Revenue & growth
Demand forecasting, churn scoring, recommenders, and personalization tied to measurable lift.
NASBIX TECHNOLOGIES
Machine learning that runs in production
Forecasting, scoring, classification, and recommenders—feature pipelines, trained models, and MLOps so predictions serve in batch or real time with monitoring, not notebooks that nobody deploys.
Feature & label pipelines
Business-aligned metrics
Batch & online serving
Drift & model monitoring
Machine learning
Models only matter when ops can run them
ML projects die in Jupyter when training-serving skew, leaky features, and unowned pipelines make production scores untrustworthy. NASBIX delivers classical and deep learning with engineering discipline: reproducible training, evaluation that matches business outcomes, and deployment paths your platform team can operate.
From Karachi we build for teams that need prediction—not necessarily another chatbot. Pair with LLM and copilot work when generative AI sits on top of structured models, and with data engineering when lakes and streams must feed features first.
- Problem framing: baseline metrics, label strategy, and feasibility before GPU spend
- Leakage checks, holdout design, and explainability where regulators or buyers ask why
- Serving on cloud, Kubernetes, or embedded APIs—with versioning and rollback
- Monitoring for data drift, performance decay, and retrain triggers
Who we help
Built for prediction at scale
Greenfield models, replatforming legacy scores, and hardening pilots stuck in research.
Risk & operations
Fraud detection, credit scoring assist, anomaly detection, and capacity planning models.
Vision & language (classical)
Classification, extraction, and embedding workflows when fine-tuned ML beats generic LLM cost.
What you get
Machine learning capabilities that matter
End-to-end delivery from messy tables to scored users—not a Kaggle notebook handoff.
Data & features
Cleaning, feature stores, label pipelines, and documentation so training matches serving inputs.
Modeling
Baselines to tuned models—tree ensembles, linear, deep nets, and embeddings chosen for data size and latency.
Evaluation
Metrics aligned to business: precision/recall tradeoffs, calibration, uplift, and backtests on time series.
Deployment
Batch scoring jobs, REST/gRPC endpoints, edge export, and CI for retraining pipelines.
MLOps
Experiment tracking, model registry, environment reproducibility, and promotion gates between stages.
Monitoring
Drift detection, alert thresholds, shadow deployments, and playbooks for rollback and retrain.
ML + data + product
Models embedded in software—not siloed in research
We integrate predictions into apps, dashboards, and workflows—with auth, SLAs, and fallbacks when models abstain or fail.
- Forecasting for logistics and inventory when seasonality and spikes dominate
- Scoring and anomaly patterns for fintech with audit-friendly logs
- Recommenders and LTV models for e-commerce catalog and CRM data
- Feature pipelines on Spark, dbt, or cloud-native stacks via data practice
- Human-in-the-loop review UI when low-confidence predictions need approval
- Secure handling of PII and model access aligned with secure SDLC
How we deliver
From feasibility to governed production
Prove lift on historical data, then ship serving with monitoring before full automation.
- 01 · DiscoverUse case, data availability, constraints, baseline performance, and success metrics.
- 02 · ExperimentFeature work, model candidates, offline eval, and stakeholder review of tradeoffs.
- 03 · ProductionizePipelines, serving, integration tests, and pilot with shadow or limited traffic.
- 04 · OperateMonitoring, retrain cadence, documentation, and optional managed ML pod.
Engagement models
Buy the way you mature ML
ML feasibility sprint
Data audit, baseline model, and go/no-go recommendation with effort estimate for production.
Fixed-scope model
One prediction problem—from features through deployment and dashboards for stakeholders.
MLOps uplift
Take existing models into registry, CI, monitoring, and reliable serving without retraining from scratch.
ML product squad
Embedded data scientists and engineers shipping multiple models on your platform roadmap.
Explore more
Related AI & data paths
LLM programs, lakes, and BI that pair with classical ML.
