Pebblely
An AI platform designed that takes care of product photography for online businesses.
COMPUTER VISION & IMAGE RECOGNITION
AI Image Recognition Software Built to Scale
Custom vision models trained on your data, not stock photography, tuned to the accuracy thresholds your business actually needs, and monitored after launch instead of handed off and forgotten.
500+
Projects Delivered
98%
Client Retention
ISO 27001
SOC 2
HIPAA
GDPR
Trusted by industry leaders
Certifications and Accreditations
Core AI Image Recognition Software Capabilities
Custom Vision Model Development
Deep learning models trained on your labeled data, not generic pretrained sets, tuned to the specific defects, mismatches, or objects your business needs flagged. Fine-tuning existing foundation models cuts initial data requirements when the use case allows it.
Real-Time Detection and Edge Inference
Low-latency object recognition optimized for the environment it runs in, whether that means a GPU cluster processing millions of images a day or a quantized model running on a factory-floor edge device with no reliable cloud connection.
OCR and Document Intelligence
Optical character recognition tuned for the documents you actually process, structured claim forms, handwritten notes, scanned contracts, turning image data into usable structured text instead of a flat transcript.
Enterprise System Integration
API-first architecture that connects vision output to the systems already running your business, ERP, CRM, EHR, or a custom internal platform, with secure data handling built in from the first integration point.
Not Sure Where to Start?
A short technical scoping call tells you whether a custom model, a fine-tuned foundation model, or an off-the-shelf API is the right fit before you commit budget.
Book a Scoping CallWhy Off-the-Shelf Vision APIs Break Down at Scale
A pretrained vision API classifies generic objects well in a demo. It has no idea what a hairline weld crack looks like on your production line, what a mislabeled carton looks like next to a correctly labeled one, or what "normal" even means for the specific images your business generates every day. That gap does not show up in a sales demo. It shows up three months into production when the false positive rate makes the system more work than the manual process it replaced. Most vendors sell computer vision development services as a single deliverable: a model, handed over, done. That framing skips the part where accuracy actually gets built.
Domain-specific accuracy is not a data problem you solve by feeding a generic API more images. It is an architecture decision made before training starts, about which model family fits the task, how much labeled ground truth is realistically available, and whether the answer is a model trained from scratch or a foundation model fine-tuned against your data. A medical image analysis use case and a retail shelf-monitoring use case need different answers to that question, even though both get marketed as "AI image recognition" by the same vendors.
Domain-specific accuracy is not a data problem you solve by feeding a generic API more images. It is an architecture decision made before training starts, about which model family fits the task, how much labeled ground truth is realistically available, and whether the answer is a model trained from scratch or a foundation model fine-tuned against your data. A medical image analysis use case and a retail shelf-monitoring use case need different answers to that question, even though both get marketed as "AI image recognition" by the same vendors.
Generic accuracy, not domain accuracy
Pretrained APIs are validated against public benchmarks, not the specific visual variance in your environment, lighting, angle, product mix, or defect type.
No control over model versioning
Third-party API updates can silently shift model behavior mid-production with no changelog you control.
Vendor lock-in on the inference layer
Switching providers later means re-validating accuracy from zero, not a config change.
No path to edge deployment
Cloud-only APIs cannot run where the images are captured, which rules out real-time use cases with connectivity constraints.
WHERE VISION SYSTEMS EARN THEIR ROI
Industries Running AI Image Recognition Software in Production
Healthcare
Retail and E-commerce
Manufacturing
Logistics and Supply Chain
How AI Image Recognition Software Gets Built and Kept Accurate
1
Discovery and Data Audit
We assess what data you actually have against what the target accuracy requires, not what a generic project plan assumes. This includes checking for class imbalance, labeling quality, and whether existing data can support a fine-tuned foundation model or needs a purpose-built one, before any architecture decision gets made.
2
Model Architecture Selection
We choose between a custom-trained model, a fine-tuned vision-language foundation model, or a hybrid approach based on your accuracy target, latency budget, and available labeled data, not a default template applied to every project regardless of fit.
3
Training and Validation
Models get trained against your data and validated on a held-out set that reflects real production conditions, not a curated benchmark. Our AI model training process tracks accuracy, false positive rate, and inference latency against agreed thresholds before anything ships.
4
Deployment and Optimization
Deployment targets cloud, edge, or hybrid infrastructure based on where the images are actually captured and how fast a decision needs to happen. Edge deployments get quantized and optimized specifically for the hardware they run on.
5
Monitoring and Retraining
Production models drift as real-world data diverges from training data. We set up ongoing monitoring through our MLOps deployment and monitoring practice and define a retraining cadence upfront, so accuracy degradation gets caught before it becomes a business problem instead of after.
Related Case Studies
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Proof Of Execution
Real-world delivery sucess
Read Case Study
Brainkey
Platform leverages AI to analyze brain imaging data, providing insights into brain health.
HUDDLE
The Huddle is an all-in-one AI-driven e-learning platform designed to revolutionize online education.
Client Testimonials (We're Rated 4.7 on Clutch)
THE PROJECT
Web & AI Development for Education Company
AI Development, Custom Software Development
Nov. 2024 - Ongoing
THE REVIEW
Their agile way of working, competence, and friendliness are impressive.December 7, 2024
PROJECT SUMMARY:
Citrusbug Technolabs handles a data extraction project for an education company. They're also responsible for managing an AI generation project for the client.
FEEDBACK SUMMARY:
Citrusbug Technolabs has laid out and followed a clear project outline and consistently met deadlines. The team communicates well and demonstrates a strong understanding of the client's vision and goals. Their agile approach, high level of competence, and personable approach stand out.
THE REVIEWER
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THE PROJECT
Custom Automated System Dev for Veteran Medical Co
Generative AI, Application Testing, Custom Software Development
Aug. - Oct. 2024
THE REVIEW
Their communication and ability to capture exactly what I was asking for were impressive.November 14, 2024
PROJECT SUMMARY:
Citrusbug Technolabs built an automated system for a veteran medical company. The system needed to collect and store user data, generate customized letters based on surveys, and use AI to summarize reviews.
FEEDBACK SUMMARY:
Citrusbug Technolabs delivered a functional automated system that successfully organized the client's records and auto-generated statements. The team was quick to respond, addressed all roadblocks, and communicated effectively. Overall, their ability to understand the client's needs stood out.
THE REVIEWER
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THE PROJECT
MVP Development for Freight & Capacity Marketplace
Custom Software Development
$10,000 to $49,999
Jan. 2024 - Feb. 2025
THE REVIEW
They were very calm and talented, and they did bring some of their own ideas.January 28, 2025
PROJECT SUMMARY:
Citrusbug Technolabs has developed an MVP for a freight and capacity marketplace. The team has created three different interfaces for three market segments.
FEEDBACK SUMMARY:
Citrusbug Technolabs has successfully developed an effective MVP. The team has delivered the project on time and almost within budget. Moreover, their responses have been timely. Overall, the team has been calm, talented, and innovative. This has led to the client's satisfaction.
THE REVIEWER
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THE PROJECT
Web & Mobile App Development for Education Company
Custom Software Development, DevOps Managed Services
$10,000 to $49,999
THE REVIEW
What stood out most about them was their personalized approach and sense of ownership in every aspect of the project."February 27, 2024
PROJECT SUMMARY:
Citrusbug Technolabs built a Flutter cross-platform mobile app for an education company. They also developed two web apps and an admin panel and provided DevOps support for the client's infrastructure.
FEEDBACK SUMMARY:
Citrusbug Technolabs' support successfully boosted the client's user engagement by 8x, resulting in an 85% retention rate. The reliable team demonstrated a dedication to delivering on time and promptly responding to concerns and adjustments. Their personalized, client-centric approach stood out.
THE REVIEWER
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THE PROJECT
Mobile App Development for Healthcare Company
Custom Software Development Mobile App Development UX/UI Design
$10,000 to $49,999
June 2023 - Jan. 2024
THE REVIEW
They didn't like the phrase "impossible to do", which made them very solution-oriented.February 19, 2024
PROJECT SUMMARY:
A healthcare company hired Citrusbug Technolabs to design and develop a user-friendly application. The team provided tailored assistance to meet the client's requirements.
FEEDBACK SUMMARY:
Citrusbug Technolabs successfully delivered an application that was ready for market. The team was highly professional and well-organized; they stayed focused throughout the engagement and were receptive to changes. Overall, they stood out for their trustworthiness and commitment to excellence.
THE REVIEWER
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THE PROJECT
Custom Web App Dev & Design for Financial Technology Firm
Mobile App Development UX/UI Design Web Design
Sep. 2023 - Ongoing
THE REVIEW
They're 100% on time and transparent throughout each stage of the project.February 24, 2024
PROJECT SUMMARY:
A financial technology company has hired Citrusbug Technolabs to build a custom web application. The team handles the development and design of the app.
FEEDBACK SUMMARY:
The project is ongoing, but the client has been satisfied with Citrusbug Technolabs' services. The team is very professional — they always respect timelines and provide regular updates to ensure everyone's on the same page. Also, their transparent approach makes them a trustworthy partner.
THE REVIEWER
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THE PROJECT
Staff Augmentation for Singaporean Development Company
Mobile App Development
$10,000 to $49,999
THE REVIEW
"I do believe they are exceptional at where they stand."
PROJECT SUMMARY:
Citrusbug Technolabs took on a mobile app development project for a Singapore-based development house. They were provided with UX designs to execute. Currently, they're finalizing and fine-tuning the product.
FEEDBACK SUMMARY:
Citrusbug Technolabs contributed to the successful launch of the product, which has garnered over 10,000 users in just three months. The product has also received support from investors. They consistently followed-up with their progress and delivered at a speedy rate. The team is truly stellar.
THE REVIEWER
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What Makes a Vision Model Production-Ready
Validated against real environmental variance, not a clean benchmark set
Meets an agreed inference latency budget at expected production volume
Ships with a documented false positive rate your team signed off on
Monitored post-launch with a defined retraining trigger, not left to drift silently
Vision AI Built for the EU AI Act's High-Risk Rules
High-risk obligations under the EU AI Act’s Article 6 and Annex III become enforceable on August 2, 2026, and biometric identification and categorization systems fall squarely inside that scope.
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Bias and accuracy audit documentation prepared alongside the model, not reconstructed after a regulator asks
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GDPR-aligned biometric data handling built into the pipeline from the first data collection step
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SOC 2 Type II infrastructure for any system processing identifiable visual data
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Clear separation between biometric verification (lower obligation) and biometric identification (high-risk) built into the system design from day one
What's Included in a Vision AI Engagement
Every engagement includes the full path from data to a deployed, monitored model, not just the model itself.
Dataset Audit and Labeling Strategy
We assess what you have, identify gaps, and define a labeling approach that gets you to a usable training set without over-collecting data you don’t need.
Model Architecture and Training
Custom or fine-tuned model development matched to your accuracy target, latency budget, and deployment environment.
Integration and API Layer
A secure, documented API layer connecting model output to your existing systems, including intelligent document processing pipelines where OCR and structured data extraction are part of the workflow.
Monitoring and Retraining Plan
A defined cadence for tracking accuracy drift and retraining, agreed before launch, not negotiated after accuracy drops.
Choose the Right Vision AI Engagement Model
Proof of Concept
Feasibility validation
Test model accuracy against a real, representative slice of your data before committing to a full build.
• Small labeled dataset
• Single use case
• Go/no-go accuracy report
• 4-6 week timeline
Production Build
Full model and integration
End-to-end development from architecture through deployment, integrated into your existing systems.
• Full dataset labeling
• Custom or fine-tuned model
• API integration
• Deployment support
• Post-launch handover
Managed Vision Ops
Ongoing monitoring and retraining
Continuous accuracy monitoring, drift detection, and scheduled retraining once the system is live.
• Drift monitoring
• Scheduled retraining
• Performance reporting
• SLA-backed support
How Much Does It Cost to Develop AI Image Recognition Software?
Custom image recognition projects typically range from $20,000 for a scoped proof of concept to $150,000 or more for a production system with edge deployment and ongoing monitoring, depending on data readiness and integration scope.
Why Citrusbug for AI Image Recognition Software
Models Trained on Your Data
Not a generic API fine-tuned at the margins. We train and validate against your actual images, environment, and edge cases from the first sprint, not a benchmark dataset that looks nothing like production.
Vision Systems We Keep Watching
Most vendors hand over a model and move to the next project. We build monitoring and a retraining cadence into every engagement, so accuracy drift gets caught before it costs you.
Proof of Concept Before Full Build
We validate feasibility against real data before you commit to a full production build, so the accuracy question gets answered early, not after budget is already spent.
FAQs About AI Image Recognition Software
How accurate is custom AI image recognition software?
Accuracy depends on data quality, model architecture, and training volume. Enterprise deployments with well-labeled domain-specific data typically outperform generic APIs on the exact cases that matter to your business.
How long does implementation take?
A proof of concept runs 4-6 weeks. Full production builds with integration and edge deployment typically run 10-20 weeks depending on data readiness.
Do we need to worry about the EU AI Act?
If your system does biometric identification or categorization, yes, high-risk obligations apply from August 2026. Biometric verification-only use cases are generally exempt, but the distinction is easy to misclassify.
Can it integrate with our existing systems?
Yes. Our API-first architecture integrates with ERP, CRM, EHR, and custom internal platforms through secure, documented endpoints.
What happens if model accuracy drops after launch?
We define a monitoring and retraining cadence upfront as part of Managed Vision Ops, so drift gets flagged and addressed on a schedule, not discovered by accident.
Do you build on our data or a foundation model?
Both, depending on your use case. We assess whether a fine-tuned foundation model meets your accuracy target or whether a purpose-built model is the better fit before recommending either.
Can it run on edge devices without cloud connectivity?
Yes, models can be quantized and optimized for edge hardware when real-time processing without reliable connectivity is a requirement.
How is cost determined?
Cost depends on data readiness, model complexity, and deployment environment, not a flat per-project rate. A scoping call gives you a realistic range before you commit.
Turn Visual Data Into a System You Can Trust
Stop relying on a generic API's accuracy claims. Build a vision system trained on your data, validated against your accuracy bar, and monitored after it ships.
