Computer Vision Pipelines & Stacks
Detection, segmentation and OCR pipelines engineered for real imagery: data loops, serving architecture and accuracy you can measure.
Sounds familiar?
- 01The model works on clean samples; real camera feeds, scans or phone photos break it.
- 02Nobody can quantify accuracy on production data, so every change is a gamble.
- 03Inference cost per frame makes the feature uneconomical at scale.
- 04Capture, preprocessing, inference and postprocessing fail independently and nobody knows which one.
What it costs to leave it
- Vision features ship with silent failure modes that erode user trust.
- GPU spend scales linearly with traffic because batching, quantization and caching were never engineered.
- Ground-truth data never accumulates, so accuracy can never improve systematically.
What the engagement covers.
Data pipeline design: capture, labeling loops, versioning and evaluation sets built from your real imagery.
Model selection and fine-tuning: detection, segmentation, classification and OCR against your accuracy targets.
Serving architecture: batching, quantization and GPU/CPU/edge tradeoffs made against the hardware you run.
Product integration: streaming results, confidence handling and human-review checkpoints where stakes demand them.
Drift monitoring: input distribution, accuracy proxies and alerting for when the world changes under the model.
How the engagement runs.
Define accuracy, latency and cost targets on representative data.
Stand up labeling and evaluation sets from production-like imagery.
Pipeline architecture: model choice, serving shape, integration.
Drift monitoring, retraining triggers and accuracy reporting.
Deliverables
Vision pipeline architecture and model-selection rationale
Labeled evaluation dataset and accuracy methodology
Serving implementation: batching, quantization, deployment target
Product integration with confidence and review handling
Drift monitoring and retraining playbook
A good fit when
- Products whose core value depends on seeing: document processing, inspection, monitoring, retail analytics.
- Teams with real image/video volume and no vision specialist.
- Organizations moving a research-grade vision model into production.
Probably not, when
- One-off demos with no production data or accuracy requirement.
- Projects expecting human-level perception from a few hundred samples.
Common questions.
Do you train models from scratch?
Rarely. Fine-tuning established architectures on your data is almost always faster, cheaper and more accurate than from-scratch training. From-scratch is considered only when the domain genuinely demands it.
Can vision run on edge devices or on-prem?
Yes. Deployment target is a first-class constraint: quantization, distillation and architecture choice are made against the hardware you have, not a cloud assumption.
How do you evaluate vision systems?
Labeled evaluation sets from your real imagery, with per-class metrics, an error taxonomy and drift proxies — the same evaluation discipline applied to language systems.
Can you work with our existing labeling tools?
Yes. Labeling loops integrate with your tooling, and the pipeline is designed so labels keep accumulating after the engagement ends.