Bit intends to solve the pain points of enterprise implementation with AI and seek Pre-A round financing

Project Launches Pre - A Round of Financing 

Shenzhen Bit Intent Technology Co., Ltd. (hereinafter referred to as "Bit Intent"), which focuses on the implementation of enterprise AI, has launched a Pre - A round of financing. The funds from this round will be mainly used for product R & D, AI engineering capability building, expansion of benchmark customers, and supplementation of the core team. Bit Intent was founded in May 2026 by Liu Tiansi, a former Huawei Level 21 expert and Tencent T12 expert. The company has launched three product lines, SheetBot, GeoOps, and AtlasBot, around three directions: high - frequency enterprise data processing, AI search visibility, and knowledge engineering governance, aiming to solve the implementation problems of "verifiable but difficult to produce, able to answer questions but difficult to deliver" in the application of large models in enterprises.

Enterprise AI Enters the Implementation Stage, but There are Still Breakpoints in Business Systems

In the past year, large - model tools have been rapidly popularized within enterprises. However, many enterprises have encountered new implementation bottlenecks when moving from pilot projects to production. On the one hand, AI chatbots and single - point tools can improve local efficiency but are difficult to stably access core processes such as data processing, market growth, and knowledge governance. On the other hand, enterprise internal data, documents, systems, and expert experience are scattered in spreadsheets, business systems, and document libraries. The output results of AI lack verifiable evidence and are difficult to directly support business decisions.

In Liu Tiansi's view, enterprise AI procurement is shifting from proof - of - concept to result delivery. Customers are more concerned about whether AI can enter the real workflow, whether it can process complex files and large - scale data, whether it has permission, audit, and traceability capabilities, and whether it can form a verifiable ROI in a relatively short period. Therefore, Bit Intent has converged its product direction to scenarios with high frequency, verifiable results, and clear budget paths in daily enterprise work.

Three Product Lines Cover Data Execution, AI Search Growth, and Knowledge Engineering

SheetBot is the product that Bit Intent is currently prioritizing. It is positioned as an enterprise data execution and business delivery engine. The product starts from Excel and online spreadsheet scenarios, supporting users to complete operations such as data cleaning, classification and summarization, report generation, and batch output of PPT and Word through natural language. It also makes engineering adaptations for the analysis of large files over hundreds of MB, enterprise system connectors, and private deployment. This product is targeted at high - frequency spreadsheet - using departments such as finance, operations, sales, and administration, solving problems such as long report delivery cycles, a large amount of manual repetitive processing, and difficult integration of cross - system data.

GeoOps is for brand visibility management in the era of AI search. After connecting to more than 20,000 authoritative media sources, the product can track the situations of brand recommendation, citation, and comparison in mainstream AI portals such as DeepSeek, Doubao, Tongyi Qianwen, and ChatGPT, providing visibility scores, competitor comparisons, content optimization diagnoses, and weekly review suggestions. As users increasingly obtain brand and product information through AI portals, enterprises need new tools to measure the exposure quality of their brands in AI answers.

AtlasBot is for the linkage scenario of enterprise knowledge engineering and AI Agent. The product takes ontology modeling as the core, organizing objects, concepts, and relationships scattered in business systems and document libraries into a unified semantic layer. Combining knowledge graphs, GraphRAG, and rule engines, it provides a knowledge base that can be cited, explained, and audited for AI Agents. When it comes to business operations such as work order creation, status change, and approval flow triggering, the system controls risks through permission verification and manual confirmation mechanisms.

The three product lines correspond to the needs of data execution, growth evaluation, and knowledge governance in enterprise AI implementation respectively. Bit Intent hopes to first establish usage entrances in high - frequency and essential scenarios, and then expand the customer lifetime value through enterprise versions, private deployment, and knowledge engineering delivery.

Technical Route Emphasizes Engineering Delivery, Not Just Model Invocation

Bit Intent's technical route does not only rely on large - model invocation but builds an engineering system around "executable AI Agents". SheetBot focuses on the data execution engine, including large - file parsing, block - based calculation, intelligent caching, operation orchestration, and result verification capabilities. The goal is to enable AI not only to generate analysis suggestions but also to complete a verifiable data processing process.

AtlasBot focuses on knowledge engineering and Agent governance. The system unifies business semantics through ontology modeling and then incorporates documents, structured data, rules, and external tools into a controlled invocation framework, ensuring that AI Agents have a basis when answering questions, have permissions when executing, and have audit records afterwards. For customers in finance, government, and manufacturing industries with high requirements for data sovereignty and compliance, the product can be privately deployed and supports SDK/API integration with existing enterprise systems.

Liu Tiansi believes that the difficulty in enterprise AI implementation is not to make the model answer more questions but to incorporate the uncertain generation ability into a verifiable, roll - backable, and iterative business system. Bit Intent's product design also revolves around this judgment: deeply develop high - frequency scenarios first, retain manual confirmation in key processes, and let technical capabilities serve real delivery results.

Business Model Starts from Low - Threshold Tools and Expands to Enterprise - Level Delivery

In terms of business model, Bit Intent combines SaaS subscriptions, charging based on function and model usage, industry template packages, enterprise versions, and private project delivery. In the early stage, it acquires users in high - frequency office scenarios through SheetBot, and then provides team versions and enterprise versions for professional teams. GeoOps corresponds to the budget for market and brand growth, while AtlasBot is targeted at the budget for knowledge governance, data governance, and enterprise AI platform construction.

The target customers include growing enterprises and medium - to - large - sized organizations with clear needs for spreadsheet processing, report delivery, AI search exposure, knowledge Q&A, and process automation. Typical user departments cover operations, finance, sales, marketing, knowledge management, data governance, and IT teams.

Currently, Bit Intent has entered the product verification and early commercialization verification stages, and some seed customers have participated in product testing. The team plans to complete the verification of more than 30 paying customers in the next 12 months, achieve a monthly recurring revenue of over 200,000 yuan, and accumulate 2 to 3 industry benchmark cases. The above goals will be the key focus of the phased operation after this round of financing.

Founding Team Comes from the Front Line of Complex System Construction in Large - Scale Technology Companies

Founder Liu Tiansi has nearly 20 years of experience in technology and management. He has participated in infrastructure, platform engineering, and complex system construction at Huawei and Tencent, and has long been responsible for technical systems with high requirements for reliability, concurrency, and engineering. His entrepreneurial judgment comes from front - line observations: enterprises do not lack AI concepts and demonstration applications. What is truly scarce is the productization ability to stably enter business processes and form a delivery closed - loop.

At this stage, the team focuses on product R & D and customer verification. In the future, key positions such as enterprise sales, solution design, AI engineering, and delivery implementation will be supplemented. In the financing fund plan, about 50% will be used for product R & D and AI engineering, 30% for market expansion and benchmark customer construction, and 10% for team expansion and operation.

Next Step: Move from Product Verification to Enterprise - Level Expansion

Next, Bit Intent will continue to promote product refinement and business verification around SheetBot, GeoOps, and AtlasBot, focusing on improving the connectors for commonly used enterprise systems, industry templates, ontology models, and private delivery capabilities. The company hopes to accumulate high - frequency usage scenarios through the data execution entrance and then extend to growth evaluation and knowledge engineering scenarios, gradually forming a basic capability platform for enterprise AI implementation.

In the stage where enterprise AI is moving from trial use to budget - based procurement, whether AI capabilities can be transformed into verifiable business results is becoming the core standard for customer decision - making. The Pre - A round of financing of Bit Intent will be mainly used to verify the replicability of this product path.