How it works?
Overview
The problem with traditional pre-sales workflows
In most organizations, estimates and proposal workflows depend on scattered knowledge found in Slack, emails, spreadsheets, documents, and past projects.
As companies grow, it gets harder to reuse knowledge in a consistent way. Teams often solve the same problems again, recreate similar estimates, and lose historical context between project details.
Most AI proposal automation tools only offer chat features, instead of operational workflows integrated into company sales processes.
How AI Pre-Sales Agent solves it
Our AI estimation tool connects to your company’s knowledge sources, such as Slack, project documents, past estimates, and workflows. The AI sales agent finds relevant information, identify missing requirements, generate clarification loops, and prepares organized outputs.
Rather than being just a chatbot, this workflow acts as a layer that connects and coordinates your company’s existing systems, processes, and tools.
Architecture overview
The sales proposal automation consists of four operational layers:
1. Knowledge ingestion layer
The AI Pre-Sales Agent links and syncs your company’s knowledge sources into a system you can reuse for future proposals.
2. Retrieval & reasoning workflows
It retrieves historically analyzed estimates, reusable delivery modules, operational assumptions, and implementation patterns based on incoming project requirements.
The system identifies similar historical workflows and uses them as context to generate new estimations and proposal outputs.
3. Operational orchestration layer
The agent manages workflows across Slack, Docs, Sheets, Jira, CRMs, and your internal systems. You can adjust input and output interfaces as needed.
4. Human review layer
It supports approval steps and lets people review outputs before they are finalized.
Data flow
Project brief submitted → Historical workflows & delivery modules retrieved → Similar estimates and assumptions matched → Missing requirements identified → Clarification workflow generated → Estimate logic prepared → Proposal outputs generated → Human review & approval.
Get started
Step 1: Connect AI sales agent to organizational knowledge
Integrate Slack, project documentation, historical estimates, and operational systems into the contextual retrieval layer.
Step 2: Configure workflows
Set up estimation rules, proposal formats, clarification steps, and outputs that fit your organization.
Step 3: Start generating operational outputs
After setup, the system can create estimates, proposal drafts, operational documents, and workflow outputs by using your company’s past knowledge.
Roadmap
- CRM integrations
- Proposal presentation generation
- Multi-agent workflow coordination
- Extended approval workflows
- Multi-project organizational memory
- Custom enterprise connectors
- BI & analytics integrations
Outcome
Cut down on routine tasks and sales efforts in pre-sales and delivery. Save time by automating manual processes, respond faster to your clients, and make better use of your company’s knowledge.
The AI Pre-Sales Agent shows that enterprise AI workflows can do more than just chat. They can connect directly to your operational systems, company processes, and past delivery knowledge.
This open-source workflow was created by the team behind enterprise-scale Next.js and AI engineering systems.
