Enterprise AI Chatbots Connected to Your Systems, Files, and Databases

A website chatbot answers visitor questions from public content. An enterprise AI chatbot is used by your own staff and connects to private systems — ERP, CRM, SharePoint, file shares, and SQL databases — so employees can ask questions about the business in plain English. It knows who is asking, enforces what they are allowed to see, cites its sources, and can run live, read-only queries to answer questions about numbers, not just documents.

Most deployments combine three kinds of sources: databases (such as SQL Server, PostgreSQL, Oracle, MySQL, or Snowflake), documents (SharePoint, OneDrive, file shares, PDFs, and scanned files), and business applications with APIs (such as Dynamics 365, NetSuite, Salesforce, HubSpot, ServiceNow, or ConnectWise). If a system has a database, an API, or an export we can schedule, we can usually connect to it. We confirm feasibility for your specific versions during discovery.

For data questions, the assistant translates the request into SQL against governed, read-only views that we design with your data owners. The query runs under a service account with no write permissions, results are filtered to what the user is allowed to see, and the response includes the figures, a table or chart when useful, and the query that produced them so the answer can be verified.

No — that is the core design requirement. The assistant authenticates each user through single sign-on and applies their existing permissions at retrieval time: SharePoint access lists, database roles, and row-level rules. Restricted records are filtered out before the language model ever sees them, and every question, source, and query is logged for audit.

No. We build on enterprise model services and private deployments where prompts and outputs are not used for model training. Your data stays in its source systems; the assistant retrieves only what is needed to answer each question. For the most sensitive environments we can run open-weight models entirely inside your network.

Every answer is grounded in retrieved documents or query results and includes citations. The assistant checks its draft against the evidence and is allowed to say it does not know. Before launch, we build a golden question set with your team and use it as an acceptance test, then re-run it whenever data sources, prompts, or models change.

Those tools are strong general-purpose assistants, and we help clients deploy and govern them. A custom assistant goes where they typically do not: live queries against line-of-business databases and ERP systems, your specific business rules and terminology, custom approval workflows, and deployment inside your tenant, our private cloud, or on-premises. Many clients use both — Copilot for everyday productivity and a custom assistant for operational data.

You choose: inside your Microsoft Azure or AWS tenant, on ITECS-managed private cloud infrastructure, or on-premises. The platform is model-agnostic — we select from Anthropic Claude, OpenAI GPT, Google Gemini, and open-weight models based on accuracy, cost, and data-residency requirements, and we can change models later without rebuilding the connectors.

A first production use case typically takes 4–8 weeks: discovery, data and access mapping, a pilot with one team, and production rollout. We need an executive sponsor, an owner for each data source, 20–50 real questions your staff ask with the correct answers, and access to a test or reporting copy of the systems involved.

Yes, when you want it to. Agentic capabilities — creating tickets, drafting quotes or emails, updating CRM records, starting approval workflows — are added deliberately, one action at a time, with human approval steps and a full audit trail. Most clients start read-only and add actions once the team trusts the answers.

Yes. The same platform powers public assistants grounded in your website and documentation, and authenticated portal assistants that can answer account-specific questions such as order, case, or invoice status. The assistant on this website is a public example you can try in the lower-right corner of any page.

Assistants need care like any production system. Through our managed intelligence service, ITECS monitors answer quality, usage, and model costs; adds new sources and use cases; re-tests on every model upgrade; and provides reporting to your leadership team — delivered by our Dallas-based engineers.