Enterprise AI Agents
We help businesses become AI-enabled: securely, measurably and at scale. It starts with agents that answer from your own internal data and act with human approval: permissioned down to role, audit-ready, and built to pass a security review before they answer a single question.
CFO Agent
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“The hard part is not the pilot.
It is production you can trust.”
Most enterprise AI stalls between a promising demo and a system leadership will stand behind, because a chat window is not an agent. An enterprise AI agent is an AI assistant that works inside your own systems: it answers from your internal documents at each person’s permission level, acts only with human approval, and leaves an audit record for every step. That is exactly what XNOR builds: permissions resolved in code you can audit, fail-closed controls, and a verdict from every reader on every answer. What the model is never shown, it cannot reveal. What your people rate, you can improve.
The AI agents
Each agent pairs one business function with the same security architecture. The pattern extends to Operations, Sales, Delivery, Legal and Compliance, or any function that runs on documents.
A sourced answer to a question, rendered at the asker’s access level, citing the document behind every figure. Read-only, and the default mode for everyone.
Run the steps of a process you have already documented, partly or end to end, on request or on a schedule. Restricted to the executive and manager roles.
Finance assistant for the leadership team
Sourced answers on revenue, receivables, budgets and finance procedures, plus the recurring finance workflows around them.
HR assistant for the employee lifecycle
Policy answers scoped to each person’s authority, and the lifecycle processes behind them automated end to end.
Company knowledge for every employee
Strategy, policies, OKRs and guides for the whole company, answered only from documents leadership has approved.
Security first
If any layer fails, the system refuses to answer rather than risk a leak.
The agent’s credentials can only reach the libraries it is meant to read. Out-of-scope access is denied by the platform itself, verified with negative tests.
A deterministic allowlist decides what enters the context, per tier, per request. What the model cannot see, it cannot reveal.
Retrieved documents are wrapped as untrusted data with structural markers neutralized, so a planted document cannot escape into system authority.
A deterministic screen plus an independent model verdict. Only an explicit pass ships. In doubt, it blocks.
Permissions cannot be resolved? Refuse. Post-check unreadable? Block. Unknown chat type? Treated as the most public audience. Every default protects your data.
Your ecosystem
The channel, document and model layers are each isolated behind a single interface, so the platform adapts to your stack instead of replacing it.
The agents live inside the conversation, not in another tab.
Read in place, within permissions. No copy of your data is taken anywhere.
Model per role is a configuration variable, never a code change.
Architecture
Every question runs the same route: identity first, then a scoped search, then two more models before anything is allowed out. Pick a scenario and watch it travel the map.
What was our revenue last month?
The question arrives in a 1:1 chat and is acknowledged in 0.4s.
Swipe the map sideways to follow the whole route.
Value for your business
Leaders and their teams get a sourced answer in about 30 seconds, 24/7, inside Teams. No new application to adopt, no waiting for a report to be compiled.
Serverless throughout, so there is no cluster sitting idle and nothing to patch. The only line that scales with use is model requests, and the admin console shows that spend per question, per person and per model with a month-end forecast. No servers to run, no dedicated ops team.
Staff get an official AI channel over internal data instead of pasting it into outside tools. Permissions, audit and compliance are designed in from the start.
A shared core architecture: the next department’s agent takes days, not a rebuild. Upgrading the model does not mean changing the system.
Operations included
The part most AI agent projects skip, and the reason they die before they can prove their value.
The XNOR Agents Admin console, shown with demo data.
Working with us
AI is designed into the engagement from day one, not bolted on afterward. The permission model, the audit trail and the security evaluation are part of the build, not a phase you add later.
2-3 weeks
4-6 weeks
2-4 weeks per agent
Our clients
An AI assistant that operates inside your company’s own systems instead of a public chat window. It answers questions from your internal documents at each person’s permission level, runs processes you have documented with human approval on every outbound step, and writes an audit row for everything it does.
Three ways. The agents answer only from documents your company approved, so there is no general-web guessing. Permissions are resolved in code before any model call, so what someone must not see never reaches the model at all. And the whole stack runs serverless in your own cloud account, with an admin console for cost, quotas and audit.
No. The agents read documents in place from your own libraries, within permissions you grant, and take no copy of your data anywhere. In a typical engagement the entire stack, including the models via Amazon Bedrock, runs inside your own cloud accounts.
Microsoft Teams and SharePoint / Microsoft 365 are supported in production today. Slack, Google Chat, Zalo, Google Drive, Confluence and other stores are adapted per engagement: the channel and document layers sit behind single interfaces designed for exactly that swap.
Typically 6 to 9 weeks end to end: 2 to 3 weeks for AI readiness and strategy, then 4 to 6 weeks to build the first agent on your infrastructure, pass the adversarial security evaluation and run the pilot group. Each following agent takes 2 to 4 weeks on the shared core.
Next step
Book a 30-minute demo: we show you the agents running in production, and sketch out the first agent for your business with you.