AI agent for project evaluation and preparation of commercial proposals

The initial business problem

Preparing commercial proposals is a complex and lengthy process. The manager needs to study the user's request, collect requirements, analyze the brief, select appropriate solutions, form a project structure, calculate deadlines, estimate the budget and prepare the document. Even for an experienced employee, this work can take from 1 to 4 hours per commercial proposal.

There are other difficulties. For example, information is located in different sources, and some of the data is stored in documents or Google Drive. In addition, price lists and project estimates are constantly updated. It is also necessary to adhere to the standard structure of the CP.

Solution concept

The main goal of the project is to automate the preparation of commercial offers within CRM. For this purpose, an AI agent was developed, which should significantly speed up the work of managers and reduce the burden on the sales team.

Thus, the agent can analyze consumer requests, as well as take into account information from correspondence + use data from internal databases to form a CP.

AI agent data sources

To generate not template, but truly relevant and well-reasoned commercial offers, the AI agent works as a single point of data collection and interpretation. Therefore, each offer is formed not from scratch, but on the basis of accumulated knowledge, proven solutions and real cases - taking into account the specifics of the business, client needs and project goals. Let's consider what data sources the agent uses to form the CP.

1. CRM data: for example, correspondence with the user, lead description, manager notes, communication history.
2. Project brief: An AI agent can work in conjunction with an AI assistant (also created by our team), which generates a brief.
3. Internal databases: this includes price lists, company websites, as well as service catalogs, technology descriptions, and information about typical projects.
4. Documents and knowledge base: data from Google Drive, company documentation, presentations, and technical descriptions of solutions.

How an AI agent works

The work of an AI agent is not just the automation of the preparation of commercial offers, but a clearly structured decision-making process: from collecting context to forming a reasoned offer. A digital agent does not generate text out of thin air - it consistently analyzes, compares and uses data to compile an offer. Below are step- by-step stages that describe the full cycle of work of an AI agent.

Step 1. Receiving a customer request

The user sends a request via a website, email, or form. A manager or AI agent communicates with them. Then the user fills out a brief with project details, or a digital assistant can do it instead. Not only the request itself is recorded, but also the context: the source of the lead, user behavior, previous interactions.

Step 2. Analysis of information

The AI agent studies correspondence, briefs, and additional materials. The digital assistant also identifies the client's main needs, psychology, and behavioral patterns, as well as priorities, budget constraints, and expected results.

Step 3. Data search

AI accesses the knowledge base: price lists, project examples, technical solutions, company website pages, photos, videos, files from Google Drive. The RAG approach is used: the agent pulls up only relevant data and cases for a specific request.

Step 4. Formation of a commercial offer

Artificial intelligence independently creates the structure of the CP. It adapts to the type of client (B2B/B2C), the complexity of the project, and the stage of decision-making.

Step 5. Manager verification

The employee reviews the document, makes corrections, generates a PDF, and sends it to the client. Thus, the manager keeps the final quality under control, clarifies the accents, and strengthens the offer, taking into account the negotiation strategy.

What does an AI agent generate?

Full-fledged commercial offers created by our AI agent include:

• project description (taking into account the business context and its objectives);
• product goals (with a focus on measurable results and business value);
• system architecture (with an explanation of the main technical solutions and approaches);
• functional modules (with details of roles, usage scenarios and work logic);
• development stages (with a breakdown into phases and expected results of each step);
• assessment of deadlines (taking into account complexities and possible risks);
• preliminary budget (with transparent cost structure and optimization options).

Limitation

The AI agent does not replace the manager. The system:

• forms a preliminary proposal;
• does not send the document automatically;
• requires verification by manager.

Interface within CRM

The AI agent is integrated directly into the AvadaCRM interface, allowing managers to work with it in a familiar environment without additional transitions between systems. Such integration makes the process of generating a commercial offer as efficient and transparent as possible: all data is already at hand, and AI control is intuitive.

The manager can:

• Click the “Generate CP” button → the agent instantly analyzes the available data about the project, collects relevant information from knowledge bases, and generates a preliminary document.
• Receive the generated document → the document is ready for viewing and editing, structured in blocks, so you can evaluate the proposal and check its content.
• Make changes → the manager can correct texts, add clarifications, or adapt the style to a specific client, preserving all the AI's developments.
• Send to user → the final file can be sent directly from CRM along with accompanying files. This approach speeds up communication and reduces the risk of errors.

Solution architecture

To ensure consistent quality of commercial offers and scalability of the process, the AI agent is built as a system of interconnected components. Each of them is responsible for a separate part of data processing — information collection, its interpretation and generation of a commercial offer — and together they form a single logic of work without manual breaks in the process. Below we demonstrate the structure of the system and the interaction of its key components.

Technology stack

The AI agent is built on a combination of natural language processing, data processing, and integration solutions. This allows the system to not just generate text, but to work with context, internal company knowledge, and business logic for generating commercial offers. Then there is a set of technologies that ensure the clarity of its work and scalability.

• Natural Language Processing (NLP): provides understanding of user requests, analysis of correspondence, briefs, and formation of content-correct commercial offers based on context.
• RAG (Retrieval-Augmented Generation): allows the agent to access the internal knowledge base, select relevant cases, technologies, and solutions, and use them during CP generation.
• AI Content Generation: is responsible for creating streamlined commercial offers — from presentation logic to text formulation and argumentation.
• Data analytics and estimation logic: used to calculate project timelines, budgets, and complexity based on typical models and accumulated data.
• Cloud data processing: ensures stable system operation, prompt processing of requests, and the ability to scale to different loads.
• Integration with CRM system (AvadaCRM): allows you to work with leads, communication history and automatically apply this data to form CP.
• Integration with Google Drive: serves to store, organize, and quickly access generated documents and materials.
• Integration with email and web forms: guarantees automatic receipt of user requests and their transfer to the system for future processing.
• OpenAI API: used for text analysis, suggestion generation, information structuring, and context management.
• Python: the main development language in which the logic of the AI agent, data processing, and integration between system components are implemented.

Result

The project developed an AI agent for preparing commercial proposals, integrated into the AvadaCRM CRM. The agent is able to analyze customer requests, briefs, and the company's internal databases, work in conjunction with other AI tools, and automatically generate ready-to-use commercial proposals.

Thanks to the implementation of the AI agent , the following results were achieved:

• acceleration of CP preparation: formation time was reduced from several hours to several minutes;
• reducing the burden on managers: employees spend less time on routine and more time working with customers;
• standardization of sentence structure: all sentences have a logical, understandable and uniform presentation;
• prompt responses to users: automation speeds up communication and increases satisfaction;
• increasing the efficiency of the sales team: decisions are made instantly, as correctly as possible, taking into account the client's full profile and needs.

Scaling the solution

The AI agent is built to be easily adaptable to different types of companies and processes. Its logic is universal, and integrations with CRM and other systems make scaling easy.

• CRM systems. The AI agent works with any leads and communication history.
• Development agencies. AI generates CP for projects of varying complexity.
• IT companies. The agent supports the preparation of proposals for clients and internal projects.
• Consulting companies. Artificial intelligence automates the preparation of presentations and proposals for consumers.
• Service companies. The digital assistant helps to quickly generate commercial documents for users and partners.

Want to implement an AI agent to prepare commercial proposals?

We'll analyze your proposal preparation process, connect the necessary data sources, and implement a solution that will work within your team's logic and CRM. 👉 Submit a request – let's discuss your project.