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PROOF OF CONCEPT · HITL
Proof of
Concept
A PoC done right is not a demo it's a structured validation that answers one critical question: does this AI actually work in your context, with your data, for your users?
WHY IT MATTERS
Only 25% of AI initiatives deliver expected ROI. The root cause is almost always data quality not the model.
DURATION
6 to 12 weeks from kickoff to
validated go/no-go
recommendation.
ARCHITECTURE
Human-in-the-Loop at key
decision points. Built for
production, not just demo
conditions.
TRACK RECORD
1.5M+ images/month. 50+
retail sites. 98% accuracy in
production.
25%
AI initiatives that actually deliver expected ROI. The PoC is how you join this group.
1.5M+
Images processed per month in production across retail and fashion clients
98%
Accuracy achieved on image classification for retail clients in production
50+
Retail sites with active HITL computer vision deployments
Four stages no shortcuts
Framing
Define the precise question the PoC must answer. Set success criteria before touching data. Scope the minimum viable test so results are clean and actionable not ambiguous.
Data validation
Pilot execution
Evaluation
Assess data quality, volume and labeling requirements. Identify gaps early and surface the hidden blockers. This stage alone prevents the majority of PoC failures.
Build and run the minimum viable model with your real data. Human-in-the-Loop checkpoints where model confidence is low. Measure against the criteria defined in stage one.
Honest go/no-go with full rationale. If yes: the path to production. If no: what conditions need to change to make it viable so the work isn't wasted.
01
02
03
04
Where this sits in the BPI pathway
PATTERN 01
Monitor
The model runs autonomously but flags low-confidence predictions for human review. Used when speed matters and errors are recoverable. Typical in e-commerce cataloging and trend analysis.
PATTERN 02
Validate
Humans validate model outputs before any action is taken. Used in regulated environments or when error costs are high. Typical in quality control and security alert systems.
PATTERN 03
Command
The model provides recommendations but humans retain full decision authority. Used in high-stakes contexts where explainability and legal accountability are critical.
Human-in-the-Loop isn't a workaround it's the architecture that makes AI reliable at scale. Three patterns, each suited to a different risk profile.
Proven in production four sectors
40M+
labeled images
10K+
attributes
98%
accuracy
Fashion & E-Commerce
Trend forecasting, product cataloging and visual search at scale. 1.5M images/month standardized across 50+ retail sites. HITL Monitor pattern throughout.
HITL
command
Industrial Quality Control
Visual inspection on production lines. HITL Command pattern: operators retain full decision authority for defect classification, preventing costly misclassifications on high-value parts.
0.5M
frames/month
50+
sites
95%
accuracy
Retail Analytics
In-store footfall counting across 50+ retail locations. HITL Validate pattern: operators review and validate model counts before reporting, achieving 95% accuracy.
HITL
Validate
Modeling & Talent
Profile matching and visual search for agency databases. Human curation remains central the model accelerates, not replaces, the creative and editorial decision.
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