Not Every Frame Needs Deep Learning
Classic computer vision still wins for fixed geometry, barcodes and measurement. Deep learning handles variation: glare, damage, obscured plates, new SKUs. Frontier multimodal APIs fit low volumes; purpose-built models win at millions of images. Review tools ship on low-code. We choose per component.
Legacy Vision Pipelines We've Modernized
We replaced a Haar-cascade license plate recognition pipeline with neural make-and-model classification, raising automated processing roughly 40% and cutting manual review for a 300-person team. For a beauty retailer, a multimodal returns verifier is piloting. Explore our case studies.
Footage Stays Private
Camera feeds, product photos and labeled datasets stay inside your private cloud perimeter. Nothing trains a public model, and SOC2/GDPR-aligned practices apply from the first prototype onward.
Your Models, No Lock-In
You own the trained models, datasets, labeling tools and code. No per-camera license or proprietary vision platform stands between you and your system if you take it in-house.
Measured on Your Frames
Every claim about accuracy comes from a benchmark on your own images, compared with your legacy system on the same frames, not from a vendor demo.
Built to Retrain
Data pipelines, labeling workflows and evaluation sets are part of the delivery, so adding new products, cameras or sites means retraining a model, not starting another project.
Computer Vision Development Cost & Packages
Vision Prototype
$15,000+
A modern model for one legacy failure point, trained and benchmarked against your current system on your real images in about two weeks. Best when manual review keeps growing and you need proof before a full rebuild.
What's included?
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Pipeline Audit: failure modes, review volume and the real cost of the legacy system
Prototype Model: deep learning or multimodal model trained on your own images
Side-by-Side Benchmark: accuracy and automation rate compared on identical frames
Findings Report: measured gains, risks and a costed path to production
Core CV Modernization
$45,000+
One legacy vision pipeline rebuilt end to end: model, integration, thresholds and guarded cutover with parallel running. This is the tier most mid-market teams choose to retire a brittle system without stopping operations.
What's included?
Everything in Vision Prototype: carried straight into production without re-discovery
Integration: connection to your cameras, existing pipeline and business systems
Guarded Cutover: parallel running, reconciliation, rollback plan and threshold tuning
Monitoring: drift alerts, automation-rate dashboard and a retraining workflow
Full Vision Platform
$90,000+
A multi-pipeline modernization across sites, cameras or product lines, with shared data and retraining infrastructure, compliance-grade deployment and a trained internal team. Built for operations where vision is core to the business.
What's included?
Everything in Core CV Modernization: applied across multiple pipelines, sites and product lines
Data Platform: shared labeling, versioned datasets and evaluation sets for every model
Compliance: SOC2/GDPR-aligned deployment inside your private cloud perimeter
Team Enablement: training, runbooks and a roadmap your engineers can execute
Why Choose Building Agentic?
You could hire computer vision developers one by one or license a closed vision platform. Neither replaces a team that has already modernized a legacy pipeline in production.
Classic Where It Works
We keep classic computer vision for fixed geometry and measurement, and bring deep learning in only where variation breaks the rules.
Proof on Your Frames
You see the modern model match or beat your legacy system on your own images before the full build is approved.
No Rip and Replace
We modernize around your existing cameras and pipeline, so operations keep running while the new system proves itself in parallel.
You Own the Models
Trained models, datasets and code stay yours, with no per-camera license standing between you and your own vision system.
How It Works
Computer vision modernization fails when it is sold as one huge rewrite. We run it as four short, funded stages, each with a measurable result on your own images before the next one starts.
Step 1
Audit the Pipeline
We review cameras, algorithms, error logs and manual review volume, then rank the failure points worth modernizing first by business impact.
Step 2
Prototype Side-by-Side
A modern model runs next to your legacy system on real images within about 14 days, with accuracy measured on identical frames.
Step 3
Cut Over Safely
Old and new pipelines run in parallel until results reconcile, then thresholds shift gradually and the legacy component retires for good.
Step 4
Monitor & Retrain
We track drift, automation rate and review volume in production, then retrain as products, cameras and conditions change over time.
Industries We Modernize Computer Vision For
Legacy vision shows up wherever images or scanned documents drive a decision. These are the operations where we already run production systems and where modern computer vision pays back fastest.
The Team Behind Your Vision Modernization
A founder leads every engagement, together with ML engineers who have shipped production vision models. Here is who you will work with.
Production vision work is led by our ML engineers, who average 5–7 years of senior experience.
Inside a Vision Pipeline Rebuild
Our engineers answer the questions operations teams ask before replacing a working but brittle vision system: when to switch, how to label cheaply and how to cut over safely.
Frequently Asked Questions About Computer Vision Development Services
