Computer Vision Parking Occupancy Detection
Computer vision reads an entire deck in real time, replacing gate estimates with live occupancy by level and row for operators and drivers.
Applied AI Solutions and Engineering Services
We develop and integrate machine learning, computer vision, language, retrieval, and automation systems for defined business and product requirements.
Challenges We Solve
We address the data, evaluation, integration, security, deployment, and operating requirements that determine whether an AI system can be used in production.
Models That Do Not Perform in Production
We evaluate models using representative operational data, edge cases, latency requirements, resource limits, and defined acceptance criteria.
Insufficient or Poor-Quality Data
A feasibility assessment reviews data quantity, quality, coverage, labeling, access, privacy, and collection requirements before full development begins.
No Process for Reviewing Incorrect Results
We define confidence thresholds, exception handling, human review, corrections, and feedback processes according to the cost of an error.
Undefined Performance Requirements
Success criteria are documented in operational terms, including accuracy, error types, latency, cost, availability, and required human oversight.
Data Privacy and Security Requirements
Data access, storage, retention, third-party services, model providers, user permissions, and audit requirements are defined before implementation.
Our AI Services
Services are selected according to the use case, available data, required performance, deployment environment, and level of human oversight.
- Machine Learning SolutionsCustom prediction, classification, anomaly-detection, ranking, and automation models developed and evaluated using client or application-specific data.
- AI IntegrationIntegration of models and AI services with existing applications, databases, APIs, permissions, business rules, user interfaces, and human-review workflows.
- Computer VisionComputer vision systems for detection, classification, tracking, counting, inspection, measurement, and site monitoring using representative image or video data.
- Natural Language ProcessingText classification, information extraction, search, summarization, routing, retrieval, and assistants grounded in approved documents and data sources.
- Deep Learning ApplicationsDeep-learning systems for image recognition, signal processing, sequence analysis, anomaly detection, and other applications requiring learned representations.
- Deployment and MonitoringCloud, private-infrastructure, and edge deployment with versioning, monitoring, logging, performance review, and documented retraining procedures.
Benefits of Applied AI
Applied AI systems require representative evaluation, appropriate controls, reliable integration, and continuing performance review.
Evaluation on Representative Data
Models are tested against relevant examples, operating conditions, edge cases, and agreed performance requirements.
Appropriate Human Oversight
Review and approval steps are included where an incorrect result creates financial, operational, safety, or customer risk.
Flexible Deployment
Inference can run through cloud services, client-managed infrastructure, private environments, or edge hardware according to requirements.
Production Monitoring
Monitoring tracks model quality, latency, failures, data changes, and other indicators that may require investigation or retraining.
Early Feasibility Validation
A focused assessment can confirm whether available data and current technology support the proposed use case before a full build.
Application and Workflow Integration
Models are integrated with the interfaces, APIs, databases, hardware, permissions, and review processes required for operational use.
How We Work
The delivery process covers feasibility, data preparation, model development, evaluation, integration, deployment, monitoring, and handover.
Data Strategy and Preparation
Define the use case, users, workflow, acceptance criteria, data requirements, privacy constraints, security requirements, deployment environment, and feasibility risks.
Model Development and Training
Prepare data, establish evaluation sets, develop and compare approaches, tune the selected model, and document performance against approved criteria.
Integration and Deployment
Integrate the model with applications and workflows, implement validation and human-review controls, complete testing, and deploy to the approved environment.
Monitoring and Improvement
Monitor production performance, investigate failures and data changes, update evaluation sets, retrain when required, and maintain version and change records.
AI Applications
Common applications include computer vision, document processing, forecasting, knowledge retrieval, and workflow automation.
Visual Inspection and Counting
Object detection, classification, tracking, counting, defect inspection, and event detection using fixed or mobile cameras.
Occupancy and Site Monitoring
Computer vision systems for occupancy, utilization, movement, queue, and site-condition monitoring.
Document and Text Processing
Document extraction, classification, summarization, routing, structured data capture, and review workflows.
Forecasting and Prediction
Predictive models for demand, maintenance, scheduling, risk, capacity, anomaly detection, and operational planning.
Knowledge and Retrieval Systems
Search and question-answering systems grounded in approved internal documents, records, and knowledge sources.
Workflow Automation and Agents
AI-assisted workflows that retrieve information, prepare outputs, use approved tools, and route exceptions for human review.
Why Work With Us
Our AI work combines feasibility analysis, model engineering, conventional software controls, product integration, and defined data handling.
Use-Case and Feasibility Review
We determine whether AI is appropriate and compare it with conventional software, process changes, and other technical options.
Testable Controls Around Model Outputs
Validation, permissions, business rules, approval requirements, and irreversible actions are implemented in conventional software that can be tested.
Complete Product Engineering
Our software and hardware teams can build the application, interface, data pipeline, infrastructure, and edge system associated with the model.
Defined Data Handling
Project agreements and technical documentation specify how client data is accessed, stored, processed, retained, and shared with approved service providers.
Engagement Models
Begin with a feasibility assessment, a milestone-based build and deployment program, or ongoing support for an existing AI system.
Feasibility Assessment
Review of the use case, workflow, available data, technical options, evaluation requirements, deployment constraints, and risks, followed by a recommendation and implementation estimate.
Best before funding a pilot or production implementation.
Build and Deploy
Data preparation, model development, evaluation, application integration, deployment, documentation, and handover managed through defined milestones.
Best when the use case is defined and the required data is available or can be collected.
Ongoing Model Support
Production monitoring, incident analysis, evaluation updates, retraining, model releases, and performance reporting for deployed AI systems.
Best for AI systems that require continuing performance review and updates.
Common Questions
Mostly about data, confidentiality, and what happens the first time the model gets something wrong.
How do we know if our data is good enough?
A feasibility assessment reviews data volume, quality, coverage, labeling, access, privacy, and relevance to the intended use case. The result may support immediate development, identify a required data-collection period, or recommend a different technical approach.
Will you use our data to train models for anyone else?
No. Client data is used only for the approved client project. The project documentation identifies any third-party model, hosting, labeling, or data-processing services and defines the applicable access, retention, and confidentiality requirements.
Do we actually need AI for this?
A feasibility assessment compares AI with conventional software, integration, analytics, and process changes. We recommend AI only when it provides a meaningful advantage for the defined requirement.
What happens when the model gets something wrong?
The system is designed around expected error conditions. Depending on the use case, controls may include confidence thresholds, validation rules, exception handling, human review, approval requirements, audit records, and restrictions on irreversible actions.
Selected Work
Case studies covering the use case, system architecture, evaluation requirements, implementation, and operational result.
Our Own ProductAI Agents for PCB Specification and Review
Conductor selects components, reads the datasheets, validates the electrical contracts, and returns a bill of materials and a schematic that can be built.
AI is one of four service areas
Sunlab coordinates hardware, software, AI, and marketing within one delivery team. This reduces handoff delays, clarifies responsibility for cross-disciplinary decisions, and provides one point of contact.
HardwareProduction-ready electronics and connected products, from architecture and PCB design through prototype, validation, and manufacturing handoff.View the ServiceSoftwareCustom web, mobile, platform, and internal software developed around defined business and user requirements.View the ServiceMarketingMarket research, positioning, websites, search, content, campaigns, and analytics for technical products and services.View the Service
Discuss an Applied AI Requirement
Send us the intended workflow, available data, required outputs, performance requirements, deployment environment, and current technical constraints. We will recommend an appropriate assessment or implementation phase.
hello@sunlabdigital.comSend us the detailsSt. Petersburg, Florida · we work with teams anywhere