Train fast. Deploy smarter.
We design and operate enterprise ML platforms, from feature stores to real-time inference, so your models ship to production in days, not quarters.
Every layer of your AI/ML stack
Every layer of your AI/ML stack
Every layer of your AI/ML stack
Six reasons enterprises trust Infivit with their AI roadmap.
Built around the actual 2026 pain points: getting GenAI past pilot, controlling hallucinations and cost, staying ahead of AI governance and never being locked to one model vendor.
GenAI in production in 30 days, not 30 quarters.
Most enterprises are stuck in 12-month GenAI pilots that never reach customers. Infivit ships agentic, retrieval-grounded systems that touch real revenue in 30 days flat.
Hallucinations under 2%, audited every release.
Retrieval grounding, eval harnesses, guardrails and human-in-the-loop review baked into every GenAI workflow. Outputs your CFO and compliance team can sign off on.
40% lower inference cost at the same quality.
Smart model routing, distillation, semantic caching and right-sized GPU infra. Frontier-grade output without frontier-tier bills every quarter.
Drift caught in hours, not customer complaints.
Continuous evals, drift detectors and shadow-mode rollouts running 24/7. Your model degrades, our pipeline catches it before your users do.
EU AI Act and ISO 42001 ready from day one.
Audit trails, model cards, lineage, PII redaction and bias testing baked into the pipeline. AI compliance is non-negotiable in 2026 we treat it that way.
Model-agnostic, future-proof by default.
OpenAI today, open-source tomorrow, your fine-tune next quarter. Our abstraction layer lets you swap models without rewriting the product around them.
Explore every AI service we ship.
Eight production-ready AI workstreams. Tap a row to preview the capabilities, or jump straight to the full detail page.
Anomaly detection, predictive incident response and self-healing infrastructure powered by ML, across hybrid clouds.
From raw data to live model.
Our ML lifecycle pipeline automates every step, from feature engineering to production serving, with governance, explainability and observability built in as mandatory gates.
Drift detected = automatic retrain
Every monitored model triggers a retrain pipeline when concept drift or data skew exceeds configured thresholds, zero manual intervention.
Data Ingestion & Feature Store
Automated collection, validation and feature engineering at petabyte scale using Spark and dbt.
Model Training & Tuning
Distributed training on GPU clusters with hyperparameter optimization and experiment tracking via MLflow.
Evaluation & Validation
Rigorous offline evaluation with fairness metrics, bias detection and business KPI alignment gates.
Model Registry & Packaging
Containerized model artifacts in a versioned registry with full lineage, DVC tracking and reproducibility.
Deployment & Serving
Blue/green model rollouts to Kubernetes with auto-scaling, shadow mode testing and A/B experiment routing.
Monitor & Retrain
Continuous data drift and performance monitoring, auto-triggering retraining pipelines when thresholds are crossed.
Our AI/ML Technology Stack
Industry-standard, battle-tested tools, not experimental pet projects.
Ready to put AI to work?
Book a free AI/ML architecture review and leave with a clear roadmap to production-grade ML for your team.
