Turning internal data into subject-matter-expert AI agents
We build custom AI that first analyzes your organization's real-world material - messy documents, customer call transcripts, etc. - and distills it into insights and a deep understanding of your business.
From there, we create agents whose answers, analysis, and outputs stay grounded in evidence you can inspect.
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Core Use Cases
Legal, Policy & Compliance
Supporting high-stakes interpretation where correctness, authority, and traceability matter.
- Contract and clause review against internal standards
- Audit preparation and evidence-backed reporting
- Policy and regulatory research across large and evolving bodies of material
Sales & Go-to-Market
Turning high-volume commercial signals into consistent insight and assets.
- Analyzing sales call transcripts to surface themes, risks, and objections
- Generating account briefs, handovers, and enablement assets
- Producing post-call coaching grounded in what was actually said
Operations Insights & Delivery
Interpreting progress and keeping work aligned with organizational priorities.
- Interpreting updates and metrics into decision-ready summaries
- Explaining what changed, why it changed, and where attention is needed
- Producing reports aligned to stated objectives
Product Decision-Making
Supporting product sense by applying a deep understanding of the company, customers, and market.
- Extracting insight from research, customer calls, and internal material
- Critiquing roadmaps against customer evidence and stated priorities
- Challenging assumptions using historical context and market signals
Your questions,
answered
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.
Enterprise 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.
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.
No. They are designed to support expert work, not replace it. The agents handle synthesis, retrieval, and consistent application of standards so people can focus on judgment, decision-making, and oversight where it matters most.
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Ready to explore how AI agents can support your team's expertise? Let's start a conversation.