August 11, 2025|5:34 PM
Whether it’s IT operations, cloud migration, or AI-driven innovation – let’s explore how we can support your success.
Can a tailored imaging solution cut costs and boost accuracy while fitting your real operations? We ask that because state-of-the-art models can exceed 90% on benchmarks, yet real-world factors like lighting, occlusion, and image quality often change outcomes.
We design practical systems that align technical choices with clear business metrics, starting with goals, data readiness, and operational limits to reduce risk and speed value delivery. Our approach targets logistics, retail, and manufacturing use cases where real-time monitoring and automation save time and money.
We build, train, and optimize models for detection, recognition, OCR, pose estimation, and analytics, and we choose the right hardware and deployment—edge, cloud, or on-premises—to match your needs.
Partnering with a U.S. team that knows how to bridge field constraints and business goals speeds adoption and reduces operational risk. We focus on practical wins like preventing forklift collisions with real-time alerts, automating analog meter reads to curb fraud, and shortening downtime with image-based parts search.
Our approach aligns with your business, industry rules, and IT standards from day one, so integrations meet customer expectations and compliance. We combine edge responsiveness for immediate action with cloud elasticity for analytics, giving you real-time decisions in the field and centralized oversight for improvement.
We set clear success criteria, governance, and security controls early, enabling local processing when data privacy demands it. Our team blends consulting, architecture, and delivery to de-risk projects, transfer knowledge to your staff, and maintain systems with monitoring, documentation, and post-launch support.
From feasibility studies through final integration, we map technical choices directly to your operational targets, so outcomes align with measurable business goals.
What we include:
We run feasibility studies, performance benchmarks, and architecture design before iterative builds begin.
Our process uses continuous testing, transfer learning, model compression, and quantization to hit latency and cost targets on real hardware.
Integration is pragmatic and secure, with APIs, microservices, and connectors that fit your workflows and scale with demand.
We define success in concrete terms—target accuracy, latency budgets, throughput, and unit economics—so results match business needs.
End result: reliable solutions that meet accuracy, speed, and cost-control objectives at scale, and that remain extensible as new data and use cases emerge.
We begin with a focused discovery that turns ideas into clear success criteria and practical next steps, aligning technical choices with the goals your teams care about.
We run a structured discovery to define the use case, stakeholders, and operational context, then codify the success metrics that matter to your business.
We assess data readiness—volume, variety, labels, and gaps—and outline the data strategy required to reach target performance.
| Assessment item | Outcome | Typical timeline |
|---|---|---|
| Use‑case clarity | Documented success criteria and KPIs | 1–2 weeks |
| Data readiness | Gap analysis and collection plan | 1–3 weeks |
| Feasibility model | ROI, budget, and milestone roadmap | 2–4 weeks |
| Technical recommendation | Architecture, hardware, and pilot scope | 1–2 weeks |
We deliver clear documentation and estimates, so executives can align and decide with confidence before a build begins.
We deliver end-to-end applications that connect automated detection with the exact alerts and reports your teams need to act fast.
We build bespoke solutions that match industry needs—from retail shelf monitoring and cashierless concepts to forklift collision alerts in logistics, medical imaging analysis in healthcare, and high-speed defect detection in manufacturing.
Deployments span web dashboards, mobile apps, edge appliances, and desktop clients so insights reach operators, managers, and associates where they work.
Outcomes: reduced errors, faster decision cycles, and measurable cost savings backed by a focused product roadmap and a collaborative team.
We craft model blueprints that prioritize real-world reliability and predictable operational metrics. Our goal is to translate accuracy numbers into outcomes your teams can act on, under real lighting, motion, and occlusion.
We select architectures matched to task demands—object detection, image segmentation, or recognition—balancing accuracy with latency and cost. We compare vision models and computer vision models to find the right trade-offs.
We accelerate training using transfer learning and pre-trained backbones to reduce data needs and improve generalization. Then we apply pruning, compression, and quantization so models run fast on edge and embedded hardware.
Benchmarks can exceed 90%, but real scenes vary. We build evaluation suites that mirror lighting changes, occlusion, and motion so accuracy maps to operations.
| Focus | Measure | Typical outcome |
|---|---|---|
| Architecture choice | Accuracy vs latency | Balanced models for edge |
| Model tuning | Training time, data need | Reduced sample size via transfer learning |
| Inference optimization | Throughput, power | Compressed, quantized models |
We deploy interoperable systems that connect field capture, local inference, and enterprise workflows so insights flow where decisions happen. We design topologies across edge, on‑prem, and cloud to balance latency, bandwidth, and cost while preserving reliability.
Edge appliances use accelerators for on-site inference, and cloud APIs on AWS, Azure, or Google Cloud handle scalable analytics and long-term model updates.
We containerize components with Docker and orchestrate with Kubernetes to ensure resilient rollouts. Secure, documented APIs and event streams connect dashboards, ERP and CRM systems, and data lakes for automated workflows.
We collaborate with your team on training and handover, delivering operational runbooks so your staff can operate and extend the solution with confidence.
We treat data as product, shaping it for resilience in the field while protecting privacy and compliance. Our approach ties collection and governance to outcomes, so models deliver real value under practical constraints.
We architect pipelines that capture diverse examples across lighting, angles, motion, and occlusion to surface hard edge cases early.
Preprocessing standardizes denoising, normalization, and quality checks to raise signal-to-noise and speed training cycles.
Annotation is managed with clear guidelines, audits, and active learning to focus labels that improve object recognition and model accuracy fastest.
We expand rare-event coverage using augmentation and synthetic datasets, so machine learning models generalize where real samples are scarce.
Privacy-first practices keep captures local when required and avoid long-term retention beyond project needs.
Governance covers lineage, access control, and compliance, and datasets are versioned with changelogs for reproducibility and rollback.
| Stage | Purpose | Outcome | Typical timeline |
|---|---|---|---|
| Collection | Capture diverse scenarios | Representative dataset for field | 2–6 weeks |
| Preprocessing | Improve signal quality | Faster training, fewer failures | 1–3 weeks |
| Annotation & Augmentation | Label and expand coverage | Higher accuracy on rare cases | 2–8 weeks |
| Governance | Control and compliance | Audit trail, safe deployments | Ongoing |
We map specific operational problems to image-based solutions that deliver measurable gains in safety, speed, and cost. Our focus is practical: match algorithms, models, and deployment to real constraints so performance holds up in the field.
We implement object detection and recognition pipelines to classify and localize items, people, and assets, enabling automation across retail, logistics, and manufacturing.
We deploy image segmentation for precise region-level understanding, supporting medical imaging, inspection, and visual search experiences that speed triage and reduce manual checks.
Automated OCR extracts structured fields from documents and labels, lowering manual entry and improving throughput for back-office workflows.
We use pose estimation for motion analysis, virtual coaching, and ergonomics monitoring so teams can prevent injuries and improve performance.
Real-time video analytics monitor live streams for tracking and alerts, helping teams respond faster and reduce risk in high-value environments.
GAN-based models enable denoising, super-resolution, and style transfer, accelerating creative workflows and improving data quality for downstream models.
We build logo detection for brand monitoring, 3D reconstruction for depth-aware robotics and AR, and anomaly detection to flag irregularities in industrial and security contexts.
| Use case | Core capability | Business outcome |
|---|---|---|
| Retail shelf monitoring | Object detection, OCR | Faster restock, fewer stockouts |
| Medical inspection | Image segmentation, anomaly detection | Higher diagnostic consistency |
| Logistics & safety | Pose estimation, video tracking | Reduced incidents, faster response |
We translate real-world constraints into practical product designs that deliver measurable gains in safety, throughput, and cost control across industries.
Planogram compliance and shelf monitoring keep stock visible and sales steady, while smart checkout and visual search speed checkout and improve conversion.
Forklift collision prevention, cargo monitoring, and license plate recognition combine on-site detection with centralized analytics to reduce incidents and speed audits.
Medical image analysis and patient monitoring tools support diagnostics and safety, built for privacy and clinical reliability.
Edge inspection systems detect defects, feed real-time quality reports into ERPs, and reduce scrap with automated anomaly detection.
Automotive analytics cover ADAS testing, traffic tracking, and damage assessment. Sports solutions provide motion tracking and pose correction for coaching.
Finance uses KYC automation, fraud detection, and face-enabled security tuned to compliance and auditability.
We translate operational requirements into phased work packages that prioritize early wins and measurable ROI. This gives stakeholders clarity on scope, timeline, and budget while we prepare for technical risk and data needs.
We begin with focused analysis to align use cases, acceptance criteria, and timelines.
We then estimate effort, map costs, and assemble a cross-disciplinary team that includes data scientists, engineers, and product owners.
Work proceeds in short sprints with automated tests and regular demos, so quality and progress are visible to the customer.
Transparent reporting and backlog refinement keep priorities aligned as data and findings evolve.
Before rollout, we validate models and features in the field, capture operator feedback, and refine behavior.
We handle integration with existing systems, deliver operator and admin training, and provide documentation for sustainment.
Post-launch, we monitor performance and model drift, and offer ongoing maintenance and feature enhancements to protect long-term ROI.
Our stack combines proven frameworks and compact runtimes so models run reliably from prototype to fleet. We match tools to platform targets, trading flexibility for performance where needed, and we keep reproducibility front and center.
We build models with PyTorch and TensorFlow, and we optimize for Apple devices with CoreML. These frameworks speed research and ease production handoffs.
Classical and 3D operations use OpenCV, scikit-image, Open3D, and OpenCL so algorithms run efficiently and complement learning-based models.
We containerize with Docker and orchestrate via Kubernetes for portable, repeatable builds.
Edge accelerators and optimized runtimes meet strict latency targets on embedded and industrial hardware, while cloud platforms provide elastic scale.
| Layer | Tools | Primary benefit |
|---|---|---|
| Model | PyTorch, TensorFlow, CoreML | Fast iteration, native device support |
| Processing | OpenCV, scikit-image, Open3D, OpenCL | Efficient image and 3D operations |
| Infra | Docker, Kubernetes, Edge accelerators | Portability, scale, low-latency inference |
| Cloud | AWS, Azure, Google Cloud | Elastic compute, centralized telemetry |
We document configurations and performance so your teams can operate, extend, and audit models with confidence. This approach ties model quality to real data and clear integration paths, helping product owners measure impact and reduce risk.
We convert model metrics into financial metrics, demonstrating clear ROI from reduced error rates and faster workflows. By tying detection accuracy to operational KPIs, we show how fewer false positives and false negatives lower labor and rework costs, and how better throughput improves customer outcomes.
Real deployments deliver safer workplaces through real-time monitoring and alerts, lower manual entry with OCR, and faster, automated defect detection on inspection lines. These gains translate into measurable reductions in downtime, shrink, and compliance risk as models mature and data improves.
Key benefits we quantify:
We track performance over time, proving that continuous learning and enriched data boost both accuracy and economics, and we build the business case collaboratively so stakeholders see the direct link between technical outcomes and financial impact.
We set clear expectations for accuracy by comparing lab benchmarks with outcomes in real operations, so teams know what to expect when models hit the field.
Benchmarks often exceed 90% on controlled tests, but real lighting, motion, occlusion, and device quality change results.
We contrast reported scores with field measurements to show realistic ranges, and we explain why numbers shift. This helps stakeholders plan rollout, staffing, and acceptance thresholds.
We pick model families and pre-trained backbones that match your data distribution and latency needs, balancing accuracy and inference cost.
Our continuous improvement workflow prioritizes hard-example collection, targeted labeling, and active learning. We monitor drift and run A/B and canary tests to validate changes safely.
| Aspect | Field impact | Typical mitigation |
|---|---|---|
| Lighting and contrast | Lower detection rates at dusk/dawn | Augmentation, HDR capture, tuned backbones |
| Occlusion & motion | Partial views reduce recall | Temporal models, multi-frame fusion, active sampling |
| Device constraints | Latency and power limits | Compression, edge-aware architectures |
We scope projects so budget, calendar, and deliverables align, mapping pilot, expand, and scale phases to clear acceptance criteria.
Typical pricing: projects start around $15,000 and range to $50K–$100K+ depending on scope, data readiness, domain complexity, and target date.
Engagement style: we start with a fixed-fee discovery when uncertainty is high, then move to agile build and iteration so value appears early and risks shrink.
We align price to workstreams—data prep, model work, integration, and user enablement—and we provide transparent reporting so stakeholders track burn, milestones, and ROI drivers.
| Package | Typical timeline | Core deliverables |
|---|---|---|
| Discovery (fixed fee) | 1–3 weeks | Use-case, data gaps, ROI model |
| Pilot (agile) | 4–12 weeks | Prototype, integration, field test |
| Scale & support | Ongoing | Production rollouts, monitoring, retrain |
Infrastructure planning: we help choose edge vs. cloud trade-offs so product performance, cost, and governance meet your needs.
We present three concise case studies that show how tailored imaging systems and disciplined data work translated into clearer metrics for customers.
We improved cargo security by enabling object capturing, 3D camera calibration, and dense point cloud processing for reliable segmentation and tracking.
Result: higher detection rates, faster processing, and fewer manual checks in logistics yards.
We delivered a Windows 10 tablet app that captures image streams from internal and external cameras and turns frames into features used for predictive models.
Result: actionable insights for trainers, faster analysis cycles, and a repeatable product for scaling to more events.
We built a deep learning Face ID proof of concept that matched still photos to live video streams and real-time captures for secure authentication flows.
Algorithms were tuned per task—segmentation for cargo, robust feature extraction for performance, and recognition for Face ID—and validated under realistic lighting, motion, and device constraints.
We combine product strategy, engineered models, and operations to turn visual data into measurable business outcomes.
Our team delivers practical computer vision solutions that work in real settings across retail, logistics, healthcare, and manufacturing. We pair governance and data practices with tuned models and modern platforms so pilots scale into dependable systems.
We prioritize security, clear KPIs, and operator adoption, integrating with existing stacks and supporting customers through training and long-term support. If you want to map feasibility, run a pilot, or build a roadmap, we will partner with you to turn visual data into lasting advantage.
We provide end-to-end computer vision software development, including consulting, model design, application build, deployment, and ongoing optimization, so businesses can automate inspection, tracking, and recognition workflows while controlling costs and scaling reliably.
Partnering with a trusted U.S. team ensures clear regulatory alignment, faster collaboration across time zones for many North American customers, and mature practices in data governance and cloud integration on AWS, Azure, and Google Cloud, which reduces operational risk and speeds time to value.
We run use-case discovery workshops, define success criteria, map data needs, outline timelines, and estimate ROI, delivering a feasibility plan that balances technical risk, data readiness, and business impact so stakeholders can decide with confidence.
We build tailored solutions for retail, logistics, healthcare, manufacturing, automotive, finance, and sports, delivering web, mobile, edge, and desktop apps that integrate with existing ERP, CRM, camera systems, and IoT sensors to meet industry workflow requirements.
Our team implements deep learning architectures for object detection, image segmentation, facial recognition, OCR, pose estimation, and anomaly detection, and we apply transfer learning, compression, and quantization to meet latency, accuracy, and device constraints.
We benchmark models under varied lighting, occlusion, and resolution scenarios, run field trials, and implement continuous improvement loops that retrain and tune models based on new labeled data to maintain robust, measurable performance.
Deployments include edge devices and embedded systems, on‑prem installs, cloud APIs, and microservices, with integration to cameras, ERP, CRM, and third-party analytics, enabling flexible architectures from edge inference to centralized monitoring.
We manage collection, preprocessing, annotation, and augmentation pipelines while designing for local processing where possible, minimizing data transfer and ensuring we do not retain raw customer data unless explicitly agreed for model improvement.
Yes, we deliver video analytics for surveillance, tracking, and retail traffic analysis, combining object tracking, multi-camera calibration, and temporal models to provide actionable alerts and business insights in real time.
Our stack includes PyTorch, TensorFlow, CoreML, OpenCV, scikit-image, and Open3D, deployed with containerization and orchestration tools like Docker and Kubernetes and optimized for edge accelerators and cloud compute to scale efficiently.
We offer fixed‑scope projects, time-and-materials engagements, and outcome-based agreements, with pricing that reflects complexity, delivery platform, and ongoing support needs, and we provide transparent estimates after requirements analysis.
Timelines vary by scope, but pilot proofs of concept often run 6–12 weeks, while full production systems typically take 3–9 months depending on data readiness, integration complexity, and regulatory requirements, with Agile iterations shortening each milestone.
We offer continuous monitoring, periodic retraining, performance audits, and incident response, ensuring models remain accurate as conditions change and that integrations and cloud infrastructure stay secure and up to date.
We present anonymized case studies demonstrating gains such as improved cargo security via object capture and 3D calibration, race performance analytics from image pipelines, and face ID proofs of concept that reduced verification times and fraud risk.
We follow best practices for consent, bias testing, and data minimization, offer privacy-preserving on-device processing options, and work with legal teams to align implementations with applicable laws and corporate ethics policies.
Provide clear objectives, sample imagery or video, existing system diagrams, and KPIs you want to impact; this information lets us scope data collection, annotation needs, and integration points to produce an accurate proposal.
