Most companies hold back on what they delegate to AI. They don't trust it to work reliably - and they're right not to.
We change that - building the deep structured context your AI needs to understand your business, and automated systems to prove it's working reliably.
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Structure internal documents and other knowledge into context AI can reason over
Create agents on that foundation, or enhance the tools you already use
Know whether your AI - built or bought - can really be trusted
You can't just feed company data into an AI tool and expect it to understand your business. It won't know your terminology. It will mix opinion with fact, and stale information with current. It will give you persuasive answers that sound right but miss the mark.
If you want AI you can trust with real work, two ingredients are key: structured, nuanced context about your company, and a reliable way to track whether it's working. Neither happens by accident.
Supporting high-stakes interpretation where correctness, authority, and traceability matter.
Turning high-volume commercial signals into consistent insight and assets.
Interpreting progress and keeping work aligned with organizational priorities.
Supporting product sense by applying a deep understanding of the company, customers, and market.
ChatGPT is a general-purpose assistant trained on broad public data. These agents are built on your organization's internal material and shaped around how you work. They use your documents, transcripts, and policies as their source of truth, and they show where answers come from so they can be checked.
Internal search platforms focus on helping people find and summarize information across systems. These agents focus on interpretation and application. They learn how your organization reasons, applies standards, and weighs evidence, then use that context to produce analysis, answers, and assets that go beyond search or summarization alone.
Much of what makes organisations work isn't written down - it's tacit expertise held by key people. We use structured interviews, voice agents, and targeted capture sessions to surface this knowledge and integrate it into your context layer. This is especially valuable where gaps are identified in existing documentation.
Trust is designed into the system. We identify trusted sources and ground truth within your organization, then map messier material back to those standards. Outputs surface their sources, highlight uncertainty or assumptions, and make it clear how conclusions were reached so users can inspect and validate them.
The system is designed to recognize when information is incomplete, ambiguous, or based on assumptions. Instead of filling gaps with confident guesses, it highlights uncertainty, shows partial evidence, or flags areas that may require human review.
Not all internal material is treated equally. The system takes into account context such as recency, source type, and relevance, and can surface conflicting information rather than silently merging it. Where appropriate, it can prompt subject-matter experts to review or resolve ambiguity.
Yes. They are designed specifically for real-world internal material, including drafts, emails, transcripts, scanned documents, slide decks, and mixed-quality sources. Messiness is expected and handled explicitly.
Each system is built specifically for your organization, and security requirements are designed in from the start. We work with you to understand your data sensitivity, governance needs, and risk profile, then design an architecture that fits - from secure cloud deployments with strict access controls to private or local model deployments where data must not leave your environment. Internal material is never used to train public models.
Yes. Evaluation isn't limited to systems we build. If you've invested in off-the-shelf AI tools, we can assess how reliably they perform against your internal standards and use cases. This helps you understand where those tools can be trusted, where they fall short, and whether the gaps are worth addressing.
No. They're designed to support expert work, not replace it. The agents handle the heavy lifting - synthesis, retrieval, and consistent application of standards - so your people can focus on judgment, decision-making, and the high-stakes interpretation where human expertise matters most.
Ready to explore how a structured context layer can transform your AI capabilities? Let's start a conversation.