Bi-Mind

Problem

Enterprise AI pilots often stay stuck as isolated chat tools: they cannot reach enterprise systems, their actions cannot be controlled, and the systems they touch are not isolated per tenant.

Architecture

Bi-Mind is a multi-tenant enterprise AI agent platform: a secure chat layer, specialist assistants for specific domains, and controlled tool execution with tenant access controls, so tool calls run with the signed-in user's permissions.

• ERP reporting & operations copilot: Reviews ERP sales, stock and operations data and produces operational summaries and reports.
• Database & infrastructure performance monitoring: Monitors database and infrastructure performance signals and highlights items that need attention.
• Network operations automation: Translates network operation requests into device configuration changes with policy checks.
• Log & incident analysis: Correlates logs across services to help identify root causes and build incident timelines.
• Knowledge-base Q&A assistants: Answers questions from an internal knowledge base using retrieval over indexed documents.
• Document & form intelligence: Extracts and summarizes readable fields from uploaded documents and flags unclear fields.
• Automation opportunity assessment: Assesses a described business process and recommends a suitable automation approach.
• Data-access governance: Manages data-access roles through natural-language commands, restricted to administrators.
• WhatsApp & multi-channel delivery: People reach the assistants from WhatsApp, from the web workspace and from an assistant embedded in the applications they already use — with the same permissions, the same approval gate and the same audit trail on every channel.

Live Enterprise Agent Workflow Example

A worked example of one request moving through the Bi-Mind operations layer: the orchestrator routes it to a specialist agent, the agent reads the connected enterprise sources, every step is written to the audit trail, and the side-effect action waits for explicit human approval.

1. 01 Request: A user submits an operational request — for example, maintenance invoice reconciliation.
2. 02 Routing: The orchestrator routes the request to the Finance Specialist Agent for that domain.
3. 03 Sources: The agent reads the connected enterprise sources: ERP, service APIs and databases, under the tenant access rules.
4. 04 Audit: Every tool call and decision is recorded to the audit trail — 14 steps in this run.
5. 05 Human approval: The side-effect action is held and executed only after an authorized user explicitly approves it (human-in-the-loop).

How It Works

Sign in with Google or your corporate SSO. Your request is routed to the specialist assistant for that domain, which works through controlled tools under your tenant's access rules. Critical or side-effect actions require your explicit confirmation before they run.

Assistants

• Knowledge Base Assistant — Answers staff questions from an internal document corpus with retrieval, and cites the source passage it used. Example: "Which revision of the maintenance procedure applies to a line commissioned last year?"
• Database Performance Analyst — Reads a database performance report and returns a prioritized, manager-readable diagnosis. Example: "Summarize last night’s report and tell me which three waits to fix first."
• Database Operations Agent — Watches heterogeneous database estates for anomalies and proposes routine administrative actions for approval. Example: "Tablespace growth looks abnormal on two instances — propose a remediation plan."
• Network Operations Agent — Turns a high-level network intent into vendor device configuration and runs policy checks before anything is applied. Example: "Open this port for the branch VLAN, respecting the standard ACL policy."
• Log & Incident Analyst — Correlates logs across services to build an incident timeline and narrow down root cause. Example: "Checkout errors spiked at 02:10 — what changed and where did it start?"
• Document & Form Reader — Extracts and summarizes readable fields from uploaded documents and flags anything unclear instead of guessing. Example: "Read this scanned application form and list the fields that need a human check."
• Automation Assessment Specialist — Scores a described business process for automation readiness and returns a scorecard report with a recommended approach. Example: "Here is our invoice approval flow — how autonomous can it safely become?"
• ERP Operations Copilot — Reviews ERP sales, stock and operations data and produces an executive decision summary. Example: "Which product groups are below safety stock while sales are trending up?"
• Access & Role Manager — Manages data-access roles from natural-language commands, restricted to administrators and logged to the audit trail. Example: "Give the finance analysts read access to the cost center report."

Channels — WhatsApp, Web and Embedded

Agents are reachable where work already happens: in the web workspace, inside existing applications as an embedded assistant, and on WhatsApp. A WhatsApp group or a direct chat simply talks to the assistant that owns that subject, and the answer comes back in the same thread. The same sign-in, the same permissions and the same audit trail apply on every channel, so a message sent from a phone can never do more than that person could do when signed in.

On WhatsApp

• The right assistant per conversation: Each group or chat is bound to the assistant responsible for that subject, so people write in plain language with no commands or prefixes to remember.
• Same permissions as the web app: Who may ask, who may act and who may only read is decided by the person’s role, not by the channel.
• Human approval still applies: Before an action with consequences, the assistant states what it is about to do and waits for an explicit approval in the chat.
• Memory where it helps: A conversation can keep its context over time, or treat each request as a fresh topic — whichever suits the group.
• Recorded like any other request: A request raised from WhatsApp is written to the same audit trail as one raised from the web workspace.

• A maintenance group asks which equipment is overdue for service this week, and the assistant answers in the group with the list and the records it relied on.
• A field technician photographs a meter and sends it; the assistant reads the values and asks for a re-shot of the one digit it could not read, instead of guessing.
• An on-call group asks at 03:00 why the nightly job failed, and the assistant replies with the timeline and the first failing step.
• A manager asks for a credit limit to be raised; the assistant prepares the change, states the impact and applies it only after an explicit approval in the chat.

Outcomes

Enterprise systems coordinated through one secure layer, with tenant isolation, human approval on critical steps, and specialist assistants connected to the tools your teams already use.

In depth

• Architecture — How Bi-Mind keeps enterprise agents deterministic: an orchestrator that routes instead of improvising, GraphRAG hybrid memory, isolated tool execution over MCP, a human approval gate on every side-effect, and an end-to-end audit trail.
• Energy & SCADA — On-premise agentic AI for energy and industrial operations: correlate SCADA telemetry, historian data and work orders, surface anomalies early, and keep every control-adjacent action behind a human approval gate and a full audit trail.
• ERP & Finance — Agentic AI for ERP and finance operations: reconcile invoices against orders and receipts, explain the exceptions, prepare postings and corrections, and execute only after an authorized approver confirms — with a full audit trail.
• Database & IT Ops — Agentic AI for database and IT operations: read performance reports and turn them into prioritized diagnoses, correlate logs into incident timelines, propose routine administrative actions, and execute only behind a human approval gate.

FAQ

Why does your AI investment stay stuck as a chatbot?

The bottleneck is not model intelligence. It is systems that cannot be reached, actions that cannot be controlled, and value that cannot be measured. Externally, ERP, CRM, SCADA and legacy databases do not speak to each other, so manual approvals and integration dead ends stall the process. Internally, teams do not trust an uncontrolled model with production actions, so pilots never ship. Philosophically, an enterprise needs systems that decide, act and produce measurable value, not tools that only generate text. The published industry figures match this: MIT NANDA (State of AI in Business 2025) reports that 95% of enterprise generative AI pilots deliver no measurable P&L return, and Gartner (June 2025) predicts that over 40% of agentic AI projects will be canceled by the end of 2027 because of unclear business value and inadequate risk controls, with only around 130 of thousands of self-described agentic AI vendors offering real agent technology. Bi-Mind answers this with controlled tool execution, tenant isolation, a full audit trail and a human approval gate on every side-effect action.

What is Bi-Mind?

Bi-Mind is a multi-tenant enterprise AI agent platform for secure chat, specialist assistants, and controlled tool execution.

Which systems does it connect to?

Bi-Mind connects to enterprise systems through controlled tools, for example ERP, databases, knowledge bases, and network and IT operations, using specialist assistants built for each domain.

Does data leave the company?

Bi-Mind is multi-tenant with tenant access controls, and tool calls run with the signed-in user permissions.

Do critical actions run without human approval?

No. Critical or side-effect actions require explicit user confirmation before they run (human-in-the-loop); for example, orders are executed only after explicit confirmation.

How do I get started?

Sign in with Google or your corporate SSO, or contact info@bi-mind.com to arrange access.

How is it different from a free chatbot?

Bi-Mind adds tenant isolation and access controls, specialist assistants connected to enterprise tools, and controlled tool execution with human approval, which a free chatbot does not provide.

Can the agents work over WhatsApp?

Yes. A WhatsApp group or direct chat talks to the assistant that owns that subject, and the answer comes back in the same thread. The same sign-in and permissions apply as in the web workspace, actions with consequences still wait for an explicit approval in the chat, and the request is written to the same audit trail.

What can an agent actually do, in concrete terms?

Typical anonymized examples: a maintenance group asks which equipment is overdue for service and gets the list with source records; an on-call group asks why the nightly job failed and gets a correlated log timeline with the first failing step; a technician sends a photo of a form and the agent extracts the fields and flags the unreadable ones instead of guessing; a manager requests a limit change and the agent prepares it, states the impact and executes only after an explicit approval.