Updates

We are improving onboarding, support, and operational readiness for our exam preparation platform, continuing the product direction we began exploring through our exam-prep materials and earlier validation work.

As part of this phase, we also released the closed beta of the platform and opened access at prep.gulintech.com for early users and validation.

This work helps create a smoother entry into the product, improves responsiveness to user needs, and strengthens the foundation for broader rollout.

At this stage, the focus is on making the exam preparation platform feel more mature across the full user experience.

We are continuing to improve how learners move through practice in our exam preparation platform while strengthening the way we gather feedback and improve the product.

Building on the earlier foundation and workflow work from February and early March, this phase has focused on making the experience feel smoother, more consistent, and easier to refine through faster iteration.

The result is a more polished exam preparation platform and a stronger feedback loop for future improvements.

We are expanding our exam preparation platform with more focused study workflows and stronger content operations behind the scenes.

After strengthening the technical foundation in late February, this work is designed to make learning more effective for users while giving our team better ways to manage, refine, and scale high-quality content.

At this stage, we are bringing the learning experience and content engine closer together to support a more connected exam preparation product.

Following our earlier exam preparation work, including the release of our GCP Professional Cloud Architect exam-prep book, we are now strengthening the foundation of our exam preparation platform so we can grow faster with confidence.

This phase has focused on improving reliability, raising quality standards, and building a more resilient product as we expand features and content.

The result is a stronger base for faster iteration and a more dependable exam preparation experience over time.

We released a new exam preparation book to support candidates preparing for the Google Cloud Professional Cloud Architect exam.

The book includes two sets of complex practice questions and covers all major areas required for the exam.

Based on the early interest in the book, we began assessing the opportunity to build a dedicated subscription-based exam preparation web application. That work later evolved into the exam preparation platform updates shared in 2026.

We have created two mobile test clients for our business assistant product: one for iOS and one for Android.

Both clients are currently in active testing and are not yet published on the Apple App Store or Google Play.

At this stage, we can share access with a limited number of selected customers for early validation and feedback.

We delivered a number of new features for our business assistant product.

In parallel, we updated the product map for the business assistant to include many additional use cases based on ongoing customer needs and validation insights.

This update gives us a stronger foundation for upcoming iterations and broader adoption across business workflows.

We launched our business assistant product with its first core functionality: correlating bank transactions with invoices or contracts.

This release is focused on helping teams reduce manual reconciliation effort and improve financial process clarity from day one.

We are currently working on a new value investing support assistant that uses U.S. SEC EDGAR 10-K filings as its core source material.

The solution combines structured 10-K parsing with complex Retrieval-Augmented Generation (RAG) techniques so analysts can explore company fundamentals with more context and traceable evidence.

The pipeline automatically downloads 10-K reports from EDGAR and uses a caching layer to speed up repeated analysis requests and improve processing efficiency.

The assistant helps users save time and effort in analysis, while the final investment decision is always made by a human.

At this stage, we are validating document ingestion quality, retrieval relevance, and answer grounding before broader release.

We launched a closed beta of our algorithmic trading performance transparency product.

This product focuses on a problem many investors face but rarely see addressed properly: the real profitability of algorithmic trading managed by external companies.

In many cases, external traders or signal providers are incentivized to artificially boost reported performance by highlighting winning trades while ignoring transfers, capital changes, or hidden losses. As a result, reported performance can look excellent, while actual investment performance remains far less impressive.

Our solution connects to a trading account through a Read-Only (RO) API key, analyzes complete account activity, and provides a clear, unbiased report of true performance.

Instead of relying on selective performance highlights, we evaluate the full account history so investors can see the real picture across traditional and digital assets.

If you are working with external traders, funds, or signal providers and want to understand how your portfolio is really performing, this solution is for you.

Interested in testing the closed beta? Please contact us directly, and we will be happy to share more details.

At the beginning of April, we introduced a Telegram bot for our trading account audit product to support end users with algorithmic trading questions and operational tasks.

The bot is now integrated with our backend and helps streamline day-to-day solution operations with faster user guidance and more consistent execution support.

This addition improves accessibility for users who need quick assistance while maintaining a reliable connection to the core trading workflow.

We released a Telegram bot for our trading account audit product that allows users to run account audits directly from a chat interface.

The bot connects to broker and exchange accounts through Read-Only API keys, retrieves full trading history, and generates key performance metrics including Sharpe ratio, Sortino ratio, and risk-reward analysis.

This makes it easier for users to request and receive account performance reports without needing access to a web dashboard or desktop environment.

We completed end-to-end testing of our trading account audit product across multiple exchange accounts and trading scenarios.

Testing covered a wide range of account types, trading patterns, and edge cases to ensure accuracy and reliability of performance metrics before the planned closed beta launch.

In parallel, we began preparing the Telegram bot interface to make audit functionality accessible directly from a chat environment.

We completed the reporting layer for the trading account audit solution, producing structured performance reports that clearly present key findings to end users.

Reports now include visual summaries of portfolio performance over time, risk-adjusted return metrics, drawdown analysis, and benchmark comparisons, presented in a format accessible to non-technical investors.

We added benchmark comparison capabilities to the trading account audit solution, allowing users to compare their managed account performance against simple buy-and-hold strategies for the same assets and time period.

This feature highlights whether active management is adding value compared to passive holding, which is one of the most important questions for investors evaluating external traders.

We completed the implementation of a comprehensive risk metrics engine for the trading account audit solution.

The engine calculates key portfolio performance indicators including Sharpe ratio, Sortino ratio, risk-reward ratio, maximum drawdown, and win rate across configurable time windows.

We expanded exchange coverage and improved the reliability of our trading account audit data pipeline.

The solution now handles edge cases in trade history more accurately, including partial fills, internal transfers between sub-accounts, and fee calculations across different exchange fee structures.

We also improved how capital inflows and outflows are tracked, ensuring that performance metrics reflect actual investment results rather than gross trading volume.

We built a data pipeline to normalize trade history from digital asset exchange APIs into a unified internal format.

Different exchanges report trades, deposits, withdrawals, and transfers in significantly different structures and conventions. Our pipeline handles these differences and produces a consistent dataset suitable for accurate performance analysis.

This step was essential to ensure that audit results are comparable across exchanges and account types.

We completed the initial integration with major digital asset exchange APIs for account data collection using Read-Only API keys.

The integration supports retrieving full trade history, balance snapshots, deposit and withdrawal records, and fee information. All connections use Read-Only access, ensuring no trading or fund movement is possible through the audit process.

We completed the requirements analysis and system architecture design for the trading account audit solution.

The goal was defined: provide investors with an unbiased, transparent view of their managed trading account performance by analyzing full account activity rather than relying on selective reporting from external traders.

We designed the system architecture to support secure API connectivity, scalable data processing, and modular metric calculation.

We developed an initial proof of concept for the trading account audit tool, validating the core idea of connecting to exchange accounts via Read-Only API keys and computing basic performance metrics.

The prototype confirmed that sufficient data is available through standard exchange APIs to calculate meaningful risk-adjusted return indicators and detect discrepancies between reported and actual performance.

We conducted research into established portfolio performance measurement methodologies and their applicability to algorithmic trading accounts.

The research covered risk-adjusted return metrics commonly used in traditional finance, including Sharpe ratio, Sortino ratio, Calmar ratio, and maximum drawdown analysis, and evaluated how these apply across traditional and digital asset markets with different liquidity and trading-hour profiles.

This research informed the design decisions for our trading account audit solution.

We identified and analyzed a recurring problem in managed algorithmic trading: the lack of transparent, independent performance reporting.

Many external traders, signal providers, and fund managers report performance selectively, highlighting winning trades while omitting capital changes, fees, or losing periods. This creates a significant gap between reported and actual investment performance.

We began exploring how an independent audit tool could address this transparency gap for investors across traditional and digital assets.

We began exploring opportunities in trading account analytics, focusing on the growing need for reliable performance tracking and verification tools.

Initial work included evaluating available broker and exchange APIs, understanding data access patterns, and assessing the feasibility of building automated account analysis capabilities.

This exploration laid the groundwork for the trading account audit solution developed in the following years.

We are pleased to announce the launch of a closed beta of our visualization platform designed for construction companies building residential properties and housing projects.

Our solution enables teams to generate high-quality exterior and interior visualizations before construction begins, based on real materials, architectural layouts, and planning documentation.

We are currently inviting a limited number of construction companies, property developers, and real estate teams to participate in the beta program.

If you are interested in early access, please contact us directly to learn more.