. Each frontier model release solves another tier of general intelligence failure modes (hallucination, forgetfulness, tool misuse, etc.) that AI teams had to engineer around. Many AI app companies whose competitive advantage was execution have already had their lunch eaten, not as a hostile move from foundation labs, but as the natural progression of the technology.

What will remain is task specification: the body of ground truth about how a task should be done that no foundation model can ever contain. Tasks grounded in IP, internal processes, and expertise no foundation model has ever seen are permanently out of distribution. The value of a domain-specific AI system reduces to how thoroughly it captures this and how well it can provide that to an agent at inference. Our work is the tooling and infrastructure for capturing, codifying, and operating on this knowledge. This product will not be part of a foundation model, an observability solution, or a storage provider. It exists as a new stack component that redefines human-agent collaboration, pushing the frontier of agents learning how to work in the real world.

Today, specifications are complex and painful to interact with

Specifications and instructions for the agents have grown increasingly complex. As the context grows in token count, instructions, edge case requirements, and few-shot examples, confusion is created for both the agent and the builders. Gaps in specification, contradictory and inconsistent instructions, and rotted context are just a few of the tangible consequences of poor inference-time retrieval, context management, and auditability.

As the scale and complexity of specification and implementation increases, manual read/write actions become intractable for humans. The only way to understand future AI systems will be in terms of what they do for a given task. The only way to shape their behavior will be by teaching them by providing human insight, knowing that they can recursively self improve with the right feedback. Interacting with these large/complex systems effectively will look more like collaborating with a teammate via human-like communication rather than tweaking rerankers, prompt engineering, or other low-level actions.

Specifications and retrieval have to be first-class

The valuable tasks, with high complexity, near-innumerable edge cases, long-tailed input distributions, and specifications that could never exist inside foundation model knowledge or a static prompt, require a new paradigm. A spec needs to be durable, queryable by every role that participates in defining it, and consumable by the system at inference time. It needs to grow with the team's understanding without regressing unrelated behavior. At inference time, the right context from the spec needs to be precisely pulled into the context window. Vanilla retrieval methods won't keep up with growing complexity and coverage of what agents are responsible for. New infrastructure and tooling is necessary.

The flywheel: your system learns by doing

Our infrastructure for domain knowledge isn't just a knowledge base and a retrieval service, it's a closed loop system that learns by doing. By deeply integrating with your team, it becomes an autonomous collaborator you can trust. And the work compounds:

• Triage becomes structured: Domain-specific failures map cleanly to items in the specification, fixes become auditable and verifiable, and everyone knows who owns it.
• Expert corrections accumulate proprietary data: When an SME edits the spec, the edit isn't just a prompt change, it also produces tons of newly corrected agentic trace data that demonstrates how the task should be done. Over time, that produces the high quality, domain-grounded data that no foundation model has access to and no competitor can replicate.
• Continuous improvement unlocks compounding cost and capability gains: You stop depending on frontier inference to stay ahead because you've put in the work to gather the data that defines your success, allowing you to easily decide what you actually need general frontier capabilities for and if you want to use other models or even own the weights.
• A competitor starting six months later can't catch up: By having a solid context foundation through months of improvements, all built on top of an environment and interface you've perfected for your customers, no one stands a chance in joining the race.

Data and infrastructure as defensibility

The teams pulling ahead on hard AI tasks don't just have the best prompt, model, or harness today. They're the ones whose systems get measurably better every week, generating data no competitor has access to. The question worth asking: how much of that data is your current setup actually capturing? Is it in a format you can leverage? Are you able to operationalize it? At what rate can you compound its value?