Case study

From 4 Days to 4 Minutes: Automating Fire Safety Quotes with Deep Agents

4 minutes

To generate complex, high-confidence quotes

20% Uplift

vs unquoted revenue

12-Hour

End-to-end turnaround including expert sign-off

The upshot

Modernising high-stakes sales operations with AI agent architecture

Datasparq partnered with Encore, a leading US fire safety provider, to revolutionise their mission-critical quote generation process. By deploying an advanced "Deep Agent" architecture, we transformed a legacy manual workflow into a streamlined, automated engine that captures millions in previously "leaked" revenue.

Historically, the complexity of fire safety systems meant that 25% of identified deficiencies were not quoted within a timely manner - our solution ensures 90% are now successfully processed, with the remainder flagged for expert review.

Accelerated Speed-to-Lead: Significant reduction in quote lead times directly correlates with higher customer conversion rates.
Eliminated Revenue Leakage: Automated identification and quoting of deficiencies ensures maintenance opportunities aren’t missed.
Scalable Intelligence: A future-proof cloud architecture that digitises "tribal knowledge" for long-term growth.

AI and data solutions development partner

The opportunity

Closing the 4-day gap in fire safety compliance

Encore’s technicians identify thousands of system deficiencies during on-site inspections. Historically, translating these technical findings into customer quotes was a manual bottleneck, taking four days on average. This delay hindered "speed-to-lead" and, due to the sheer volume and complexity, resulted in 20% of revenue opportunities being missed due to delayed response times. Encore needed a way to condense this window to minutes, ensuring safety-critical repairs were quoted accurately and instantly.

The solution

Deploying "Deep Agents" for complex business logic

A human-centric approach to AI automation

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Strategic Discovery & COE Workshops‍

We partnered with Encore’s Center of Excellence (COE) to map complex fire safety business rules. This allowed us to extract "tribal knowledge" from senior experts and translate it into a digital brain capable of handling diverse deficiency types.

Building the "Deep Agent" Architecture‍

Leveraging the LangChain Deep Agents framework on GCP, we built an AI that manages multi-step, long-running tasks. Unlike standard bots, this agent uses Vector Database RAG to reference "golden examples" of historical quotes, ensuring high-confidence outputs.

The "Golden Quote" Pilot‍

We deployed an MVP to a select group of representatives, using real-world feedback to refine the agent’s logic. A key breakthrough was the ability to capture new "golden quotes" in real-time, which were fed back into the dataset to sharpen accuracy.

US-wide Service-Line Rollout

To ensure adoption, we rolled out the tool systematically by service line. This ensured that the outputs were perceived as a productivity-boosting partner rather than "AI slop," gaining trust across the national sales team.

1. Proof of concept

First testing with a supermarket depot demonstrated capabilities beyond established industry tools, impressing transport leadership with performance that surpassed existing SaaS solutions.

2. First deployment

Applied to a major retailer's largest and most complex depot, exploring delivery types and time window flexing. This phase delivered substantial annual savings that continue to benefit operations today.

3. Extended capabilities

Extended to a more challenging network with multiple depots, handling unionised workforces, two-person deliveries and a wider variety of vehicle types. This phase demonstrated the optimiser's adaptability to different operational constraints.

4. Enterprise-ready

Standardised data formats and pipelines to create a system applicable across diverse customer networks, with further optimisation for a home improvement retailer adding wait time and compactification features that doubled performance.

Interactive example

Try it for yourself

Explore how the science works in this PlayML data science notebook

The impact

The implementation of the Deep Agent has redefined Encore's customer experience and bottom line. By reducing technical generation time from 4 days to just 4 minutes, Encore can now respond to safety needs at the speed of business. Even with human-in-the-loop verification, the total end-to-end turnaround is now 87% faster than the previous manual standard.

Crucially, the solution has solved the problem of "unquoted deficiencies." By automating the heavy lifting of quote drafting, 90% of all identified issues are now quoted to the client, ensuring fire safety systems are maintained to the highest standard while simultaneously driving significant incremental revenue.