Precision AI Engineering for Real-World Systems

We work across computer vision tasks such as image classification and object detection, natural language processing including text classification and sequence modeling, tabular data analysis for structured datasets, time series forecasting, and graph-based problems. The focus is on supervised learning challenges where you have labeled data or can create it, though we also handle semi-supervised and transfer learning scenarios.

Architecture design engagements usually span two to four weeks depending on problem complexity and the number of candidate architectures to evaluate. This includes initial problem analysis, literature review, baseline model training, candidate architecture experimentation, ablation studies, and final documentation. We structure the work in phases so you can review preliminary results before committing to the full engagement.

For architecture work, you receive experiment notebooks with reproducible training procedures, architecture specifications in code, performance benchmarks comparing candidate designs, ablation study results, and documentation explaining design decisions. For integration consulting, you receive a structured recommendations document with implementation timelines, vendor comparison matrices, infrastructure sizing guidance, and change management considerations. OCR projects deliver trained models, deployment code, testing tools, and accuracy benchmarks on your specific document types.

For most architecture design work, we can operate on sample datasets and don't need production system access. Integration consulting may require system architecture diagrams and technical specifications but typically not direct system access. We're comfortable working under non-disclosure agreements and can structure data sharing to meet your security requirements. For OCR work, we need representative document samples but these can be anonymized versions.

We sign non-disclosure agreements as standard practice and can work within your existing data governance framework. Training and experimentation happens in isolated environments with no internet connectivity if required. We don't retain copies of your data after project completion unless explicitly agreed for ongoing support purposes. All code and documentation can be delivered through your preferred secure channels.

We structure engagements with checkpoints so you can evaluate preliminary results before committing to the full scope. If initial experiments suggest the target performance isn't achievable with current data or computational constraints, we document why and propose alternatives — this might mean collecting additional training data, adjusting the problem formulation, or accepting lower but still useful performance thresholds. Honest assessment of what's achievable is part of the service.

We offer post-project support arrangements on a retainer or hourly basis for questions about the delivered work, assistance with deployment issues, model retraining when new data becomes available, or extension of the work to related problems. The goal is knowledge transfer during the main engagement so your team can operate independently, but we're available when specialized expertise is needed.

We primarily work with PyTorch for deep learning implementations due to its flexibility and strong research community support. For traditional machine learning, we use scikit-learn. OCR work involves Tesseract, EasyOCR, or custom models depending on requirements. We're comfortable adapting to your existing technology stack if you have strong preferences — the underlying algorithms and architectures matter more than the specific framework.