Expertise

Explore the questions behind our nine areas of focus.

Foundations

AI models

Capabilities, architecture, and technical differentiation.

Questions we explore

What capabilities change the user’s workflow? How do quality, data requirements, and inference cost interact? What remains differentiated as baseline models improve?

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Compute & inference

Infrastructure for training, deploying, and serving models.

Questions we explore

How does the system behave under real load? What drives cost and latency? How much operational work is required to keep it reliable?

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Data infrastructure

Databases, storage, pipelines, retrieval, and business context.

Questions we explore

How is data kept fresh and useful? What consistency and access patterns are required? Can the architecture fit the buyer’s data estate?

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Building with AI

Developer tooling

Tools for building, changing, and operating software.

Questions we explore

Does the product remove a recurring engineering bottleneck? Does it preserve context and control? What is the cost of integrating it into an existing workflow?

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Agent platforms

Context, memory, tools, and orchestration for agentic work.

Questions we explore

What can the agent actually complete? How are permissions and exceptions handled? How is context maintained across actions and over time?

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Evaluation & experimentation

Methods for measuring behavior, reliability, and product impact.

Questions we explore

Are tests representative of real tasks? Can teams detect regressions? Does a quality improvement translate into a better user or business outcome?

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Enterprise readiness

Security & governance

Identity, access, policy, and risk management.

Questions we explore

What actions and data need protection? Are controls visible and enforceable? Can the organization adopt AI without losing accountability?

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Observability & reliability

Understanding and operating AI systems in production.

Questions we explore

Can teams see why a system failed? Can they trace changes in models, data, and tools? Does the product support an effective response?

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Applied AI

Applications that change how a specific job gets done.

Questions we explore

Is the problem important to the people doing the work? Does the product fit the surrounding process? Can value be demonstrated in actual use?

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