Operational Bottlenecks in Pre-Sales Workflows
Meeting Tomorrow is a leading event production company delivering end-to-end event services for major corporate clients across the United States.
With decades of industry experience, the company specializes in managing every aspect of event execution, including venue sourcing, logistics coordination, audio-visual production, and on-site technical support.
The client’s reputation is built on deep operational expertise, supported by teams with up to 20 years of specialized experience in transforming client requirements into seamless, high-quality event experiences.
However, as the organization scaled, pre-sales teams faced increasing operational complexity:
- Manual quoting bottlenecks: Converting vague client requests into detailed technical orders with hundreds of inventory line items relied heavily on manual work, creating delays and limiting scalability.
- Dependency on tribal knowledge: Critical operational knowledge existed primarily with senior employees, making the quoting process difficult to standardize and vulnerable to knowledge loss.
- Limited operational capacity: Growing demand required the company to scale efficiently without significantly increasing headcount or losing the human expertise that differentiates its event services.
Meeting Tomorrow knew they needed to automate repetitive processes, improve information access, and increase operational efficiency.
Implementing an AI Agent Workflow Platform
Addressing these challenges required more than a standalone chatbot or automation script. A coordinated AI agent system capable of retrieving contextual information, orchestrating workflows, and supporting operational decisions was essential.
Our team engaged as a strategic engineering partner to design and implement an intelligent AI-driven workflow platform.
1. Building an Agentic Workflow Architecture with LangGraph
A non-linear AI workflow architecture was implemented using LangGraph to mimic how experienced project managers process client requests and operational decisions. Instead of following fixed sequential logic, AI agents can evaluate multiple paths, revisit previous steps, and dynamically determine the optimal workflow based on the context of each request.
An agentic approach enables more flexible and adaptive handling of complex quoting scenarios, improving both efficiency and decision quality across the workflow.
2. Developing a Prompt Engineering and Validation Layer
Each workflow node was fine-tuned using specialized prompt engineering techniques combined with statistical validation rules to improve output consistency and reliability.
Validation mechanisms help prevent hallucinations and ensure generated outputs align with operational standards and business requirements. A layered quality control approach enables the system to maintain accuracy while handling highly variable customer requests.
3. Enabling Inventory-Grounded Generation
AI-generated recommendations were strictly grounded in the company’s actual inventory database to ensure operational accuracy. Every generated recommendation maps directly to valid NetSuite ERP inventory IDs and passes through post-processing validation before being surfaced to users.
Grounding outputs in real inventory data eliminates inconsistencies between generated proposals and operational availability, reducing errors and improving trust in the system.
4. Designing a Human-in-the-Loop Workflow
A human-in-the-loop framework was implemented to support collaboration between AI agents and operational experts. Rather than fully automating proposal generation, the system produces draft orders that are approximately 50–70% complete, allowing experts to review, refine, and finalize outputs efficiently.
An assisted workflow approach solves the “blank page” problem while preserving human oversight and maintaining the service quality expected by customers.
5. Enabling Context-Aware Processing Across Multiple Data Sources
The platform was designed to ingest and process information from multiple formats, including CSV files, PDFs, and email correspondence. AI agents use these inputs to build comprehensive context for generating accurate and relevant proposals.
Context-aware processing improves the system’s ability to interpret ambiguous requests and align outputs with customer expectations and operational constraints.
6. Integrating with Existing NetSuite ERP Infrastructure
Seamless integration with the company’s existing NetSuite ERP environment ensured that the new AI-driven workflows could operate within established operational processes. Compatibility with legacy infrastructure minimized disruption and accelerated adoption across teams.
A tightly integrated architecture allows organizations to modernize workflows without replacing core operational systems, reducing implementation risk and improving long-term scalability.
“Our approach was simple: automate the 70–80% of work that’s mechanical, and protect the 20–30% that requires judgment. That’s not a compromise, it’s good system design. Every attempt to push AI beyond its reliable boundaries is just another way of manufacturing technical debt.”
Adam Komorowski
Senior Data Scientist at Addepto
Transforming Operational Workflows with Intelligent AI Agents
The partnership between Addepto and the client transformed fragmented and manual pre-sales operations into a scalable AI-driven workflow ecosystem. Teams can now access contextual information faster, automate repetitive activities, and generate proposals more efficiently.
Before
- Quoting relied entirely on manual work from highly experienced staff
- Senior team members spent significant time on repetitive inventory selection and data entry
- Bottlenecks in the quoting process limited the ability to scale new opportunities
- Standard equipment selection reduced time available for higher-value creative work
- Critical operational knowledge remained concentrated within individual team members
After
- AI pilot automates 50–70% of standard equipment selection workflows
- Experienced staff shifted from manual tasks to strategic review and creative planning
- Draft orders generated in minutes instead of hours, increasing operational throughput
- Teams can focus on high-value event production tasks that require human expertise
- Scalable architecture supports growing demand without proportional headcount increases
- Operational knowledge is embedded into the system while preserving human oversight
In their current operation landscape, the improved workflow orchestration and AI-driven automation reduce operational bottlenecks while enabling more consistent and responsive customer interactions
As part of KMS Technology, Addepto continues to deliver enterprise-grade AI solutions that help organizations transform operational complexity into scalable, intelligent workflows.
Ready to scale your operations with intelligent AI agents? Contact us today!