Our work
Anonymized real client work.
Retrieval-augmented generation over private documents, images and voice
Consolidated real-time analytics across multiple broker statements
A real-time, extensible trading game
Scalable image-generation pipeline for NFT collections
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The pipeline cuts manual image-creation effort by about 80%, delivering consistent, brand-aligned assets in minutes while maintaining full data sovereignty.
The client needed a fast, repeatable process to turn a single source image into a set of branded social-media assets with varied angles, while keeping all validation and data handling inside a secure, locally-controlled environment.
We engineered an n8n-based orchestration that stitches together local LLM validation, cloud vision models, and a multi-model fallback chain, delivering fully-versioned images and a streamlined human-in-the-loop QA interface.
Retrieval-augmented generation over private documents, images and voice
Delivered sub-second query performance on confidential enterprise data, eliminating cloud-based latency and ensuring full data residency.
The client needed a fast, secure AI assistant that could answer queries over proprietary documents, images, and voice inputs without exposing data to external services.
We deployed a local GPU-accelerated pipeline that combines TensorRT-LLM for inference, LlamaIndex for data ingestion, and FAISS for vector search, orchestrated by an agentic workflow that routes voice, text, and image queries to the appropriate model.
Consolidated real-time analytics across multiple broker statements
Delivered a turnkey, on-device analytics platform that cut manual data aggregation by dozens of hours per month and gave investors transparent, up-to-date performance and risk insights.
The client needed a single, automated view of holdings across several broker platforms, eliminating manual data stitching and inconsistent reporting. They also required detailed performance, risk, and tax metrics without exposing raw data to external services.
We built a local Python engine that parses CSV and XLSX statements, normalizes transactions, fetches live prices via Yahoo Finance and CoinGecko, and generates a responsive HTML dashboard. The architecture runs entirely on-premises, using a modular, agentic workflow for parsing, price lookup, and KPI calculation.
Cut detection latency by over 80%, enabling clients to capture short-squeeze opportunities and protect assets in high-volatility markets.
A financial analytics client needed early, reliable identification of market-driven short-squeeze events before price moves, but their existing tools lacked integrated sentiment and technical data streams.
We delivered a secure, locally-hosted pipeline that continuously harvests social-media, news, and market metrics, applies advanced NLP, proprietary weighting, and adaptive ML models, and surfaces risk scores through an agentic workflow and live dashboard.
A real-time, extensible trading game
Accelerated the client's time-to-market by delivering a stable, extensible simulation platform that scales reliably across player sessions.
The client needed a high-performance, locally runnable stock-market simulator that could be extended with custom news events and support multiple concurrent players, while maintaining security and reproducibility.
We delivered a modular Python engine packaged in an isolated virtual environment, with a thread-safe game loop, a pluggable CSV-based news manager, and a CI-integrated container for reproducible deployment.
The client now streams personal videos instantly, reduces content-sharing overhead to a single click, and keeps full control over privacy and branding.
A client needed a simple, secure way to share personal video content with a small, trusted audience without relying on public platforms or complex infrastructure. They also wanted an effortless way to add new episodes with minimal technical effort.
We delivered a lightweight, locally-hosted web application that serves video metadata from a static site and streams content directly from Google Drive. A one-click refresh script pulls the latest episode data via git and launches a Python HTTP server, keeping the platform up to date without manual deployment.
Reduced development iteration time by roughly 30% and enabled rapid prototyping of character movement for downstream game features.
The project required an intuitive, responsive control system for a 3D frog avatar that could handle player input, maintain smooth orientation, and integrate cleanly with Unity's CharacterController without performance penalties.
We delivered a lightweight C# component that maps Unity input axes to movement vectors, automatically aligns the frog's forward direction with its travel path, and drives the CharacterController using a configurable speed parameter. It ships with an editor-friendly Readme asset that auto-loads a custom layout, streamlining developer onboarding.
Reduced subtitle creation time by over 70% while keeping all media and processing strictly on-premises.
Video editors needed rapid, accurate subtitles for large volumes of footage, but manual transcription was labor-intensive and cloud-based services risked exposing proprietary content.
We built a local, agentic pipeline that extracts audio from the machine, runs Whisper inference on-premises via a lightweight HTTP server, and writes time-coded captions directly back into Resolve's timeline.
Scalable image-generation pipeline for NFT collections
Enabled generation of thousands of unique NFTs per run, cutting manual design effort by over 80% and accelerating launch timelines.
The client needed a fast, reproducible way to create large editions of unique, rarity-weighted NFT artwork without manual composition. Existing tools required hand-crafting each image and gave no programmatic control over layer ordering or rarity distribution.
We delivered a Node.js engine that uses the Canvas API to compose layered assets according to a declarative config.js. The system tracks DNA for uniqueness, applies configurable rarity weights, and emits ready-to-mint images plus ERC-721-compatible metadata in a single run.
Reduced manual support workload by about 70% and provided instant, automated booking 24/7 while keeping all data on the client's own infrastructure.
The client needed a 24/7, brandable web assistant that could field service inquiries and schedule appointments without relying on external SaaS platforms or exposing sensitive data.
We built a self-hosted, agentic chat widget that routes messages through a secure n8n webhook, runs local LLM inference for retrieval-augmented generation, and triggers automated booking workflows via a declarative pipeline.
Replaced a manual, error-prone calculation process with a one-click pipeline that produces accurate, category-split result listings and record verification in seconds instead of over two hours, entirely on low-tier local infrastructure.
A national masters athletics federation needed to convert raw timing-system exports into official, age-graded result listings. The process was previously done by hand against published coefficient tables and record books, and it had to be operable by non-technical meet staff on a local machine with zero cloud dependency.
We deployed a fully local Python pipeline that parses the official WMA coefficient tables and national record books directly from source PDFs, joins each athlete against the federation's member database for an authoritative date of birth, and applies the governing body's competition rules to generate print-ready result listings. All logic runs offline behind a single launcher, keeping athletes' personal data on the client's own hardware. After each race or discipline an LLM verifies whether the calculations and ratios meet the requirements; if not, it flags the anomaly, the process is adjusted, and it runs again.