QualiteknikQA/ML
AI & ML Engineering

AI systems built with engineering rigor, not just prototypes.

Qualiteknik designs, ships, and hardens machine learning systems for teams who can't afford to find out in production that the model doesn't hold up. Applied AI, delivered like an engineering discipline.

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A monitored signal inside its control limits. Drift is flagged and corrected before it reaches production — which is the entire job.

Deep Learning Project

Teaching a phone to tell a pothole from a speed bump.

Proof of ConceptMVPPWA · Live

A crowdsensing pipeline that reads a smartphone’s accelerometer, GPS and camera as a vehicle drives, classifies the surface beneath it, and separates genuine road defects from speed bumps, street gutters and the driver simply picking up the phone.

CNN, LSTM and reservoir-computing models evaluated head to head against three non-neural baselines. Detecting a pothole is the easy half; not calling a speed bump one is what decides whether a road authority can act on the data.

Signal Capture

A phone in a moving vehicle senses the road through the suspension. Each trace is plotted against road position, so every spike sits directly beneath the defect that produced it.

The tyre bridges what is narrower than its contact patch, so a street gutter registers differently from a pothole of the same depth — and a speed bump produces a clean signature that looks a great deal like a defect. Separating those three is the classification problem.

machinelearnspothole.site ↗Read the full case study93% classification accuracy

End-to-end

From model design to production deployment

Quality-first

Evaluation & monitoring built in, not bolted on

Two offices

Toronto and Lucknow, working with teams across North America and India

Recent engagements acrossPublic InfrastructureGovernment & Civic ServicesLegal TechnologyJudicial Administration
What We Do

AI/ML engineering, end to end

We work as an embedded technical partner — from the first model prototype through to a system your team can operate, trust, and scale.

  • AI/ML Product Development

    Custom models and applied AI features, scoped, prototyped, and built to your product's real constraints — not a generic demo.

  • MLOps & Deployment

    Pipelines, infrastructure, and CI/CD for models — so retraining, rollout, and rollback are routine, not an emergency.

  • Model Quality & Evaluation

    Evaluation frameworks, test harnesses, and monitoring that catch drift and regressions before your customers do.

  • Applied AI Strategy

    Technical audits and roadmaps for teams deciding where AI actually earns its keep in the product — and where it doesn't.

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Why Qualiteknik

Quality is the engineering discipline, not an afterthought.

Our name is literally “quality” plus “technical.” That’s not branding — it’s how we build. Every model ships with the evaluation, monitoring, and documentation it needs to be trusted, maintained, and handed off.

  • Built to be operated, not just demoed

    We hand off systems your team can run, retrain, and debug — with documentation and runbooks included.

  • Evaluation before optimism

    We define what “working” means and measure against it before we call anything done.

  • Senior engineers, direct access

    No account-manager layer between you and the people writing your system.

Selected Engagements

Recent work

Public Infrastructure

Crowdsensed road surface monitoring at state scale

A road network of roughly 589,000 km was monitored by manual photo-and-GPS reports: weeks to action, inconsistent data, and no way to separate genuine defects from ordinary vibration. We built a crowdsensing pipeline that reads smartphone accelerometer, GPS and camera data as vehicles drive, classifies the road surface it is travelling on, and distinguishes potholes from speed bumps and driver handling — the false-positive problem that makes accelerometer-based detection unreliable in the field. Smartphone imagery serves as the source of truth, so the models are retrained against evidence rather than assumption.

Focus
Deep learning (CNN, LSTM, reservoir computing), crowdsensing, computer vision
Engagement
Proof of concept and MVP — a PWA, live at machinelearnspothole.site
Outcome
93% accuracy on pothole and surface classification, with an imagery-backed retraining loop

Government & Civic Services

Civic infrastructure complaint and repair platform

A development authority tracked road repairs, footpaths, parking and waste hotspots through disconnected manual processes, with no shared view of what had been reported or resolved. We ran the feasibility study and wrote the SRS, then built a web and mobile platform with separate agency and administration dashboards: the full complaint lifecycle from report to resolution, categorisation by type and by responsible agency, configurable zones and sectors, and an audit log on every transaction so accountability survives a dispute.

Focus
Systems analysis, full-stack platform, real-time reporting
Engagement
Feasibility study, SRS, and build
Outcome
Real-time complaint dashboards with agency-level accountability and a complete audit trail
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Let's Talk

Have an AI system that needs to actually work?

Tell us what you're building or where it's breaking down. We'll tell you honestly whether we're the right fit.

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