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A modern kitchen design featuring white cabinets, wooden countertops, and decorative plants on shelves.

Kitchen & Interior
Design Company (NDA)

Executive Summary

A kitchen and interior design company specializing in custom residential renovation projects was generating steady inbound demand, yet only around 10% of inquiries converted into signed contracts. Every request was manually reviewed in the CRM, with no structured way to identify high-intent buyers early.

Darwin introduced an AI-driven qualification layer integrated directly into the client’s existing CRM. The system automated initial conversations, gathered structured project data, and ranked leads by conversion likelihood before human involvement.

Within two months of deployment, lead identification accuracy reached 95%. Conversion on the existing inbound flow increased by 30%. Eighty percent of early-stage interactions became automated, and marketing spend scaled by 50% without additional headcount.

Introduction

The company provides residential kitchen and interior renovation services, working directly with homeowners on project planning, budgeting, and material selection. Sales are consultation-based, with scope and pricing defined through direct discussion before contract approval.

Inbound demand was consistent, but early-stage qualification relied entirely on manual CRM review. Budget range, project size, and intent became clear only after a manager engaged in conversation.

The objective was to introduce automated first-touch qualification, structure incoming project data, and create a prioritized pipeline so the team could focus on high-intent buyers.

The problem

Leads entered the CRM without qualification

Every inquiry appeared the same inside the CRM. Budget range, project size, and intent were clarified only after a manager responded. Around 10% of contacts became customers, yet the team manually reviewed and replied to the entire volume.

Support workload exceeded capacity

The customer support team managed roughly half of submitted requests. The rest were delayed or left without timely follow-up. Time spent answering low-budget or exploratory inquiries reduced availability for contacts ready to proceed.

Marketing growth tied to manual review

Additional ad spend would increase the number of inquiries requiring manual processing. Without automation at the first-touch stage, each increase in volume required proportional human effort.

The Solution

Darwin implemented a lead qualification framework that automated initial conversations with inbound contacts, integrated directly with the client’s CRM, and introduced internal scoring. The implementation included CRM cleanup, a custom LLM-based agent, direct CRM integration, and a scoring mechanism. Below is how it was implemented:

01

Custom LLM-based qualification agent

A custom LLM-based agent was deployed with business-specific context, including services, pricing structure, materials, and customer profiles.The system initiates first-touch conversations, asks structured questions, and collects the information required for evaluation before a team member becomes involved.

02

Direct CRM integration

A custom integration was developed to connect the system directly to the client’s CRM.Conversations are stored automatically within existing lead records. When a team member changes lead status or takes over communication, the system stops responding to ensure a clean transition to manual handling.

03

Custom LLM stack implementation

Off-the-shelf chatbot solutions were tested but were unable to maintain sufficient business context or follow the required conversation flow.A custom LLM stack was built using Python, LangChain, LangSmith, OpenAI, Claude, Kubernetes, and PostgreSQL to support conversation control and context management.

04

Internal lead scoring mechanism

Each contact processed by the system is evaluated against predefined criteria.The scoring model ranks contacts by likelihood to convert with 95% accuracy. The customer support team receives a prioritized list instead of reviewing all inquiries manually.

Results

Darwin’s implementation of automated qualification and lead scoring delivered measurable improvements in conversion efficiency and marketing scalability.

95% Lead Identification Accuracy

Darwin’s scoring mechanism identified high-potential leads with 95% accuracy, enabling the support team to shift from reviewing every inquiry to working from a prioritized list.

30% Increase in Conversion Rate

Following the deployment of Darwin’s qualification framework, conversion rate on existing inbound leads increased by 30%.

80% of Initial Interactions Automated

With Darwin’s system handling early-stage engagement, 80% of the first five customer touchpoints are now fully automated.

50% Increase in Marketing Spend with Stable ROAS

By removing presale capacity constraints, Darwin enabled a 50% increase in marketing spend without a decrease in return on ad spend.

Before-and-After
Comparison

Metric

  • Lead Qualification
  • Inquiry Handling
  • First Response
  • Acquisition Capacity
  • Conversion Rate

Before

  • Manual review, no scoring
  • All inquiries processed equally
  • Dependent on staff availability
  • Limited by team workload
  • ~10% baseline

After

  • Automated qualification with 95% accuracy
  • Leads ranked by conversion likelihood
  • Automated first-touch engagement
  • 50% increase in marketing spend without added headcount
  • Increased by 30%

Client Feedback

Working with Darwin gave us a practical solution to a problem we had been trying to solve for some time. They understood our setup, worked closely with our team, and delivered a system that fits the way we operate. The whole process felt focused and well-managed.

Head of Sales,

A Kitchen and Interior Design Company

A 3D animated character with a beard, wearing a light gray suit and white shirt, looking at a smartphone.

Conclusion

By partnering with Darwin, a kitchen and interior design company transformed how inbound requests are qualified and managed. What began as a CRM cleanup evolved into a structured qualification layer that now supports scalable growth.

With automated first-touch engagement, prioritized lead handling, and integrated scoring logic, the company increased conversion rates and expanded marketing activity without increasing headcount.

Looking ahead, the qualification model can continue to evolve alongside new services and changing customer profiles, ensuring the system remains aligned with business priorities.

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