Complex B2B companies already live between CRM, ERP, documents, chat tools and manual approvals. Data exists, but execution and control stay fragmented.
Logicot OS is the working environment where B2B companies run workflows, documents and AI under control
We start with complex B2B companies where sales, documents, approvals and ERP already live across fragmented systems. Logicot OS brings portal, workflows, business modules and AI into one controlled operating environment.
The investor read before the deeper narrative.
This is not a standalone chatbot and not another CRM module. Logicot OS is a working environment where workflows, documents, roles and AI have to run together. The broader product read sits on the Logicot OS page.
Logicot OS combines the company portal, workflows, business modules, AI actions and management visibility inside one working environment. The broader product read sits on the Logicot OS page.
The first launch focuses on one limited path: document or request, AI check, approval, action inside the process and a management view of the outcome. That flow is unpacked further in the demo.
There is already a working demo core, 319 schema migrations, 900+ backend tests, 4 demo scenes and a selected early pilot scenario. The technical base is separated on the architecture page.
Document -> approval -> action -> control
Logicot OS does not start with a promise to replace the whole enterprise stack. The first launch is built around one controlled end-to-end scenario where the pain is already repeatable: document, roles, approval, action and control of the outcome.
An input document or request enters the portal and starts the flow.
AI helps verify data, extract fields or prepare the next action.
A responsible person confirms the step where control is required.
The workflow executes the business action and records the outcome.
Management sees status, metrics and the outcome of the same scenario.
The platform can carry a business scenario from input event to management result instead of only presenting screens. The wider product frame remains on the Logicot OS page.
It does not claim full ERP replacement, heavy integration chains or the entire scenario breadth of the company in the first pilot.
What can already be checked today.
This is not a pitch without a product. There is already proof that can be discussed through code, demo structure and the selected early pilot scenario.
Schema depth and data-model evolution show a platform far beyond an early draft stage.
The backend test base supports the investor thesis of a disciplined platform foundation.
Portal, AI, workflows and analytics can already be shown as one connected and controlled sequence.
The first launch can already be discussed around a limited scenario without overclaiming the full platform breadth.
AI is already inside companies. The next bottleneck is execution inside real work.
The market window is no longer about another helper tool. It is about systems where AI is embedded in roles, documents, approvals, permissions and control of the result.
According to McKinsey's November 5, 2025 survey, 88% of respondents said their organizations use AI in at least one business function. The bottleneck is shifting from access to scale.
In the same survey, 23% of respondents said their organizations already scale agentic AI in at least one function, while another 39% are experimenting. The question is no longer access to AI, but how AI gets embedded into real workflows, roles and control rules.
Deloitte reports that worker access to sanctioned AI tools rose from under 40% to around 60% across 2025, or roughly 50%. At the same time, expectations around auditability, approvals and deployment control keep rising.
Why the current stack does not solve the problem end to end
Companies already try to solve this pain through separate systems. The problem is that each one closes only one part of the work.
Those systems store data and rules, but they do not always create one executable flow between teams.
They are easy to start with, but they lose control, history and repeatability as complexity grows.
It helps answer questions, but it does not manage roles, permissions, actions and audit inside the process.
The first is hard to repeat as a product, the second is too slow to deploy where a fast controlled start is needed.
Target revenue is built around a deployed company environment, not around access to one module
The investor question here is not a public pricing table. It is the target revenue shape and the expansion path around an installed company portal.
The first contract is assembled around the portal, infrastructure, selected modules and a limited launch scope.
Revenue is retained through support, updates, operations and the ongoing development of the working environment.
Growth comes through new workflows, deeper automation, integrations and AI inside working scenarios.
The round is sized against the next fundable milestone, not against open-ended product expansion.
In the illustrative operating model, $750k funds roughly 16–17 months of runway at a blended burn of about $44k per month. The purpose is to build the first delivery team, harden the selected pilot scope and make the path from demo to pilot more repeatable.
Engineering, QA, design and rollout form the first delivery layer that turns the current core into a repeatable operating model.
Cloud, AI consumption, developer tooling and the operating infrastructure required for the pilot scope and early deployment.
Packaging the pilot-ready scope, launch logic and the first rollout playbooks so each case is not rebuilt from scratch.
Deck, demo discipline, proof surfaces and sales-grade material that support investor and design-partner conversations.
Legal and operating work around the round, the first pilot contracts and the early company layer.
A buffer for slower revenue ramp, additional implementation cost and early go-to-market variance.
By the next fundable milestone, testable outcomes should be visible.
The round should be judged not by broad promises, but by whether the path from demo to pilot and early deployment becomes materially more repeatable.
Engineering, QA, design and rollout no longer act as ad hoc founder support, but as the first repeatable delivery contour.
The path from the first walkthrough to a limited pilot is packaged and depends less on one-off manual assembly.
The key pilot-ready intersections become more stable both as product surfaces and as an early delivery model.
Launch logic, roles, support and early operating delivery are packaged into a more standard rollout layer.
Materials, launch scenarios and demo discipline should support a more concrete conversation about the first limited pilot.
Team and right to launch
Logicot OS is built from hands-on work around automation, CRM/ERP, documents and AI. The next step is turning that velocity into the first delivery team.
The product frame comes from real operating pain between systems and manual handoffs, not from an abstract category pitch.
The round adds the first team around engineering, QA, design and rollout so the path from demo to pilot becomes repeatable.
Who Logicot OS is relevant to first
The first entry point is where documents, approvals, ERP actions and manual handoffs between teams already create a repeated operating loss.
Sales, documents, approvals and internal operations no longer fit inside a fragmented stack of separate systems.
The more transitions there are between teams, roles and systems, the more expensive delay, error and weak control become.
Russia remains the primary monetization market, Kazakhstan strengthens the growth wedge and `.com` stays the later expansion layer.
Sources
- McKinsey, November 5, 2025: 88% of respondents said their organizations use AI in at least one business function
- McKinsey, November 5, 2025: 23% of respondents already scale agentic AI in at least one function, another 39% are experimenting
- Deloitte, January 21, 2026: worker access to sanctioned AI tools rose from under 40% to around 60% across 2025, or roughly 50%
Short answers for the first investor read
This FAQ mirrors the visible investor logic on the site and supports both human scanning and machine-readable grounding.
Logicot OS is at the pre-seed stage. There is already a working demo core, a selected pilot-ready scope and a guided investor path from deck to demo and founder call.
Logicot OS is raising a $750k USD pre-seed round. The round is sized for roughly 16–17 months to the next fundable milestone and funds the first delivery team, infrastructure, AI tooling and pilot delivery.
Logicot OS is a portal-first operating layer for complex B2B companies: a company portal, executable workflows, business modules, control and governed AI inside one working environment.
Logicot OS does not place AI next to work. AI acts inside selected working scenarios through workflows, tools, approvals and audit boundaries.
Public proof includes 319 schema migrations, 900+ backend tests, 4 guided demo flows and a selected scope that can already be discussed as an early pilot-grade operating environment without overclaiming the whole platform.
The first monetization wedge is Russia and Kazakhstan. Belarus remains adjacency, while the broader .com path sits on top of the same product category.
The canonical market pack is fixed: McKinsey 2025 says 88% of organizations already use AI in at least one function, 23% already scale agentic AI and another 39% are experimenting; Deloitte reports worker access to AI tools rose by roughly 50% in 2025.
The target path is one chain: deck, then guided demo, then founder call and only then a pilot discussion if there is fit.
Who we want to meet now
We are currently speaking with pre-seed funds, angels, strategic operators and design partners.
The path is simple: after the first read, the conversation moves into a Logicot team call, then a guided demo and, if there is fit, a pilot-scope discussion.