For account managers, proposal teams, sales, and management when requirements are scattered across multiple documents.
Astrafox product · AI for sales and proposals
Qualify RFQs before your sales team invests in a response
The AI agent organizes the request for quotation (RFQ) materials, compiles the requirements, and prepares a preliminary recommendation on whether to pursue the opportunity. It explains the rationale and flags conditions that could block participation. The account manager reviews the recommendation and makes the final decision on whether to bid, the scope, and the response terms. We usually deploy the agent on the AMODIT platform, within the process the team already uses.
We deploy the solution on Azure, Google Cloud, or AWS. We configure data sources, permissions, and connections to company systems.
Before → After
Qualify the opportunity before starting proposal work
The agent reduces the time spent manually analyzing RFQs that fall outside the company's offering or contain conditions that block participation.
Before
The account manager reads the RFQ, specifications, and attachments before determining whether the opportunity fits the company's offering.
After
The agent compiles the requirements and presents a preliminary recommendation for the account manager to review.
Before
The team manually checks the documents for conditions that could rule out participation.
After
The agent flags potential blockers and points to the information behind each finding.
Before
The bid decision waits until the full set of documents has been reviewed and the reasoning has been assembled.
After
The account manager reviews the recommendation and rationale, then makes the final decision on the next step.
How the agent works
From RFQ documents to a preliminary bid recommendation
Receives the materials
It works with the RFQ, specifications, and attachments provided in the process.
Organizes the requirements
It brings information from the documents into materials the team can review in a consistent order.
Recommends a next step
It prepares a preliminary recommendation based on the agreed criteria, explains the rationale, and flags participation blockers.
Hands it to the account manager
The account manager reviews the recommendation and makes the final decision on whether to bid, the scope, and the response terms.
AI recommends; a person approves.
The agent prepares the preliminary recommendation, rationale, and blocker list. The designated person reviews the output and makes the final decision.
What the user gets
A clear basis for the bid decision
A proposed next step
A preliminary recommendation on whether to pursue the RFQ, based on the criteria agreed during configuration.
Reasoning ready for review
The requirements and findings that support the recommendation, organized for the responsible team.
Conditions that require attention
Conditions that may prevent participation, flagged against the agreed criteria for a person's assessment.
Integrations and data
The agent works within the company's environment, not alongside it
We usually deploy the agent as a solution on the AMODIT platform. The agent then becomes a step in the process: it works within the same document workflow, with the same permissions and case history, and the result reaches the right person as a task. AMODIT connects to ERP and CRM systems, data warehouses, and other company applications. When an organization does not use AMODIT, we integrate the agent directly with its systems.
RFQ sources
We select the documents and data the agent uses to prepare the assessment.
Controlled connections
We configure the interfaces and permissions required to retrieve the materials and deliver the result.
Next step in the process
We define where the reviewed result goes. The connection method depends on the interfaces available in the specific system.
Security and data
Full control over where data is processed
We deploy AI on Azure, Google Cloud, or AWS in European Union regions. The models are not trained on client data, ensuring compliance with GDPR and the AI Act.
EU regions
We select and control the data processing location within the solution architecture.
Three platforms
We have experience deploying AI on Azure, Google Cloud, including Vertex AI, and AWS.
No training on client data
The models used in the product are not trained on client data.
Minimum permissions
The agent receives only the access required to read the materials and prepare the result.
Client implementation
We start with one RFQ type and a clear decision
We configure an existing model for the client's process. We do not train LLM or OCR models.
Process and responsibility
We define who assesses the RFQ, what result is needed, and who makes the decision.
Materials and exceptions
We select the documents, criteria, incomplete cases, and situations that require manual handling.
Configuration and integrations
We select the model, instructions, permissions, and connections to company systems.
Testing and launch
We test the result on representative examples, launch the product, and monitor the quality of its work.
Related solutions
Connect RFQ assessment with subsequent proposal work
FAQ
Frequently asked questions about the RFQ agent
What does the AI Agent for RFQ Assessment do?
It organizes the RFQ materials, compiles the requirements, and prepares a preliminary recommendation with its rationale and participation blockers. The account manager reviews the output and makes the final decision.
Who decides whether to bid?
The account manager or another designated person reviews the agent's recommendation and decides whether to bid, the scope, and the response terms.
What materials does the agent use?
It works with the RFQ, specifications, attachments, and other materials selected during process configuration.
Does the product integrate with company systems?
Yes. The agent usually operates as a solution on the AMODIT platform, which connects to ERP and CRM systems, data warehouses, and other applications. When a company does not use AMODIT, we integrate the agent directly with its systems through the available interfaces.
Where is the data processed?
We deploy AI on Azure, Google Cloud, or AWS in European Union regions. We control where the data is processed, and the models are not trained on client data.
What AI experience does Astrafox have?
One of Astrafox's owners holds a PhD in artificial intelligence and has worked with AI since 2009.
How does implementation begin?
We start with one RFQ type, its input materials, the recommendation and blocker criteria, and the person responsible for the final decision. We then configure the integrations, permissions, and tests.
AI Agent for RFQ Assessment
Show us an RFQ your team has to qualify manually
Using that example, we will show how the agent prepares a preliminary recommendation, explains the rationale, and flags blockers for a person to review.
See the agent work on your example