The blocker is rarely model capability — it is the missing connection to enterprise systems, and the missing trust.
It Is Not Connected to Your Data
A general chat tool does not know your contracts, your inbox, or your internal documents. AI that is not wired into enterprise data returns generic answers.
It Cannot Explain Itself
If no one can see why an output was produced, no team will build a process on top of it. Without auditability, usage never leaves the pilot stage.
The Permission Boundary Is Unclear
If it is not settled up front what a system reading your inbox can read, can send, and how long it retains data, you are carrying legal and reputational risk.
Every item below corresponds to a system we have actually built.
An inbox or document flow is classified automatically: urgent, opportunity, and noise are separated, and a first-pass reply is drafted for each. The decision stays with a human.
We bring knowledge scattered across systems behind a single question interface. Semantic search and graph traversal work together, and the system shows which method it chose and why.
We build agents that connect to what you already run — Microsoft 365, Pipedrive CRM, Zapier. You do not have to migrate to a new platform.
Read-only permissions, no unnecessary retention, and a record of every step. What the system cannot do is defined as clearly as what it can.
Both projects below are proof-of-concept work, not client deliveries. Both are live and open for inspection.
Reads a Microsoft 365 inbox, classifies every email, summarises it, and drafts a first-pass reply.
Recent emails are pulled through the Microsoft Graph API, and each one gets a single JSON-constrained Claude call. A failure on one email never affects the rest. The app never requests the "Mail.Send" permission — it cannot send mail on your behalf — and it persists no data.
Runs the same question through semantic search and graph traversal side by side, and shows why the auto-router picked one over the other.
The decision log exposes the signals used, the number of entities found, and a confidence score. The sample dataset holds 201 entities and 175 relationships across 8 documents, with 20 ready-made questions you can run in one click.
We are not an agency that picked up AI along the way — this is how we build our own product and internal systems.
In a free 30-minute call we look at your current process and find the point where AI would genuinely make a difference. If it would not, we will tell you that too.