Services

Barton Malow's operational truth lived across a data warehouse, BI dashboards, and a data catalog - CRM opportunity pipelines, financial records, project status reporting, and market intelligence. Answering a routine question meant filing a request with a BI analyst and waiting. Off-the-shelf AI assistants could not close the gap: they had no path to the warehouse, no concept of who was permitted to see which data, and no knowledge of the domain rules that make construction numbers correct - a fiscal year that does not follow the calendar, entity-level filters, and mandatory joins between certified tables. Worse, they answered anyway.


Challenge

Working inside Barton Malow's Data & Automation Engineering team, we helped build Moneypenny - a company-owned, multi-model AI platform where every answer is produced by executing a tool, never by recalling one. The architecture separates the conversation layer from the capability layer using the Model Context Protocol, so warehouse search, SQL execution, BI reporting, catalog lookups, web automation, charting, and deterministic math all run as governed tools. Models from four providers are selectable per conversation, and data access is resolved per person rather than through a shared account.

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Solution

Rather than asking a model to write SQL directly, we built a two-stage pipeline. A metadata search tool first resolves the question to the correct certified tables, returning their join specifications, entity filters, and fiscal-year semantics. Only then does a second tool compose and execute the query. That ordering is what separates a plausible query from a correct one: in benchmark testing, a question about open projects for a specific company returned 21,314 records through this pipeline versus 370 from direct natural-language generation, which had selected the wrong table and dropped the company filter entirely. Arithmetic is handed to a deterministic expression parser rather than the model, removing an entire class of silent error.


Barton Malow Key Feature 1

Retrieval-First SQL Generation

Most AI-on-data projects connect through a single privileged service account and attempt to filter results afterwards. We inverted that. Every user is provisioned their own warehouse service principal with OAuth token refresh, and catalog grants are derived from their assigned data domain, with custom agents constrained to a single domain by design. Governance is therefore enforced by the warehouse itself rather than by prompt instructions - an employee without finance access does not receive a filtered answer, the query simply never runs. That made it possible to open the assistant to the whole organization instead of a vetted pilot group.


Barton Malow Key Feature 2

Identity-Scoped Warehouse Access

Moneypenny was built so the tool layer could outlive any single interface. Users compose custom agents with their own instructions, tool sets, and embedded reports. Scheduled prompt automations run saved questions on a timezone-aware schedule and email the results, turning ad-hoc queries into standing reports. Browser automation handles sources with no API at all. The same governed capabilities are exposed four ways - web application, Microsoft Teams, a versioned REST API, and an OAuth 2.1-secured MCP endpoint for external agents - with streaming state shared across Kubernetes pods so a response survives a restart mid-answer.


Barton Malow Key Feature 3

A Platform, Not a Chat Window

Lexis Solutions helped Barton Malow build a governed, multi-model AI assistant that answers business questions with warehouse-backed SQL - outperforming native NL2SQL on 62% of real queries.

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delivery surfaces - web, Teams, REST API, and MCP - served by a single governed tool layer

shared warehouse credentials, with per-user service principals across every data domain

Lexis Solutions helped Barton Malow build a governed AI assistant that answers business questions with real SQL, outperforming native NL2SQL on 62% of queries.

Enterprise AI Data Assistant Outperforming Native NL2SQL by 62%

Dan Stephenson

Governed Enterprise AI Assistant Replacing BI Ticket Queues

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Onno Plus needed to rapidly scale its operations while managing an increasing volume of leads, clients, reviews, and legal workflows across multiple platforms. Their existing systems were fragmented, with sales, support, and operations relying on separate tools that didn’t communicate effectively. 

This made it difficult to track performance, automate processes, and scale efficiently. The company required a single, extensible platform that could unify teams, automate repetitive work, and support continued growth without operational bottlenecks.

Lexis Solutions built an AI-first CRM platform that automated key workflows across sales, account management, and legal operations. AI-powered tools analyze review data, assist lawyers in generating review removal requests, and leverage historical case data to improve accuracy and speed. Automated review scraping continuously feeds structured data into the system, enabling smarter prioritization and faster decision-making.

Developed from scratch, the platform embeds AI directly into daily workflows rather than treating it as an add-on. Combined with custom data pipelines and real-time integrations, this approach significantly reduced manual effort and allowed Onno Plus to scale operations efficiently while maintaining consistency and control.


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We built a custom CRM that unified sales, support, account management, and legal workflows into a single system. Sales representatives could manage leads, generate offers, assign follow-up tasks, and track deal progress, while account managers handled clients, orders, and review removal processes. This eliminated data silos and gave management full visibility into performance across teams.

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End-to-End CRM for Sales & Operations

The platform integrates with key external services such as email, calling systems, maps, and review platforms. Automated daily scraping collects new reviews from sources like Google, Trustpilot, and Kununu, while workflows trigger follow-ups, reminders, and status updates automatically. These automations significantly reduced manual work and ensured no lead or review was missed.

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Automation & Integrations at Scale

An AI-based tool was developed to assist lawyers in generating review removal complaints. Using Anthtopic models enriched with historical data and prior removal cases, the system accelerates document preparation while maintaining consistency and quality. This reduced turnaround time and allowed legal teams to handle a higher volume of cases efficiently.

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AI-Powered Legal Assistance

Discover how Lexis Solutions built an AI-driven CRM platform that automates review monitoring, accelerates legal workflows, and optimizes sales operations. By combining custom data pipelines, intelligent automation, and Anthtopic-powered tools, the system enabled Onno Plus to scale faster while maintaining high operational efficiency.


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OnnoPlus Admin Screenshot

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210%

99.8%

216%

Lexis Solutions built an AI-driven CRM automating review monitoring, legal workflows, and sales operations, enabling Onno Plus to triple sales.

AI-Powered CRM Platform Driving 3X Sales Growth for Onno Plus

Walter

Onno Plus Scales Review Management with AI-Powered CRM, Achieving 3X Sales Growth

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Finansi.bg faced the formidable task of developing a platform that could efficiently extract and present critical financial data from a vast and diverse set of documents. The primary challenge was dealing with inconsistently structured data across __2 million documents__, where not all pages held relevant information. This required a sophisticated solution to accurately sift through the noise and extract only the most pertinent data. Additionally, the platform needed to maintain high Google rankings, deliver exceptional site speed, and ensure a seamless user experience across all major browsers, all while controlling costs in processing and managing this extensive data.

To address these challenges, we employed a data-driven architecture combining advanced AI models, semantic retrieval and automated data-processing pipelines. The platform processes more than __2 million document__ records, identifying the relevant financial information hidden across inconsistently structured company reports.

The AI architecture combines __Retrieval-Augmented Generation__ (RAG), __Voyage AI embeddings__ and __ChromaDB__ for semantic document retrieval with __Anthropic Claude__ models for intelligent document analysis and structured financial-data extraction. Relevant document content is identified and supplied to Claude, which interprets and extracts the required financial information into standardized outputs. Automated validation and exception-detection mechanisms ensure that uncertain cases are routed for additional review.

This architecture enabled Finansi.bg to process financial documents at a scale and speed that would have been economically impractical using manual processing alone.


Finansi case study solution

The platform combines semantic classification with __Claude-powered document intelligence__. __Voyage AI__ embeddings and __ChromaDB__ identify and retrieve relevant document pages, while __Claude__ analyzes the selected content and extracts the required financial information into standardized structured outputs. An automated pipeline sanitizes and normalizes the data before and after model processing, while additional algorithms validate the resulting data.

Throughout the process, __error detection algorithms__ flagged documents or pages for manual review, ensuring the highest level of data integrity. Out of all processed documents (__over than 2 million__), __fewer than 8k__ required human intervention, highlighting the efficiency and accuracy of the automated system.

Finansi case study feature 1

AI-Powered Document Classification

Our innovative approach reduced the expected data processing costs by __80%__ through the application of various resource optimization techniques, making the solution __highly cost-efficient__. By calculating the wages of manual data processors, it was evident that handling this volume manually would have been prohibitively expensive and time-consuming.

In contrast, our AI-driven solution slashed costs by __99.1%__, reducing the expense from an estimated __2 million euros__ to just __18k euros__. This automation accomplished in a fraction of the time what would have taken a team of employees __over a year__ to process manually. This not only made the project financially viable but also ensured timely and scalable data handling for ongoing operations.

Finansi case study feature 2

Cost-Effective Data Processing

The team achieved a __98-speed rating__ by leveraging modern tools like __Next.js, React and TailwindCSS__, optimizing the platform for rapid performance across all major browsers. This focus on cross-browser compatibility ensures a seamless and responsive experience on any device or browser. The result is a highly efficient platform that exceeds industry standards for speed and usability, driving user engagement and satisfaction.

Finansi case study feature 3

High-Performance Web Architecture

Discover how Finansi.bg leveraged AI to revolutionize financial data extraction, achieving __40x__ the speed of manual methods. While competitors processed __10k__ companies per month with errors, our solution analyzed __416,000__ companies monthly. This breakthrough led to rapid user growth and an __80%__ reduction in operational costs.

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210,000

Lexis Solutions helped Finansi.bg build an AI-driven financial platform, reaching 160K users in one year while reducing data processing costs by 80%.

AI-Powered FinTech Platform Driving 160K Users and 80% Cost Cuts

Emil-Popov

Finansi.bg Leveraged AI to Transform Financial Data Processing, Achieving 250k Users in Just One Year

The client needed to aggregate coupon and promotional data from a large number of websites across multiple countries, languages, and regional formats. Each source had different structures, anti-bot protections, and inconsistent data quality. Scaling the system while maintaining accurate merchant identification and coupon validity was a major challenge. On top of that, the distributed nature of the scrapers required robust monitoring to quickly detect failures and data anomalies.

We designed and built a modular, scalable web scraping infrastructure capable of handling hundreds of independent data sources reliably. The system was developed with a standardized data model, shared utilities, and templated scrapers to accelerate onboarding of new websites. Advanced monitoring and validation mechanisms were implemented to ensure data quality and system stability. The result was a resilient, production-grade scraping platform with predictable performance and streamlined maintenance.

The system is built around a modular scraper architecture, where each website is handled by an independent, purpose-built scraper while sharing a common core. This approach allows new sources to be added quickly without affecting existing ones and keeps maintenance isolated and predictable. Shared utilities and standardized data outputs ensure consistency across all scrapers, even when dealing with highly diverse site structures and anti-bot measures.

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Modular Scraper Architecture

To maintain high data accuracy at scale, we implemented a multi-layer validation system that checks merchant identity, coupon relevance, and data consistency early in the pipeline. Automated mismatch detection prevents invalid or unrelated coupons from entering the database, while prioritized source logic improves merchant resolution. These safeguards significantly reduce noise in the dataset and ensure reliable, production-ready data.

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Data Quality & Validation System

A comprehensive monitoring framework continuously tracks scraper performance, data yield, and system health across the entire network. Automated alerts detect issues such as zero-yield scrapers, build failures, domain mismatches, and data anomalies in real time. Detailed, context-rich notifications allow the team to react quickly, minimize downtime, and keep the platform running reliably at scale.

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Monitoring & Alerting Framework

Discover how a large-scale, modular scraping platform was built to reliably collect and validate promotional data from 100+ websites across multiple countries and languages. By standardizing data pipelines, automating quality checks, and introducing advanced monitoring, the solution delivered millions of structured records with high accuracy while significantly reducing maintenance effort and operational risk.

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websites scraped across multiple locales and languages

100+

reduction in manual data validation efforts

of structured coupon records processed and stored reliably

Millions+

Lexis Solutions built a scalable web scraping platform processing millions of coupon records from 100+ sites, improving data quality and reducing operational overhead.

Enterprise Web Scraping Platform for Scalable, High-Quality Data

Scaling Global Web Scraping Infrastructure for High-Quality Coupon Data

In an increasingly competitive marketplace, businesses often struggle with managing online reviews across multiple platforms, such as Google, Yelp, and Facebook. Manual review management leads to missed opportunities, particularly with unfair negative reviews and under-leveraged positive reviews. ReviewHero set out to address these challenges with a data-driven approach, using AI to automate key processes like review collection, response generation, and market analysis.

The goal was to provide businesses with a tool that not only simplifies review management but also enhances customer satisfaction and increases sales through improved online visibility.

Lexis Solutions worked closely with ReviewHero to develop a cutting-edge, AI-powered platform that automates the entire review lifecycle. Key features include:

- __Rating Radar with AI Analytics__: Tracks reviews in real time across 20+ review portals and leverages AI to analyze sentiment trends and detect patterns, helping businesses stay on top of customer feedback
- __Review Collection__: A personalized campaign page automates the process of collecting reviews from customers via email, QR code, widgets, and NFC stickers, using machine learning to optimize engagement.
- __AI-Powered Commenting__: Our advanced AI automatically generates personalized, authentic responses to reviews with just one click. The system learns from each business’s preferred tone and customer engagement style, ensuring consistent and meaningful interactions.

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ReviewHero’s dashboard provides businesses with real-time, actionable insights into customer sentiment and feedback. By analyzing thousands of reviews, the platform surfaces trends and offers data-backed recommendations to help businesses refine their offerings and customer service strategies.

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Data-Driven Insights

ReviewHero uses data-driven automation to market positive reviews effortlessly. Businesses can showcase their best customer feedback using rating seals, widgets, and video testimonials, with AI ensuring the content stays updated and relevant. This feature boosts visibility and customer trust while requiring minimal manual intervention.


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Automated Review Marketing

Each business is equipped with a fully SEO-optimized profile page, which is constantly updated by AI to improve search engine rankings and maximize visibility. This digital “shop window” allows potential customers to discover businesses quickly, enhancing their online presence.

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SEO-Optimized Profiles

ReviewHero is building a one-of-a-kind online reputation management platform for SMBs in Germany that gathers customer review insights, boosts engagement, and automates engagement on popular platforms using AI.

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65M+

20M+

500+

Lexis Solutions built an AI-driven reputation management platform that automates review tracking, responses, and insights, helping SMBs boost visibility and engagement.

AI-Powered Online Reputation Platform Built for German SMBs

Nicolas Capri Avatar

ReviewHero creates the first AI-powered online reputation platform for Germany

FranceVerif was building an AI-driven purchase assistant designed to protect users from fraudulent e-commerce stores while helping them find the best prices and available discounts. During an important growth phase in early 2022, changes within the existing technical setup introduced delivery risks and uncertainty around key milestones. Investors needed to reduce dependency on a single development setup while ensuring the mobile application could continue to evolve without disruption.

Beyond team continuity, the project required deep technical expertise. The existing mobile application needed a full architectural overhaul, improved stability, and close coordination with multiple development partners. FranceVerif required a trusted technology partner capable of integrating seamlessly into an active project, taking ownership of mobile development, and ensuring high-quality delivery under tight timelines.


Lexis Solutions joined the project as a strategic technology partner, taking ownership of the mobile application while collaborating closely with FranceVerif’s internal and external development teams. Our team quickly adapted to the existing codebase, stabilized the architecture, and executed a comprehensive overhaul to improve performance, scalability, and maintainability.

By leading the mobile development effort and enforcing clear project management and quality assurance processes, we ensured predictable delivery and reduced technical risk. This approach allowed FranceVerif to continue building and refining its AI-powered features while meeting investor expectations and product milestones.


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Sure-T is an AI-based tool designed to identify fraudulent e-commerce websites, including near-identical copies of reputable stores. By analyzing multiple trust signals and behavioral patterns, the system helps users assess website reliability, avoid scams, and shop online with confidence.

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Sure-T – AI Scam Detection

Optimus Price automatically scans the web to compare prices for a specific product across multiple stores. Available via the mobile application and browser extension, it helps users quickly identify the best available offer without manual searching.

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Optimus Price – Intelligent Price Comparison

Robocash crawls the web to discover active vouchers and discount codes for supported stores. The tool enables users to apply discounts instantly at checkout, ensuring they never miss available savings.

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Robocash – Automated Discount Discovery

Learn how Lexis Solutions supported the development and scaling of an AI-driven purchase assistant that detects scam stores, compares prices, and surfaces discounts to help users shop safely online.

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627K

75K+

Lexis Solutions supported the development of an AI-driven purchase assistant that detects scam stores, compares prices, and automates discount discovery.

AI-Powered App Detecting Fraudulent E-Commerce Stores at Scale

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FranceVerif Builds AI-Powered Protection Against Fraudulent E-Commerce

__Pricerobot__ set out to build a real-time insurance comparison platform that enables users across Scandinavian countries to find the best car and home insurance deals on the market. The core challenge was extracting accurate pricing and product data from multiple insurance providers, each with unique business rules, technical implementations, and security protections.

The system had to operate under strict performance constraints, returning normalized and comparable insurance offers within approximately 2 minutes and 30 seconds per request. At the same time, provider websites frequently changed flows, introduced new security mechanisms, or applied aggressive anti-bot protections. The challenge was to design a flexible, resilient system that could adapt to these variations while maintaining speed, reliability, and data accuracy at scale.


Lexis Solutions designed and implemented a modular, adaptive web scraping and automation platform tailored specifically for large-scale insurance comparison. Each insurance provider was isolated into its own dedicated actor, ensuring changes or failures in one provider would not impact others. A centralized API layer orchestrates requests, validates inputs, applies provider-specific configurations, and aggregates results into a unified response.

The architecture was optimized iteratively. Initial browser automation ensured stability and fast delivery, while performance-critical providers were later migrated to direct HTTP-based implementations for speed. Advanced handling of security mechanisms, captcha-solving integrations, and dynamic proxy strategies ensured consistent execution within strict time limits. This approach resulted in a scalable, maintainable system capable of evolving alongside constantly changing insurance provider platforms.


Pricerobot hero

Each insurance provider is handled by a separate, isolated actor with a consistent processing flow. This modular design simplifies maintenance, enables independent optimization per provider, and prevents cascading failures when individual websites change their structure or protection mechanisms.

pricerobot key feature 1

Modular Provider Architecture

Depending on provider complexity, the system dynamically selects the most effective data retrieval method – ranging from direct HTTP requests and Cheerio-based parsing to full browser automation. This hybrid approach balances performance, stability, and development effort while meeting strict execution time requirements.

pricerobot key feature 2

Adaptive Scraping & Automation Strategy

A dedicated monitoring pipeline collects execution results from all actors, tracks failures, timeouts, and empty results, and sends real-time notifications to Slack. Historical execution data is stored and analyzed to identify recurring issues, website changes, and optimization opportunities, providing full transparency for both the development team and the client.

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Centralized Monitoring & Transparency

Learn how Lexis Solutions engineered a high-performance scraping and automation system that compares car and home insurance prices across Scandinavian providers in real time.

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insurance pricing and product checks executed reliably

average end-to-end execution time per comparison request

< 2.5 min

architecture supporting continuous onboarding of new providers

Scalable

Real-Time InsurTech Platform for Insurance Price Comparison

Powering Real-Time Insurance Price Comparison Across Scandinavia

After being acquired at the end of 2019, StaffSquared had already achieved strong product–market fit, but the platform faced growing usability and performance challenges. While it offered a wide range of features, many of them were underutilized, poorly documented, or difficult for customers to discover. Several core functionalities were unstable and suffered from performance issues, leading to frequent customer support requests and frustration among users.

The new phase of growth required stabilizing the platform, improving performance, and refining the most business-critical features. StaffSquared needed a technology partner capable of quickly understanding a complex product, prioritizing improvements, and delivering reliable enhancements without disrupting existing customers.


Lexis Solutions partnered closely with the StaffSquared team to take ownership of the technical improvements across web and mobile platforms. We assembled a cross-functional team tailored to the project’s needs, covering development, performance optimization, and design enhancements. Through clear communication and close collaboration with the CTO and non-technical stakeholders, we quickly aligned on priorities, timelines, and deliverables.

Our approach focused on stabilizing essential features, improving API performance, and refining the user experience for the most frequently used workflows. This allowed StaffSquared to regain customer trust, reduce support issues, and deliver a more reliable and scalable platform.


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The performance appraisal module was optimized to support structured employee evaluations, including goal tracking, feedback, and progress reviews. Improvements focused on usability and reliability, enabling managers to conduct reviews efficiently and maintain consistent performance records.


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Performance appraisal

We enhanced the time off management feature to allow organizations to define flexible leave policies, track balances, and automate approval workflows. The improved flow reduced manual effort and made absence tracking clearer for both employees and managers.

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Time оff management

The employee database was refined to centralize all essential employee information in one place, including personal details, employment data, and historical records. This enabled better visibility across teams and supported more informed, data-driven decision-making.

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Employee database

The time and attendance module was optimized to accurately track working hours, breaks, absences, and irregularities. Performance improvements ensured reliable data collection while giving organizations better oversight of employee availability and attendance patterns.


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Time & attendance management

Learn how Lexis Solutions stabilized critical features, improved UX, and optimized performance across web and mobile, resulting in faster workflows and higher customer satisfaction.

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100%

Lexis Solutions revamped key StaffSquared features, improving platform stability, UX, and API performance, resulting in faster workflows and higher customer satisfaction.


StaffSquared Platform Revamp Improving Performance & User Experience

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StaffSquared Revamps Core HR Platform to Boost Performance & Adoption

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Mindance was scaling a fast-growing digital mental health platform and needed to accelerate product development without compromising quality, performance, or user trust. As customer adoption increased and investor expectations grew, the platform required continuous improvements across mobile and web applications, along with the delivery of new, research-based features.

Having previously worked with external contributors who lacked long-term ownership, Mindance was looking for a technology partner that could fully align with their mission and product vision. The challenge was to find a team capable of taking responsibility for technical decisions, improving platform stability, and reliably delivering high-impact features within tight timelines.


Lexis Solutions partnered with Mindance as a trusted technology partner, working closely with their internal team to improve the stability, performance, and overall maturity of the platform. The initial focus was on resolving critical performance issues that were impacting customer experience, followed by continuous improvements across the mobile and web applications.

As the collaboration deepened, Lexis Solutions helped Mindance accelerate feature development while maintaining high quality and reliability. Together, we delivered new mental training programs focused on sleep quality, concentration, and mindfulness, as well as secure communication features connecting users with psychological experts. This hands-on, partnership-driven approach enabled Mindance to meet investor expectations, strengthen their product offering, and successfully prepare the platform for long-term growth and acquisition.


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Mindance offers scientifically grounded digital programs focused on relaxation, mindfulness, compassion, and mental resilience. The exercises are developed by certified psychological experts and designed to fit seamlessly into employees’ daily routines. By improving sleep quality, concentration, and stress management, the platform supports both personal well-being and workplace productivity.

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Digital programs

The platform supports interactive workshops delivered either on-site or online, led by certified coaches. Employees can easily register, participate, and receive follow-up materials through the Mindance app, ensuring continuity beyond the session itself. This blended-learning approach combines human interaction with digital support, increasing engagement and long-term impact.


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Interactive workshops

Discover how Lexis Solutions partnered with Mindance to improve platform performance, deliver new mental health features, and support a successful acquisition through strong technical execution.

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Mindance-app

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2021

Lexis Solutions partnered with Mindance to improve performance and deliver new mental health features, supporting growth and a successful acquisition.


Mindance Scales Digital Mental Health Platform for Workplace Wellbeing

Mindance Accelerates Product Growth and Achieves Successful Exit

balans.bg logo

2019 was the year Kreston BulMar - the most prominent Bulgarian accounting and consulting agency in the last 25+ years, went to create a unique platform for further sharing its vast knowledge. The critical part was managing the thousands of documents accumulated over the last three decades. Lexis Solutions rose to the challenge. An abundant administration with sophisticated document parsing algorithms and a responsive, SEO-optimized client application was created, offered, and successfully implemented.

Balans.bg has nearly 4 000 unique resources separated into 13 categories - calculators, articles, legal acts, videos, news, textbooks, etc. With such a rich database, the platform offers its users a solution for every business case.

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Each resource has connections to other resources, and their sum accounts for well over 10 000 links. By visualizing and organizing linked resources, users can seamlessly acquire a complete understanding of the issue and hand and solve their professional case.

Resources are interconnected

Kreston BulMar is the most prominent Bulgarian accounting and consulting agency, which, over the last 25+ years, has authored thousands of articles, analyses, commentaries, videos, handbooks, and a dozen textbooks. By 2019 they came to the idea of establishing a long-term partnership with an IT company to help them create a custom knowledge management platform to organize and monetize their professional expertise.

Unleashing the power of custom software