zremb

80% less manual input thanks to smart data sync across CRMs

Machine learning-powered assistant that simplifies sales processes and delivers actionable insights from every interaction.

Key Requirements

We helped secure an innovation grant of PLN 3 million for ZREMB.
Thanks to a Lean approach, we successfully decreased the project budget for the MVP version from PLN 3 million projected by the client to PLN 500 thousand, optimizing the budget use.
Martha won the PropTech Festival 2022competition among the likes of Żabka Nano and Park Cash.
We greatly optimized the estimated implementation time for an MVP from 2 years to just 6 months.

About the client

ZREMB (eng. ZREMB Elevator Equipment Company) is an established company with over 70 years of successful presence in the Polish market. ZREMB excels in providing holistic technological solutions for elevators, with a distinct emphasis on sustainability and environmental preservation. Their expertise lies in delivering cutting-edge elevator systems engineered for heavy-duty use, ensuring prolonged operational lifespans without the necessity for frequent replacements.
What truly distinguishes ZREMB and contributes to their success are seamlessly integrated lift control systems. These systems boast universal compatibility with equipment manufacturers worldwide, setting a new standard for industry synergy. Their broad offer included extensive elevator solutions, encompassing small freight elevators, accessible platforms, passenger and freight elevators, escalators, moving walkways, and steel construction elevator shafts. From installation and modernization to repair and maintenance – they've got you covered.

About the project

Operating in a highly specialized field of industrial automation and seeking innovation that would further confirm their role as an industry leader, ZREMB has envisioned a state-of-the-art elevator management system.

Here comes Martha – an AI-powered robot with a mission to offer a personalized, human-like approach instead of the simple press-the-button interaction we know from elevators installed in our buildings. To turn our client’s vision into reality, Martha needed to utilize a well-rounded machine-human interface, incorporating automatic recognition and voice & touch interfaces.
Yet – that’s not all. It was necessary to introduce graceful degradation so that when one interface is not preferred by the user, the other one takes over. The users are, therefore, empowered to switch interfaces as they see fit. Combined with a must-have support for the Polish language and offline accessibility, Martha called for a custom solution.

The ZREMB project wouldn’t have been possible without ITCorner. It all started when Rafał Pisz, CEO of QuantUp, shared information about the Martha project within our community. With vast experience and a proven track record of successfully navigating innovative projects, Vazco was strongly recommended as a business & technology partner for ZREMB.

Key AI Techniques & Highlights

High-Accuracy Transcription Pipeline
We systematically compared four STT (Speech-to-Text) solutions, focusing on domain adaptation for education and sales.
Domain-Specific Language Model
Custom dictionaries and post-processing rules handle brand-specific terms, typical questions, and competitor mentions.
Contextual Classification
Our system uses ML classifiers to gauge context - was “free” used in a correct or violating scenario? Did the rep mention “PESEL” or “ID” requirements for the right target audience?
Self-Validation Against QA
We tested the system’s outputs against a human QA reference set. In one instance, the AI identified an error that human QA had incorrectly labeled as “passing” - resulting in an impressive “1:0 for AI”.
Handling False Positives
We discovered one false positive triggered by a transcription error. With minor refinements to domain-specific synonyms, we swiftly corrected the model - fully eliminating that error class in subsequent tests.

Results & Impact

Human-Level Accuracy
Side-by-side testing showed that on provided call sample, the AI not only matched QA performance but managed to outperform it through identifying policy violations in overlooked by QA (false-negative) and captured cases where QA wrongly marked fulfilled policy (false-positive).
Scalable Insights
The system aggregates errors into high-level reports - revealing patterns (e.g., repeated mention of wrong age limit or missed disclaimers) that inform targeted training programs.
Reduced QA Time & Cost
By automating the bulk of call analysis, manual review hours are dramatically reduced, allowing the QA team to focus on complex issues and targeted coaching. It showed potential for covering 100% of the calls in given time and cost.
Consistent Branding & Compliance
The AI solution enforces a uniform standard, ensuring all reps follow the same guidelines for pricing disclosure, disclaimers, and marketing consents.
x10
Test Coverage Increase
>80%
QA Cost Reduction
99%
AI Test Accuracy
9m
Test Coverage Increase
500%
Test Coverage Increase

Lead innovation in your industry,
just like ZREMB does

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