Computer vision development that ships to production, not to a demo reel.

Computer vision that runs in production, not just demos

Every computer vision demo looks impressive on clean, well-lit, carefully chosen images. Production systems deal with motion blur, variable lighting, partial occlusion, document scans at an angle, and conditions that weren't in the training data.

According to Grand View Research's 2024 Computer Vision Market Report, the global computer vision market was valued at $19.82 billion in 2024 and is projected to grow at a 19.8% CAGR through 2030, driven almost entirely by enterprises replacing manual inspection and document handling with automated visual processing. That growth is real demand from operations teams who can no longer absorb the cost of human-reviewed visual data at scale.

The hard part isn't getting a model to 85% accuracy on a benchmark. It's getting to 95%+ on your specific products, your specific documents, your specific environment, and keeping it there as conditions change.

We built an AI-OCR pipeline for a US gas-station operator that read industrial documents automatically and cleared 20,000+ transactions in a single day during real-world testing. We shipped a HIPAA-compliant patient-monitoring system that cut clinical decision time 20% for US chronic-care teams. Compliance is scoped in week 1, HIPAA, GDPR, or SOC 2, not retrofitted before launch. We have shipped production-grade computer vision like this since 2015.

The Production Vision Method: five decisions that separate a demo from a line

A model that scores 92% on a benchmark and a model that holds accuracy on your line for a year are two different builds. The gap is not the architecture. Five decisions most demos skip decide it. We lock all five in scope, before a single epoch of training, and write them into the fixed-price document you sign.

1. Ground truth before models

Labeling strategy comes first. We define the label schema with your operators, run inter-annotator agreement checks so two labelers agree on the same defect, and deliberately over-sample the hard cases, occlusion, glare, rare defect types, because a model is only as honest as its worst-labeled edge case.

2. Edge or cloud, decided by the latency budget

Inference placement is a tradeoff, not a default. An inspection station that must reject a part in under 100ms runs on-device (NVIDIA Jetson, TensorRT). A nightly document batch runs in the cloud where GPUs are cheaper. We size the latency budget first, then place inference where it is met.

3. Confidence-gated human review

The model does not guess. Every prediction carries a confidence score, and anything below a per-field threshold routes to a human queue instead of a confident wrong answer. High-confidence outputs flow straight through, so people only touch the fraction the model is unsure about.

4. Drift watch and scheduled retraining

Accuracy decays as conditions change, a new supplier, a re-lit line, a camera swap. We instrument accuracy in production, alert on drift, and feed corrected production examples back into a scheduled retraining loop so the model improves rather than silently degrades.

5. The accuracy-versus-latency tradeoff, written down

A bigger model is more accurate and slower; a quantized model is faster and slightly less accurate. There is no free lunch. We set the target for both, tune to the point that clears your operational need, and record why, so the tradeoff is a decision on record, not an accident.

The second decision, edge versus cloud, is the one teams most often get wrong. Here is how we choose:

The stack we build computer vision on

We are not tied to one framework or one model family. We select the architecture that hits your accuracy and latency targets, then document every choice so any competent ML team can retrain and maintain it. The technologies we reach for most often:

The rule holds at every layer: no proprietary tooling that locks you in, and no stack we cannot hand to your team on day one.

Pitfalls we plan around

Most computer vision projects that stall do so for the same handful of reasons. We name them in discovery so they are budgeted for, not discovered in week 10.

Labeling cost is the real budget line

The model is cheap; the labeled dataset is not. Thousands of accurately annotated examples take real hours. We scope labeling explicitly and use transfer learning to cut the labeled-data requirement by 60-80% versus training from scratch, but we never pretend the labeling work is free.

The edge cases you did not photograph

A model only knows what it was shown. The defect that appears twice a year, the form from one rare supplier, the pallet at an odd angle, if it is not in the training set, the model will miss it. We plan a data-collection phase to fill these gaps rather than assume your existing footage covers them.

Lighting and domain shift

A model trained on morning light degrades under afternoon glare; one trained on Line A misreads Line B's camera. This is the single most common reason accuracy drops after launch. We test across conditions before deploy and monitor for drift after, so the fix is a scheduled retrain, not a fire drill.

False positives cost trust

A quality system that cries wolf gets switched off. We tune the decision threshold to your real cost of a miss versus a false alarm, and route uncertain calls to human review rather than forcing a confident answer the operator learns to ignore.

Where computer vision is heading: vision-language models

The frontier is shifting from single-purpose detectors to vision-language models, systems that take an image and a plain-language instruction and return a structured answer, no per-class retraining required. They are already strong for open-ended document understanding and zero-shot classification, and weaker where millisecond latency or certified accuracy is non-negotiable. We track them closely and use them where they earn their place, a VLM for a long-tail document type, a tuned YOLO model where the inspection line needs sub-100ms certainty. The method above does not change: ground truth, the right inference placement, human-in-the-loop, and drift watch still decide whether it survives contact with production.

What clients say

What our clients say

Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.

Charles E.

USA

Entrepreneur at Aggie Technologies

“

All of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!

01 / 02

Proof

Case studies

AI OCR for gas station operations

20K+

transactions processed in a single day

Read case study

AI OCR receipt validation for a supermarket loyalty platform

99%

AI validation accuracy, up from 80%

Read case study

Grubly processes 10,000+ restaurant orders while saving owners $5,000 in third-party delivery commissions

Grubly lets restaurants own their online ordering channel. 10,000+ orders processed, $5,000 saved in delivery commissions in the first 15 days, and a direct customer relationship that third-party platforms do not allow.

$ 10k+

transactions in first 15 days

$ 5k

savings on food delivery commissions

Read case study

See all projects

We price by project, not by the hour. After a scoping session you get a fixed quote with a defined scope, timeline, and price, so you know the number before development starts. Where you land depends on scope, not negotiation:

Focused system, $25,000-$60,000

One use case: model training on your data, an inference pipeline, and integration to one target system.

Multi-use-case platform, $60,000-$150,000

Real-time video processing, exception workflows, and multiple output integrations.

Cost is driven by the complexity of the visual task, the amount of training data required, and the inference throughput needed. What pushes it up: variable real-world conditions that demand more domain-specific training data, strict compliance such as HIPAA and GDPR, and high-throughput real-time processing. We scope every project before pricing it.