An AI assistant that answers from your real business records

An assistant that can see the work already recorded in our business

Services: AI ERP & CRM ; AI Agents ; AI Workflow Automation
Delivery: AIOVIX's own operating workspace and AI interface.

We already had leads, follow-ups, projects, calls, invoices, and daily tasks in the application. An assistant that answered without those records could only offer general advice.

There was also a boundary to preserve: personal accounts and assets belong in Finance, but they must not be confused with sales revenue or company accounting.

Business OS combines sales records, project tracking, daily planning, call history, invoices, and a separate personal-finance area.

Antheon loads selected context before generating a response. Its backend gathers:

• Lead totals and pipeline stages.
• Pending follow-ups and personal tasks.
• Invoice totals grouped by status and currency.
• Recent lead activity and call summaries.
• The user's account and asset balances.

The interface supports chat and voice. The underlying workspace remains available so the user can inspect the records behind the answer.

The context service uses eight predefined database queries rather than letting the model issue arbitrary SQL.

Upcoming work is bounded to a seven-day window. Recent lead and task lists are limited to twelve records; recent calls are limited to eight. Workspace data is queried by workspace ID, while personal tasks and accounts use the user ID.

Those limits control the amount of context sent to the model. They also mean the answer should not be described as an exhaustive audit of every record.

Accounts and assets have separate views, names, colors, balances, and currency settings. The requested experience supports selecting which records contribute to the total and viewing the combined balance in a chosen currency.

This is personal balance tracking within the operating workspace. It does not turn those balances into CRM revenue.

The assistant now receives actual workspace context for the response workflow. The user no longer has to paste the lead summary, upcoming tasks, and recent calls into every conversation.

That is a delivered data connection, not a measured productivity percentage. Antheon and Business OS are one proof: the assistant and the application supplying its context.

Next.js/React; Node.js, Express; PostgreSQL, TypeORM; parameterized SQL context service; OpenAI; Cartesia speech integration; Google APIs, Stripe, email and object-storage services.

Hypothetical planning example, not a measured project result or a performance forecast. All volumes and timings below are assumed.

Gather selected leads, tasks, invoices, and recent calls, then check the assistant's brief.

• Workload: 22 daily workspace reviews per month.
• Manual handling: 15 minutes per item.
• Assisted handling, including human review: 7 minutes per item.
• Additional exception handling: 22 minutes in the same period.
• Modeled difference: 22 x (15 - 7) - 22 = 154 minutes (2 hours 34 minutes) per month.

The example reserves another 22 minutes per month for source-record checks. The bounded context queries do not cover every business record. This is potential staff capacity, not cash savings; setup, training, and software costs are excluded.

How to validate: Time comparable daily reviews, including opening source records and correcting answers. Record omissions caused by the query limits; do not count the same preparation time again under another MCP workflow.