Computer Vision
From OCR to image recognition — we bring visual intelligence to your business.
An honest read on the work
No marketing voice. A direct explanation of what the engagement actually covers and what it does not.
Computer Vision at SoftUs is the engineering of systems that turn pixels into structured signals — bounding boxes, labels, fields, measurements, counts — your operational systems can act on. We cover document understanding, object detection, defect inspection, video analytics, and the long middle ground where the camera is already in place but the data it produces is still being eyeballed by a human.
This service fits when the eyes-on-task cost is real and visible: a quality team manually inspecting a line, an ops team transcribing forms, a logistics team reconciling shipments by hand, or a retail team walking shelves. It also fits when the value is in the loop time — a defect that goes unnoticed for an hour costs more than one caught in seconds. We size the system to the latency and accuracy the operation actually needs, not the highest possible score on a public benchmark.
What we get right that most vendors miss: data acquisition and labeling strategy. A computer vision project lives or dies on the quality of its training set. We design the labeling protocol, audit inter-annotator agreement, plan for the long tail of edge cases your environment will produce, and build active-learning loops so the model improves as it sees more real-world traffic. We also do not over-train. If a classical OpenCV pipeline solves your problem in fifty lines, we will not train a transformer to do the same job.
Deployment is treated as a first-class concern. Models run on your cloud, on-prem, or on edge devices depending on your latency and connectivity budget. We package everything as containerized inference services with versioning, A/B comparison, and a calibration harness so you can validate every update against the previous one before traffic flips over. You leave with a working system, a way to retrain it as conditions change, and clear documentation for the team running it.
Four situations this service fits
If you recognize yourself in one of these, the engagement will move quickly. If not, we will tell you in week one.
Manufacturing line replacing human QC
You have an inspection station with a human checking every unit and you are bottlenecked on throughput or consistency. We build a vision system that catches the defects, logs every event, and integrates with your MES.
Ops team digitizing paper or PDF forms
You have thousands of invoices, claims, contracts, or applications coming in across formats and languages. We build an OCR plus extraction pipeline that produces clean structured data with confidence scores.
Retail or logistics team needing shelf or yard visibility
You already have cameras in stores, warehouses, or yards but no one is watching the feeds. We turn those feeds into a real-time signal — out-of-stock alerts, dock occupancy, container counts, planogram drift.
Healthcare or scientific team analyzing imaging
You have a stream of medical or scientific images that a specialist currently reads. We build a triage or assistive system that ranks, flags, or pre-annotates so the specialist works at the top of their skill set.
Five phases, end to end
The same shape every engagement runs in. Scoped weekly, demoed weekly, with a written deliverable at the end of every phase.
- Phase 01
Discovery & Scoping
1 weekWe agree on the visual task, the success metric (precision, recall, F1 at a usable threshold), and the latency budget. We sample your existing imagery and confirm the data is sufficient or scope what is needed to fill the gap.
- Task specification
- Metric and threshold definition
- Data sufficiency audit
- Latency and deployment-target plan
- Phase 02
Data & Architecture
1 to 2 weeksWe design the labeling protocol, set up annotation tooling, label or supervise the labeling, and pick the right model family — classical CV, YOLO-class detector, segmentation, transformer, or OCR pipeline.
- Labeling protocol and guidelines
- Train, validation, and test sets
- Baseline model on test set
- Annotation tooling and workflow
- Phase 03
Build & Iterate
3 to 5 weeksWe train iteratively, run error analysis on every cycle, and target the failure modes that matter to the operation rather than chasing average accuracy. We measure on real production samples, not just held-out batches.
- Tuned production model
- Failure-mode analysis report
- Confidence calibration
- Side-by-side comparison versus baseline
- Phase 04
Validate & Harden
1 to 2 weeksWe stress-test under lighting, angle, occlusion, and seasonal variation. We benchmark inference cost and latency on the target hardware — cloud, on-prem, or edge — and tune the deployment for the real environment.
- Robustness test report
- Hardware benchmarks
- Calibration and threshold tuning
- Edge-case sample library
- Phase 05
Deploy & Handoff
1 weekWe deploy the inference service to your environment, integrate with the downstream system (ERP, MES, ticketing, dashboard), set up monitoring with drift detection, and run a handoff with your ops or engineering team.
- Production inference service
- Downstream system integration
- Drift and quality monitoring
- Operations runbook
Tangible artifacts, not slide decks
At handoff, you receive a working system plus the documentation, dashboards, and runbooks needed to operate it without us.
The full AI/ML stack, end to end
From data ingestion to model training to vector retrieval to evaluation, we work across the tools production AI teams actually rely on. Reliable, well understood, and easy to hand off.
Languages
Vision Frameworks
Annotation & Datasets
Inference & Edge
Cloud & Storage
Observability & Eval
Three ways to work with us
Pick the shape that matches your stage. We will tell you honestly if a different model would serve you better.
Fixed-scope PoC
A four-to-six week build that takes one visual task — defects, document extraction, shelf monitoring — to a working model with a measured accuracy band.
Validating a vision use case before committing to a hardware rollout or production integration.
Embedded Pod
A SoftUs pod working with your ops or product team for three to six months, delivering a roadmap of vision features and integrations.
Companies expanding from one camera or use case to a fleet across sites.
Full-build retainer
We own the model, the deployment, the integration, and the ongoing tuning under a quarterly retainer with reviewed milestones.
Operations-heavy teams without internal computer vision capacity.
What you will gain
Concrete outcomes from our engagement — measurable impact you can track from day one.
Reduced manual QA effort by 80%
Improved accuracy in visual inspections
Real-time alerts preventing operational issues
Who we build for
We work across industries where data, AI, and automation unlock real competitive advantage.
Manufacturing
Defect detection and quality control
Logistics
Package tracking via image scanning
Retail
Smart checkout and inventory monitoring
Healthcare
Medical imaging analysis
Case studies
Examples of how we deliver under real constraints — timelines, data quality, and production requirements.
AI Retail Shelf Analytics System
Retailers lacked real-time visibility into shelf stock levels, causing frequent out-of-stock events that impacted sales and customer satisfaction.
Deployed in-store cameras with YOLOv8 models to detect out-of-stock items and planogram compliance in real time, integrated with ERP for automated replenishment.
Multinational Data Extraction Platform
Documents across 20+ countries had inconsistent formats and languages, making structured extraction manual and error-prone.
Built a multilingual OCR + AI processing pipeline that extracts structured data from documents across 20+ languages with automated compliance checks.
The honest answers
Direct responses to what you would ask on a first scoping call. If your question is not here, send it on the contact form and we will answer in writing within a working day.
How long does a typical engagement take?
A focused PoC runs four to six weeks. A production rollout with hardware integration is typically ten to fourteen weeks. We size based on the data state and the deployment target — edge devices take longer than cloud.
Who owns the IP and trained model?
You do. The model weights, the labeled dataset, the inference code, and the deployment configuration all belong to you. We assign IP in the MSA and retain no copies after handoff.
Do you sign a DPA and are you SOC 2 friendly?
Yes. We sign DPAs, work inside SOC 2 and HIPAA boundaries when needed, and run sensitive imagery — medical, biometric — entirely inside your environment with no external transmission.
Can you work with our existing cameras and infrastructure?
Yes. We have integrated with IP cameras, line-scan cameras, mobile capture, and edge devices like NVIDIA Jetson. We adapt to what is in the field rather than asking you to rip and replace.
What happens after go-live — do you provide support?
You receive a runbook and a handoff session. Beyond that, most clients keep us on a quarterly retainer for retraining as the environment shifts — new SKUs, new defect modes, seasonal lighting — or run it themselves.
How do you price?
Fixed-scope PoCs are flat-fee. Builds are quoted by phase with milestones tied to accuracy and integration readiness. Hardware costs are passed through at cost with no markup.
Do you handle the labeling or do we?
We design the labeling protocol and can either run the labeling end-to-end through our annotation partners or supervise your in-house team. Either way, we own the quality of the dataset that goes into training.
Can you start from a vague problem or do we need a spec first?
You can start vague. Week one is for site visits, sample data review, and turning "we want to use vision here" into a measurable task with a target metric and a deployment plan.
Adjacent work we do
Engagements that often run alongside this one.
Bring this work in-house, fast
A thirty-minute scope call gets you a written plan and a fixed quote. No slide decks, no follow-up cycle.
Have an AI idea, messy workflow, or product vision? Let's make it buildable.
Bring the problem. We'll help shape the product, define the architecture, and show the fastest path to a serious first version.
A practical first roadmap in the discovery call
Architecture, timeline, and delivery options in plain English
Security, scalability, and reliability discussed upfront
Model registry
softus-rag-v4.2
187ms
Latency
128k
Context
$0.004
Cost / req
Evaluation suite
Deploy pipeline
prod / canary 25% — healthy