Computer Vision Development
FISTA Solutions builds computer vision systems that hold up outside the lab: detection, classification, inspection, counting, and OCR trained and evaluated on your own imagery, deployed to cloud or edge within real constraints, with accuracy reported per class rather than as a single flattering number.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What does computer vision development include?
Vision work covers use-case definition with accuracy targets, image collection and labeling, model selection and training, evaluation under real conditions, deployment to the chosen target, and monitoring for drift as conditions change.
- 01
Data collection and labeling
Representative imagery including edge cases and poor conditions, labeled to a documented standard.
Data - 02
Model selection and training
Architecture chosen against accuracy, latency, and deployment constraints rather than novelty.
Model - 03
Condition-aware evaluation
Accuracy measured per class and per condition — lighting, angle, occlusion — rather than as one number.
Evaluation - 04
Deployment
Cloud, on-premise, or edge deployment with optimization to the target's compute and thermal budget.
Deployment - 05
Drift monitoring
Monitoring for changing conditions and performance drift, with a retraining path when they change.
Operations
Requirements
Which requirements shape computer vision development?
Vision systems fail when conditions differ from training. Requirements cover representative data including hard cases, per-class evaluation, realistic accuracy targets tied to the cost of each error type, deployment constraints, and drift detection as the environment changes.
| Requirement | Why it matters | How FISTA builds to it |
|---|---|---|
| Representative data | Models learn the conditions they see. | Imagery spanning lighting, angles, occlusion, and equipment variation, with edge cases deliberately included. |
| Per-class accuracy | Averages hide the classes that matter. | Accuracy reported per class and condition, with confusion analysis rather than a single score. |
| Error cost asymmetry | False positives and negatives cost differently. | Thresholds tuned against the business cost of each error type rather than to maximize a symmetric metric. |
| Deployment constraints | Edge hardware limits what can run. | Model selection and optimization against the target's compute, memory, and thermal budget. |
| Drift detection | Conditions change; models do not. | Monitoring for input distribution shift and performance decline, with a defined retraining trigger. |
Where AI fits
How should you sequence computer vision development?
Start with the narrowest useful vision task and prove it under real conditions. A detector that works in one controlled setting is a demo; one that holds across shifts, seasons, and equipment changes is a system.
- 01
1. Narrow the task
One class of decision under defined conditions, rather than general-purpose visual understanding.
- 02
2. Collect real imagery
From the actual environment including bad lighting, dirty lenses, and unusual angles.
- 03
3. Set accuracy targets by error cost
What a false positive costs versus a false negative, which determines the threshold.
- 04
4. Evaluate under conditions
Per class and per condition, so weaknesses are known before deployment rather than discovered.
- 05
5. Monitor for drift
Conditions change with seasons, equipment, and process; detection triggers retraining.
Cost and timeline
How much does computer vision development cost, and how long does it take?
Cost is driven by labeling volume and deployment constraints; timeline by data collection across real conditions. FISTA does not quote blind: the scoping call returns a data plan, accuracy targets, and a phased estimate.
Data collection and labeling dominate the budget. Gathering imagery that spans real conditions takes time, and models trained on convenient data fail on inconvenient days.
Edge deployment adds optimization work per device class. Model size, memory, and thermal limits constrain architecture choice, so the deployment target is settled before training begins.
Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.
Get a scoped quoteDelivery
How does FISTA deliver an AI system?
FISTA delivers AI in four phases: a discovery sprint that defines the success metric, data readiness, and specification; a design that fixes the model strategy, retrieval, guardrails, and evaluation plan; iterative builds scored against a golden set; and a production release with tracing, dashboards, cost budgets, and a change process.
- 1
Discover and define
Use-case selection, data audit, success metrics, risk review, and a written specification with an evaluation plan.
OutputSpecification, golden set, estimate
- 2
Design the system
Model strategy, retrieval and data pipelines, guardrails, human review points, and the deployment target.
OutputArchitecture, model decision record
- 3
Build and evaluate
Two-week increments, each scored on the evaluation harness for quality, latency, and cost, demoed on real data.
OutputEval reports, working system
- 4
Release and monitor
Production deployment with tracing, quality and cost dashboards, drift alerts, runbooks, and a change process that re-runs the evals.
OutputProduction AI system with SLOs
Why FISTA
Why choose FISTA Solutions for computer vision development?
FISTA trains vision systems on your real conditions, reports accuracy per class rather than as an average, and tunes thresholds against the actual cost of each error type. Work is contracted through a US entity with full IP assignment.
Computer Vision specifics
- Training and evaluation data span the real conditions of use, including the poor ones that break naive systems.
- Accuracy is reported per class and condition with confusion analysis, not summarized into one flattering figure.
- Thresholds are tuned against the business cost of false positives versus false negatives.
- Drift monitoring is deployed with the model, with a defined retraining trigger as conditions change.
How FISTA engineers
- Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
- AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
- Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
- One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.
What you get as a client
- 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
- A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
- US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
- Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.
Clear answers
What buyers ask before an AI build.
Straightforward guidance for evaluating scope, fit, and the next step.
01How accurate can computer vision be?
Highly accurate on well-defined tasks with representative data, and unreliable when conditions differ from training. FISTA sets targets against your real conditions and reports per class rather than quoting a benchmark figure.
02How much labeled data do we need?
It depends on task difficulty and variability. Condition coverage matters more than raw volume, so FISTA plans collection across lighting, angles, and equipment rather than maximizing image count alone.
03Can it run on the edge?
Yes, with model selection and optimization against the target's compute, memory, and thermal limits. That constraint is settled before training because it shapes architecture choice.
04What happens when conditions change?
Performance degrades, which is why drift monitoring ships with the model and a retraining trigger is defined. Seasons, equipment changes, and process changes all shift input distributions.
05How long does a vision project take?
Data collection and labeling usually dominate and can take weeks; training and evaluation follow faster. The data plan precedes any timeline commitment.
Scoped in writing before you commit
Build vision that works on a bad day, not just a good one.
Bring the visual task and real imagery. The scoping call returns a data plan, accuracy targets, and a phased estimate.