TeamON partner brief

Operational AI
built into the business.

TeamON orchestrates the full system: workflow, agents, models, context, permissions, actions, interfaces and operations. The company gets an operational business function in a controlled environment — not another AI pilot.

model- and runtime-agnostic
deployment in infrastructure you control
human oversight and a named operator
one foundation for every new function

More revenue captured Prevent missed leads and shorten time to close.

Lower leakage and operating cost Catch exceptions earlier and remove repetitive manual work.

Faster management decisions Managers see the issue, its owner and the deadline.

How to explain TeamON in 30 seconds

We turn one repeatable workflow into a managed AI business function: connect company data and systems, define roles and permissions, automate approved steps, and measure the result. Existing models, agents and integrations stay where they already work.

The client gets a dedicated operating environment with a business KPI, explicit permissions, human approval and clear sign-off criteria — not another chatbot.

An employee controls the company’s unified AI system — Target operating model: one system connects requests, data, documents and actions while a person remains in control.

01 · Product

What the company actually gets

Not a model licence or an empty platform. The company gets a working business function and a foundation for launching the next ones.

Business function

Completes a defined job end to end and moves an agreed business KPI.

Core product

TeamON engine

Encodes the workflow, roles, context, permissions, state, interfaces and versions.

Product foundation

Company instance

An isolated environment with the company’s data, connections, rules and access controls.

Client environment

Implementation and operations

Process discovery, integrations, testing, team enablement, client sign-off and continuous improvement.

Path to the outcome

Process contract

Trigger, 3–5 connected tasks, business KPI and sign-off criteria.

Method and roles

Operating rules, checks, skills and reasons for human handoff.

Context

Company terms, entities, examples, exceptions and decision history.

Actions

Approved CRM systems, spreadsheets, email, files, channels and APIs.

Control

Outcome owner, operator, permissions and approval of high-risk actions.

Workspace

Conversation, working canvas, files and management dashboard.

Reliability

Versioning, monitoring, backup, recovery and rollback.

Development

New rules and functions are tested and added to the same foundation.

TeamON product composition around an interchangeable technical core — The language model and agent runtime execute each step. TeamON turns them into a managed business function.

02 · Why TeamON

Not another model. A system that runs real business processes

ChatGPT, Claude, Codex and agent frameworks remain valuable tools for individuals. TeamON is for business workflows that must run reliably, safely and without depending on a single internal champion.

If you build it piece by piece	What TeamON adds
A model and personal prompts High-quality output for one person.	Outcome contract Trigger, tasks, KPI, accountable owner and sign-off criteria.
A separate bot for every task Separate servers, rules and history.	One foundation The next functions reuse the same environment, roles and controls.
Brittle point-to-point scripts Permissions and exceptions are scattered across the code.	Controlled actions Permissions, approvals, audit trail and human handoff.
Knowledge trapped with one champion When that person leaves, progress stops.	Company context Methods, rules and decisions live in a versioned environment.
A prototype that works in a demo It works only while its author keeps it running by hand.	Operations Monitoring, updates, recovery, training and an operator.

Agent runtime

An external agent framework (harness), TeamON’s own core or the client’s platform — connected through an adapter, tested and signed off.

Language model

Cloud, enterprise or local. We choose it based on quality, security, speed and process cost.

Existing AI stack

We keep prompts, agents and integrations that already work. We do not rebuild the system for the sake of a technology label.

Swap technology without rebuilding the operation

TeamON separates the process, context, permissions and interface from any specific model or execution environment. A change requires validation, not a new product built from scratch.

model, agent runtime or hosting changes → the validated business workflow remains intact

03 · Initial use cases

Start where money and delays are visible

These are starting configurations, not ready-made industry packages. We prove the impact using each company’s own data.

Sales

Handling inbound enquiries

The system gathers context, qualifies the request, prepares the reply and next contact, then records the outcome in the CRM.

fewer lost leads;
faster first response;
more meetings and deals.

We measure: response time, leads without a next step, meetings and conversion.

Marketplaces

Margin and cash-leakage control

The system consolidates approved data and detects stock-out risk, negative margins and inefficient advertising.

see financial risk earlier;
reduce losses;
remove manual monitoring.

We measure: detection time, missed exceptions, losses prevented and team hours.

Owner

Operational control

The system collects status from several sources and highlights deviations, owners and deadlines.

faster management decisions;
less manual status gathering;
issues do not remain unowned.

We measure: hours spent compiling updates, overdue items, unowned tasks and time to decision.

Synthetic dashboard for sales and financial exceptions — Synthetic example with no client data: channels are consolidated in one view and a financial exception is highlighted for human review.

Check the economics first

If the expected impact does not comfortably cover implementation and ongoing work, we do not sell that process.

incremental profit + labour saved + losses prevented − operating cost

04 · How it works

From answer to action

The outcome is not a summary or a list of “next steps”. A verifiable change must appear in the actual workflow.

EventA lead, order, document or incident requires a response.
ContextThe system reads only approved data, rules and operating history.
AI workIt gathers, checks, calculates and prepares an action.
ControlA person makes the decision whenever the cost of error is high.
OutcomeThe task is handed off, the record is updated and the metric is measured.

Path of a managed AI function from event to action — Human authority is defined in advance. The system cannot expand it on its own.

05 · Implementation

Launch ends when the business function is signed off

We do not hand over an “installed server”. The client validates the complete path on its own data, from event to outcome.

Select the business case

Identify a recurring loss, an owner and a metric. Deliverable: a use case with credible economics.

Define the process

Document the trigger, 3–5 tasks, exceptions and baseline metric. Deliverable: signed workflow and acceptance criteria.

Design the environment

Choose hosting, AI core, sources and permissions. Deliverable: delivery architecture and access matrix.

Build the function

Deploy the instance and configure roles, context and actions. Deliverable: a working client instance.

Test safely

Run real scenarios without risky external effects. Deliverable: tests and an exception log.

Go live and sign off

Launch the process, train the team and hand over the operating procedure. Deliverable: a production function signed off by the client, plus the chosen support model.

Client, partner and TeamON architect sign off the business workflow — Implementation starts with the process and ends with joint acceptance of the outcome — not the handover of an installed server.

06 · Hosting and security

Deploy on TeamON, partner or client infrastructure

Physical hosting does not change the product. Before launch, we define infrastructure ownership, data boundaries, access, backups and responsibilities.

TeamON infrastructure

Fast, managed launch. TeamON is responsible for the agreed technical environment.

Managed by TeamON

Partner infrastructure

The partner hosts the industry or client environment; operating responsibilities are defined in the delivery agreement.

Managed by the partner

Client infrastructure

Maximum company control: its server or cloud, accounts, data and access policies.

Managed by the client

Each option undergoes its own readiness review and user acceptance. A fully closed environment requires a local or approved internal model and local integrations.

Knowledge sovereignty

Hosting can change. The company keeps its accumulated knowledge.

All knowledge produced by the agent from a company’s data and work remains that company’s asset: context, accepted decisions, rules, exceptions and client-specific adaptations. This remains true even when the instance runs on TeamON or partner infrastructure.

TeamON servers = partner servers = client servers → knowledge and outcomes belong to the company

The company’s unified knowledge asset remains intact when hosting changes — Concept illustration: infrastructure changes while client context, decisions and configuration remain continuous.

The client owns

its company instance and state;
its data, accounts and source systems;
its context, accumulated decisions, rules and exceptions;
client-specific adaptations within the agreed contractual scope;
the right to export and move its environment.

TeamON is responsible for

platform code and common methodology;
product version and secure delivery;
team training and operating procedures;
limited, revocable access;
updates, recovery and rollback.

Ownership boundary between the client and TeamON — Ownership does not change with hosting. Export scope, usage rights and the exit procedure are agreed before launch.

Where is the intellectual-property boundary?

The company retains its data, context, outcomes and accepted client-specific adaptations. TeamON retains the platform code, product foundation and common methods that existed before the project. Client experience can become a shared improvement only with permission, after data removal and a separate review.

Can it run in a fully closed environment?

The architecture supports it. A fully closed deployment requires a local or approved internal model, a compatible agent runtime and local integrations. That configuration needs its own design and acceptance cycle; our internal instance is not evidence of a production-ready air-gapped deployment.

Can the system be installed on the client’s computer or server?

Yes. A client computer can be used for a simple local installation. For a continuously running business function, a dedicated server or enterprise cloud with backups and access control is preferable.

07 · Workspace

Users see the work, not a technical console

Complex work happens in the browser workspace. Quick requests can arrive through Telegram or another approved channel.

Persistent roleIts own instructions, skills, tools and working context.

Continuous conversationThe conversation continues when the working view changes.

Working canvasThe agent creates a task-specific deliverable and revises it on request.

Company dashboardOutcomes, deviations, owners and deadlines without copying private conversations.

Files and dataCSV/JSON files and approved sources are connected for the process.

Events and schedulesThe function can be triggered by a message, a business event or a schedule.

TeamON App

The main workspace: roles, conversations, work surface, outcomes and settings.

Available in Staff

Work channels

Web, Telegram, MAX and other channels connect to the right role inside a specific instance.

Configuration-dependent

Desktop and Extension

An auditable local bridge to applications and the browser: on-screen context stays local, while approved actions run in place.

Foundation exists · integration in progress

Operator MCP

One controlled operator interface to the selected instance: inspect, prepare, apply and verify the result.

Operator control plane

AI embedded in the flow of work

We are building an adaptive interface that follows the task while the agent works inside an approved application or browser tab. Access boundaries stay visible, and the agent explains each intended action before it runs.

observation → explanation → shadow mode → assistance → limited autonomy

The screenshot proves that the interface exists, not that an external client deployment is ready. Universal upload of PDF, DOCX, XLSX and image files into every chat has not yet been proven; formats are validated case by case. The full “observation → autonomy” cycle is a development direction, not a ready feature in every instance.

08 · Ongoing service

The subscription funds ongoing outcomes

The implementation remains usable without mandatory managed service. The subscription applies when TeamON continues to operate, improve or extend the function.

Technical support

We monitor availability, fix incidents, release updates, make backups and restore the system.

Operational service

A named operator monitors the metric, handles new exceptions and releases verified improvements. The current RUB 70,000–100,000 monthly range is still being tested with the market.

Agents improve through controlled versions

A new rule never appears silently. The operator explains the change, tests it without external effects, releases it gradually and keeps a rollback path.

exception → validation → new version → measurement → accept or roll back

09 · Evidence

The foundation is validated internally. Client ROI is not — yet

We separate what has been validated internally from results that require a live client workflow.

TeamON Staff v0.2.42 · revision 44Internal instance accepted: conversation, working canvas, company dashboard, memory, operator path, backup and rollback.
10 of 10 acceptance checksThe current version passed all user and operational checks.
TeamON Operator v0.1.2An operator package has been released for controlled work with the selected instance.
TeamON proprietary core v0.1.0The local contract and all 16 tests pass. A client deployment of this core has not yet been accepted.

What we do not promise yet

proven return on investment for the new product;
repeatable delivery to several independent companies;
a fully autonomous department;
every integration out of the box;
silent, automatic self-learning;
a ready public expert hub and product index.

What do the six roles in the internal Staff mean?

Director, Back Office, Sales, Product, Operations and Development form an internal working composition used to validate the shared TeamON environment. They are not six ready-made client products or proven business functions.

10 · Partnership

The partner owns the relationship. TeamON owns delivery

The partner does not need to become an AI developer. Their strength is trust, industry knowledge and the ability to spot a costly recurring loss.

No platform to build from scratch

Starts with a deployable TeamON stack: engine, interface, secure delivery and operating model.

Creates an industry product

Adds their own method, market language, use cases and relationships — without mixing client data.

Chooses the level of involvement

Introduction, joint selling, industry expert, operator or integrator.

1 · Costly-loss signal
2 · Trusted introduction
3 · Process diagnosis
4 · Proposal and implementation
5 · Acceptance and compensation

How to interpret the RUB 120,000 guideline

This is not the price of a model, engine or the entire platform. It is the working guideline for a first bounded implementation: diagnosis, function build, company instance, basic integrations, training and acceptance.

The partner does not promise a price in advance: first define the process and boundaries, then give the client a fixed proposal.

The role, client boundaries and compensation are agreed before the deal. Payment is tied to money actually received and real responsibility; there is no universal promise of a “percentage forever”.

Founder

Larry Ngomirakiza

AI engineer, mathematician and AI systems architect.

Larry personally leads product and architecture for the first deployments: he turns a tangled workflow into a verifiable operating model, connects business rules to production software, and preserves the parts of the client’s existing AI stack that already work.

11 · Short answers

What clients usually ask

These answers qualify the opportunity. TeamON is not the right fit for every company.

How is this different from employees using ChatGPT, Claude or Codex?

Those tools make individuals more productive. TeamON connects people and systems into a repeatable workflow with an accountable owner, permissions, a business KPI and sign-off criteria. If occasional prompts are enough, TeamON is unnecessary.

Why not install an agent runtime ourselves?

You can. Installation alone does not identify a viable process, encode company rules, assign authority or create an operating model. TeamON is accountable for turning the technology into a business function the client can test and sign off.

We already have agents. Will we need to rebuild everything?

Not necessarily. Models, prompts, agents and integrations that already work can remain. TeamON adds the missing control, delivery and operations layer, then validates the workflow end to end — without replacing technology for its own sake.

What remains if we stop paying?

Under any agreed hosting model, the company retains its data, context, accumulated knowledge, configuration, outcomes and client-specific adaptations within the contractual scope. TeamON provides the agreed export or migration plan; operator work and revocable access end. Platform usage rights, transfer format and infrastructure costs are defined in the contract.

Will we become dependent on TeamON, a model or an agent runtime?

The delivery is designed to avoid dependence on a single vendor. Client state is separated from platform code; models and agent runtimes can be replaced through adapters and revalidation. Before launch, we agree the export format, access-revocation process and migration path. Exit is controlled, although migration still requires engineering work.

Do you guarantee profit growth?

There is no universal guarantee. Before launch, we record the baseline, expected change and acceptance criterion. A percentage increase in profit can only be promised when the outcome can be attributed reliably.

Is this a ready-made AI employee or custom development?

It sits between those extremes: a versioned product foundation, configured around the company’s workflow, data, permissions, roles and acceptance criteria.

Next step

The first conversation is about the loss, not AI

If the answers are concrete, we diagnose one process. If a company simply “wants AI”, we first help identify an economically meaningful problem.

What repeats every week?
What was most recently lost or delayed?
Where is the required data?
Who owns the outcome?
Which number will prove improvement?