Advance Humanity, Not Replace It.

Theseus engineered a cutting-edge software solution that leverages the OpenAI API to seamlessly integrate with critical platforms like Confluence, Jira, and GitHub. This package automates the transfer and synthesis of information across these platforms, significantly speeding up code comprehension, deepening clarity in documentation, and easing the onboarding process for new team members. By harnessing advanced AI capabilities, the project allowed engineers to focus on higher-level tasks while the system efficiently streamlined routine activities and enhanced strategic decision-making.

Theseus created easy-to-use Splunk dashboards to help an investment management firm track and understand the activity of their software development tools on AWS and Azure. These dashboards used detailed logs from Bamboo, organized and labeled, to show real-time updates on any errors that happened during the process of building and launching software. The dashboards made it simple for teams to see how often problems occurred, how serious they were, and how long different steps of the process took. This gave the firm a clear picture of their projects and helped them quickly spot and fix issues.

Theseus helped a $200 billion investment firm evaluate cloud analytics platforms—BigQuery, Snowflake, and Redshift—to determine the best fit for their extensive data needs. The team conducted rigorous load-testing, assessing scalability, efficiency, and maintenance. Migration feasibility was analyzed, ensuring compatibility with Splunk, Power BI, Aladdin, LDAP, and database schemas. The study also covered database auto-shutdown, industry trends, and integration ease. This analysis empowered the client to optimize performance, scalability, and cost-efficiency.

In early 2024, Theseus designed and implemented a feature in a web portal used to manage logistics and research history related to financial securities. The project involved creating a file upload functionality that allowed analysts to send financial modeling calculations to an application hosted in a Docker container. The application, using its IAM permissions, posted the files to an AWS S3 bucket. Once uploaded, an AWS Lambda function performed quality checks on the data, ensuring proper structure and error screening by comparing calculations across the firm.

Theseus helped LMI optimize data management with Collibra. After a Proof of Concept, the team customized Collibra to fit LMI's needs, creating custom agents and automating deployment on EC2 using Ansible. They carefully planned task handling to ensure seamless integration. This improved data organization, governance, and tracking, making information easier to manage and use. The project enhanced efficiency, reliability, and scalability, enabling LMI to streamline operations and optimize long-term data management.

Theseus developed a voice-to-voice AI conversation system to pitch products to simulated personas and receive real-time feedback. The system adapted the Inworld API, typically used for creating video game avatars, to generate distinct personas that subtly display different temperaments. For transcription, the app utilized a quantized version of the OpenAI Whisper model for real-time audio transcription, ensuring the AI's response rate mirrored human conversation speeds. To assess performance, the system relied on Salesforce Einstein to yield a conversation review, which was then manipulated to create a scorecard that evaluated the user's behavior

Theseus documented key LMI Architecture components, focusing on data pipelines, Snowflake integration, and Control-M job orchestration. Guides detailed secure SA pipelines, Snowflake setup, and Control-M automation standards for scheduling, monitoring, and error handling. Security documentation covered access controls, encryption, and data integrity. These resources ensure system security, consistency, and maintainability.

Theseus strengthened security across two data integration platforms, Snowflake and SnapLogic. For Snowflake, the team implemented fine-grained access controls, encryption for data at rest and in transit, and regular compliance audits. In SnapLogic, they introduced role-based access controls, secured API connections, and real-time threat monitoring. Automated security assessments were deployed to continuously evaluate and enhance protection, ensuring a resilient and secure data infrastructure.

Theseus improved a financial investment firm's Earnings Survey reporting by integrating an auto-refresh feature in PowerBI. The project started with a Microsoft Forms survey collecting analysts' opinions on sector events. Data was sent to Snowflake via Power Automate, then sourced into a PowerBI dataset for a 10-page analysis report. The report displayed current results and quarterly changes. Auto-refresh kept data updated in real time, eliminating manual updates and enhancing decision-making.

Theseus set a front-end standard at an investment bank to eliminate unnecessary, costly frameworks. The bank's unconventional tech growth led to integration challenges and inefficiencies from uncoordinated software leadership. By uniting UX, product, architecture, and front-end teams, Theseus created a unified approach to tech decisions. This strategic coordination mitigated risks, enabling a scalable ecosystem that boosted code reusability and strengthened cross-team collaboration for future application development.

Theseus developed the technical strategy for a tokenized real-estate platform built around ChainUp and project-level SPVs. The framework covered deed transfer into the SPV, token issuance, fractional ownership tracking, smart-contract-based distributions, investor governance, and transparent record keeping. The structure preserves legal ownership within the SPV while allowing tokens to represent economic rights tied to rental income, appreciation, sale proceeds, and investor protections.

The masterclass implemented a complete fraud-detection pipeline using Python, Pandas, scikit-learn, OneHotEncoder, and Isolation Forest. Participants learned data validation, preprocessing, exploratory analysis, anomaly detection, and conversion of model outputs into interpretable 0-100 risk scores using configurable thresholds. Google Colab notebooks and GitHub resources enabled attendees to experiment with the workflow using their own transaction datasets.

Theseus validated the infrastructure strategy, coordinated technical planning, and supported partner alignment for a scalable enterprise data center. The architecture included high-density power planning, redundant power paths, generator backup, containment cooling, engineered airflow, dual fiber connectivity, resilient networking, biometric access control, CCTV monitoring, VESDA fire detection, and FM200 suppression to support secure, highly available operations.

MathPals is implemented as a Next.js application with student management, authentication, file management, AI homework analysis, exam review, answer correction, and responsive tutor dashboards. Gemini Flash and Genkit power homework analysis, topic detection, lesson generation, and personalized practice, while React, Tailwind CSS, ShadCN UI, and Lucide provide an intuitive experience designed specifically for tutors managing students, homework, exams, files, and notes.

Learnable uses a layered, event-driven architecture supporting multimodal ingestion across PDF, image, audio, and text. The platform combines OCR, speech recognition, standards mapping, personalized lesson generation, Socratic guidance, risk scoring, model routing, telemetry, REST/LTI integration, role-based access, consent workflows, and FERPA/COPPA-inspired privacy practices to create scalable, AI-driven educational decision support.

Tome combines LLM planning with structured validation and connected productivity platforms. The system enriches selected inputs from Todoist, Google Calendar, and Google Drive, grounds every activity in real source data, applies anti-hallucination safeguards, validates outputs through a strict multi-layer planning schema, performs automatic consistency checks, and assigns plan-health scores before presenting the final daily execution plan.

Siloët combines narrative parsing, emotional classification, social-context analysis, audience projection, and evaluator-based scoring within a unified AI workflow. The platform generates and evaluates candidate responses, measures qualities such as smoothness, humor, emotional charge, specificity, and naturalness, then visualizes the results through interactive emotional-analysis dashboards that reveal communication strengths, weaknesses, and audience impact over time.

Lucy combines Azure OpenAI, secure storage, AWS CDK infrastructure, CloudWatch monitoring, Langfuse-based evaluation, CI/CD pipelines, and AI output validation into a structured product-development workflow. The platform connects research, prompt engineering, implementation evidence, and automated testing to help teams move from early concepts through validated MVPs and into production-ready product direction with greater confidence.