MLOps Services

Ship code and models with the same confidence.

We engineer the delivery backbone that takes software and AI from commit to production automated, secure, and built to scale across cloud and edge. Classic DevOps gets your releases fast. MLOps brings that same discipline to machine learning.

OVERVIEW

Code and models are converging.
Most pipelines aren't.

Software delivery and model delivery are converging, but most teams are still running them on different muscle memory fast, automated pipelines for code, and manual, ad-hoc processes for models. That gap shows up as slow releases, configuration drift, environments that don’t match production, and models that perform well in a notebook and poorly in the wild. We close that gap by treating both as one delivery problem: version-controlled, automated, observable, and secure by default. The result is faster releases, fewer environment-related incidents, and models that ship as reliably as software.

Our Decade long experience, validated in numbers

AI Agent Live in Production

Hr->Min

PO processing Time Reduction

Zero

Added Head Count as Scale

95+

Customer Satisfaction

1->8+

Agents Scaled from First Diployment

A general reference

One way to picture both pipelines.

An abstract reference architecture, not a diagram of any specific deployment the code path and the model path are usually separate until they converge at observability and release gating.

CI/CD + Model Path Flow

Code Path

Commit

▶

CI/CD

▶

IaC / Infra

▶

Staging / Prod

Model Path

Data

▶

Features

▶

Train

▶

Registry

Release Gate

▶

Production Serving

Observability, drift detection & rollback — across both paths

Trusted by Leading Enterprises

What we build

The delivery backbone, for both code and models.

CI/CD for AI Pipelines

Automated pipelines for data, training, validation, and deployment so models ship as reliably as software, with full reproducibility and lineage.
Data & Feature Engineering

Feature stores, data versioning, and validation that keep training and serving data consistent and trustworthy.
Experiment Tracking & Model Registry

Versioned models, metrics, and artifacts with a governed registry that controls what moves to production and what gets rolled back.
Model Deployment & Serving

Scalable serving across real-time, batch, and streaming patterns including canary, shadow, and A/B rollouts on cloud, on-prem, or edge.
Model Monitoring & Drift Detection

Continuous tracking of accuracy, data drift, and performance, with automated alerts and retraining triggers before quality degrades.
LLMOps

Operationalizing GenAI and agents: RAG pipelines, prompt and version management, evaluation harnesses, guardrails, and inference cost control.
Responsible AI & Governance

Bias checks, audit trails, explainability, and compliance controls embedded across the model lifecycle.

CI/CD & Release Automation

Automated build, test, and release pipelines that take code from commit to production with confidence faster cycles, predictable rollouts and rollbacks.
Infrastructure as Code

Declarative, version-controlled infrastructure with Terraform, Pulumi, and cloud-native templates. Reproducible environments, zero drift, one-click provisioning.
Cloud Enablement & Migration

Architecture, migration, and modernization across AWS, Azure, and GCP cloud-native, multi-cloud, and hybrid designs tuned for cost and performance.
Containerization & Orchestration

Docker, Kubernetes, and service mesh for portable, resilient, auto-scaling workloads across every environment.
Configuration & Environment Management

Consistent dev, staging, and production environments with automated configuration, secrets management, and dependency control.
DevSecOps

Security shifted left into every stage vulnerability scanning, policy-as-code, compliance gates, and zero-trust access baked in, not bolted on.
Observability & SRE

Unified logging, metrics, and tracing with SLOs, error budgets, and on-call reliability practices that keep systems fast and available.

Our approach

How we think about building this.

01.

Architecture-first

Senior architects design the pipeline, platform, and operating model before a single tool is chosen.

02.

One delivery model, two payloads

Code and models move through the same discipline version control, testing, staged rollout, rollback.

03.

Secure by design

Zero-trust access, data isolation, audit trails, and compliance built into the foundation, everywhere.

04.

Start small, prove fast, scale

We land a high-value pipeline or use case, demonstrate impact quickly, then expand across the estate.

Platforms & tooling

Built on what your team already trusts.

AWS Azure GCP Terraform Pulumi Ansible Kubernetes Docker Helm Istio GitHub Actions GitLab CI Jenkins CircleCI Argo CD Spinnaker HashiCorp Vault Prometheus Grafana OpenTelemetry ELK Snowflake Databricks MLflow Kubeflow Airflow dbt Kafka SageMaker Vertex AI Azure ML Weights & Biases LangChain LangSmith

Where these capabilities apply

Edge-to-Cloud AI across our platforms and services.

Strategy

AI Consulting Services

Define practical AI strategies, identify high-value use cases, and build implementation roadmaps aligned with business goals.

→ ai-consulting

Data

Data Engineering Services

Build scalable data pipelines and integration workflows that deliver reliable, clean, and analytics-ready enterprise data.

→ data-engineering

Data Platform

Modern Data Platform

Create modern data foundations that support analytics, AI workloads, governance, real-time processing, and enterprise scale.

→ modern-data-platform

Analytics

Data Analytics Services

Turn enterprise data into actionable insights through analytics, dashboards, reporting, business intelligence, and AI-driven analysis.

→ data-analytics

Governance

Data Governance Services

Establish trusted enterprise data with governance frameworks covering quality, ownership, security, lineage, and compliance.

→ data-governance

Operations

AI Operations

Operate and optimize enterprise AI systems with monitoring, governance, lifecycle management, observability, and continuous improvement.

→ ai-operations

IT Operations

AIOps Solutions

Use AI-driven operational intelligence to improve monitoring, detect issues faster, automate responses, and optimize IT environments.

→ aiops-solutions

Development

AI Development Company

Design and develop production-ready AI solutions that integrate enterprise data, applications, models, and business workflows.

→ ai-development

Why XFactr

Senior architects. Outcomes, not hours.

Senior architects not juniors lead every engagement. We sell outcomes, not hours. AI isn’t an add-on to how we run delivery; it’s engineered into the pipeline, the platform, and every system we ship. Fewer clients, maximum focus, enterprise-grade from day one.

Connect With us

Want to build as part of its DevOps and MLOps services?

Tell us about the Product, the protocols involved, and what you’re hoping to learn from the data.

We’ll think through the architecture with you on the first call.