Real-Time Cost Estimation: Empowering Sales Teams with Data-Driven Pricing
An industrial cleaning service provider needed to accelerate quote generation while giving salespeople visibility into actual project costs and margins in real-time, right during customer calls.
Need
Quote delays required callbacks to customers while margin blindness prevented salespeople from assessing profitability when pricing competitively.
Background
An industrial cleaning service provider’s sales team needed to streamline their quote generation process. Creating accurate estimates required consultation with technical staff to calculate labor requirements, equipment needs, and travel logistics – a workflow that prevented real-time responses during customer calls. Sales teams delivered accurate numbers, but only after calling customers back and losing deal momentum.
Beyond speed, there was a visibility gap. Salespeople could quote prices but didn’t have insight into actual project costs or profit margins during customer conversations. This made it difficult to negotiate strategically or to see pricing trade-offs in the moment. The team wanted visibility into actual costs alongside their quotes, enabling more informed pricing decisions rather than discovering profitability only after project completion.
The Question
Could we automate project estimates with machine learning and integrate real-time travel costs to speed up quotes and improve consistency?
Approach
Developed a machine learning model that predicts project costs, then integrated it with travel estimates to generate complete pricing sheets showing real-time profit margins.
The Approach
The client provided historical project data spanning their full range of drum sizes, giving the model enough variety to predict accurately across all configurations. Each project record included three key outputs: cleaning days, technicians required, and job boxes needed.
We built three connected linear regression models, one for each output (cleaning days, technicians, job boxes), where each prediction informs the next. For instance, predicted cleaning duration influences how many technicians are needed. We engineered features to capture operational patterns, such as how cleaning efficiency changes with drum size. Linear regression was chosen for transparency: users can see why the model made specific predictions and confidently adjust estimates based on their field experience when needed. The models were deployed as a secure web application, allowing authorized team members to generate quotes from any browser.
The interface was designed around the natural sales workflow, guiding users through a five-step estimation process:
- Enter customer and project details
- Review machine learning predictions (adjust if needed)
- Integrate AI-powered travel cost analysis (Perplexity AI) via structured prompt generation and JSON response parsing
- See total costs with transparent profit margins
- Choose pricing strategy and generate quote
Each estimate downloads as a CSV file for project records. When actual project data comes in, these files feed back into the model to improve future predictions.
Results
Rapid quote generation with full cost understanding empowers sales teams to negotiate confidently and close deals while customers are engaged, without sacrificing profitability.
The Result
After pilot testing with the sales team, the tool went live. It delivers on both goals: faster, more accurate quotes and real-time visibility into costs and margins. The sales team uses it daily, and the system improves continuously as completed projects feed back into the model.
Key Capabilities in Practice:
- Real-time quote generation during customer calls with manual override capability
- AI-powered travel analysis via Perplexity integration comparing driving versus flying with comprehensive cost breakdowns
- Complete margin transparency displaying true project costs and profit for every quote
“Our estimating process was a bottleneck with a single point of failure. Proposals took 30 minutes to an hour for experts, or 4-6 hours for others, and results were inconsistent. Freya Systems delivered an automated tool that reduced estimate creation to 5 minutes while capturing our institutional knowledge. The team’s thorough requirements gathering meant we got exactly what we needed. Now we have consistent, trusted estimates and clear visibility into our margins—a true win under our belt with measurable ROI.”
— Matt Lippmann