Technical Services

Docsaid is a deep learning studio specializing in model development and long-term maintenance.

We transform real-world business needs into maintainable, deployable, and evolvable AI model modules—focusing on the quality and reliability of the models themselves.

Through a small, dedicated engineering team and a stable delivery workflow, we work side-by-side with your team—integrating into your existing systems and processes, filling resource gaps, and supporting model deployment and continuous optimization.

Note: Our frontend/backend work serves as lightweight support for model demo, evaluation, and integration—it is not our primary service offering.

Why Work With Us?​

• Model-Centric Focus – Task-oriented performance goals (mAP, F1, TPR@FPR, Latency, etc.) ensure time and resources are invested in model quality and stability.
• Fast Start, Low Risk – No team expansion required. We provide ready-to-use scaffolds for data governance, experiment tracking, and benchmarking.
• Embedded Collaboration – We integrate with your existing data, product, and engineering teams, reusing your toolchain and minimizing disruption.
• Measurable Results – Benchmark dashboards with PR curves, confusion matrices, and multi-model/data comparisons.
• Inference Optimization – ONNX/TensorRT, quantization, and latency budget management; supports on-premise or private deployment with monitoring and replay analysis.
• Security & Compliance – NDA-ready, PII masking, full data and experiment traceability meeting audit requirements.
• Transparent & Reversible – Clear iteration goals, change logs, and rollback strategies.

Flagship Projects

• DocAligner – Builds metric systems, data versioning, and visualization reports, making every iteration quantifiable, traceable, and explainable.
• MRZScanner – An end-to-end pipeline covering preprocessing, localization, recognition, and verification, emphasizing practical balance between accuracy and latency.

Our Expertise​

A. Document Understanding Models​

• A1 Text Detection – Locating document/scene text regions, rotation correction, and noise suppression.
• A2 Text Recognition – OCR for Chinese, English, numeric, and special domains (including MRZ); with error correction and dictionary constraints.
• A3 Layout Understanding – Block classification, hierarchical parsing, table extraction, and key–value pair parsing (Form/Invoice/ID).
• A4 Document Alignment – Multi-template matching, geometric/semantic alignment, and quality measurement (based on the DocAligner methodology).

B. Object Detection Models​

• B1 Training Pipelines – Data governance/annotation workflows, augmentation strategies, and experiment tracking (mAP, F1, Latency).
• B2 Framework Re-engineering – Experience modularizing frameworks like Ultralytics into project-specific components (custom Head/Loss/Augmentation/Training commands).
• B3 Inference Optimization – ONNX/TensorRT, quantization, batch and stream inference.

C. Face-Related Models​

• C1 Face Detection – Robust multi-face and small-face detection under pose variations.
• C2 Landmark Localization – 5/68+ points for alignment and pose estimation (roll/pitch/yaw).
• C3 Face Recognition – Feature embedding, database management, threshold calibration, and deduplication.
• C4 Liveness Detection – Static/dynamic anti-spoofing; extensible to RGB/IR.

Inference, Evaluation & Lightweight Tooling​

• Deployment & Inference

  • E1 Model packaging, CI/CD, versioning, rollback, and regression testing workflows.
  • E2 ONNX deployment: graph optimization, operator alignment, throughput/latency trade-offs, and offline batch processing.
  • E3 C++ inference engine: on-prem acceleration, resource monitoring, memory and queue management (optimized for air-gapped networks).

• Benchmarking & Visualization

  • F1 PR curves, confusion matrices, accuracy–speed surfaces, and cross-version comparisons (multi-model, multi-dataset).
  • F2 Periodic reports and milestone reviews for internal discussions and decision-making.

All these components are designed to serve the model, enabling easier testing, demonstration, and integration, not large-scale frontend/backend development.

Collaboration Models​

• Model Module Maintenance – Focused on a single model module, delivering improvements periodically (metric gains, performance optimization, data updates).
• Short-Term Project Engagements – Targeted tasks such as tracking module integration, inference optimization, or benchmark system design—clear goals, controllable timelines.
• Long-Term Advisory Support – Embedding best practices and methodologies into your internal team to build sustainable in-house capability.

Already have a model or dataset? We can take over maintenance and establish benchmarks. Still exploring? We’ll help you quickly build a quantifiable starting line before scaling steadily.

Collaboration Process​

1. Requirement Discussion – Clarify business objectives, current status, and constraints (approx. 30–60 mins).
2. Proposal – Present a breakdown, timeline, and risk assessment; define deliverables and success metrics.
3. Iterative Execution – Work in 1–2 week sprints, continuously delivering results and difference reports.
4. Acceptance & Handover – Once targets are achieved, hand over code, documentation, and deployment scripts; maintenance renewals available.

Service Overview​

Below is a quick summary of our main services—you can select the items most relevant to your needs:

Frequently Asked Questions​

When We’re a Good Fit (or Not)​

• ✅ Good fit: You need production-ready models, value long-term maintenance and versioning, and prefer working directly with a small, specialized team.
• ⚠️ Not a good fit: You need rapid large-scale manpower deployment, or plan to train large language models (LLMs) requiring massive cloud compute.

Contact​

To begin collaboration:

1. Submit the requirement form – Whether it’s optimizing an existing model, building a new workflow, or assessing feasibility, feel free to reach out.
2. Follow-up discussion – You’ll receive a reply within 1–2 business days; we may schedule a short call if needed.
3. Project kickoff – Once both sides confirm the scope and deliverables, the engagement officially begins.

• 📮 Email: docsaidlab@gmail.com
• 🌐 Technical articles & project records: https://docsaid.org

Collaboration Form​

Additional Notes​

• For LLM / RAG / Chatbot NLP projects: we can provide preliminary technical consulting and system evaluations. However, due to compute constraints, we do not offer full-scale LLM training or large-scale language model development.
• For non-local (non-Taiwan) or non-English markets: timelines may vary due to time zone, NDA, and compliance considerations—please contact us for discussion.