DevOps MLOps AIOps LLMOps AgentAIOps GenAIOps DataOps CyberSecurity

Faster Delivery.
Reliable Infrastructure.

We design and implement end-to-end DevOps practices that eliminate the friction between development and operations. From first commit to production - automated, monitored, and repeatable.

  • CI/CD pipeline design with Jenkins, GitLab CI, GitHub Actions
  • Infrastructure as Code using Terraform, Pulumi, CloudFormation
  • Container orchestration with Docker & Kubernetes (EKS, GKE, AKS)
  • Observability: Prometheus, Grafana, ELK Stack, Datadog
  • GitOps workflows with ArgoCD and Flux
  • Disaster recovery and SLA-driven incident response
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CI/CD Pipeline Development

Automated build, test, and deploy pipelines that cut release cycles from days to minutes.

Infrastructure as Code

Version-controlled, reproducible infrastructure across any cloud or hybrid environment.

Containerisation & Orchestration

Kubernetes-native deployments with autoscaling, service mesh, and zero-downtime rollouts.

JenkinsGitLab CIGitHub Actions DockerKubernetesTerraform ArgoCDPrometheusGrafana AnsibleHelmVault
ML Pipeline Automation

Automated data ingestion, feature engineering, training, evaluation, and deployment pipelines.

Model Registry & Versioning

Track experiments, compare model performance, and promote models through staging to production.

Production Monitoring & Drift Detection

Continuous monitoring for data drift, concept drift, and model degradation with auto-retraining triggers.

MLflowKubeflowSageMaker AirflowFeature StoreDVC BentoMLSeldonEvidently AI

Models in Production.
Not in Notebooks.

We bridge the gap between data science experimentation and reliable production ML systems. Your models get versioned, monitored, and continuously improved - at any scale.

  • End-to-end ML pipeline design and automation
  • Feature store setup and governance
  • Model registry, A/B testing, and shadow deployments
  • Data and model drift monitoring with alerting
  • Automated retraining workflows
  • ML governance and reproducibility frameworks
Discuss Your ML Stack

Self-Healing IT.
AI-Powered Ops.

AIOps applies machine learning to IT operations data - correlating events, predicting failures, and automating remediation before incidents escalate. Reduce MTTD and MTTR dramatically.

  • Intelligent alert correlation and noise reduction
  • Predictive anomaly detection on metrics and logs
  • Automated root cause analysis and topology mapping
  • IT event management and ITSM integration
  • Capacity planning and performance forecasting
  • Self-healing runbook automation
Explore AIOps
Alert Correlation & Noise Reduction

Cut alert fatigue by up to 85% using ML-driven event correlation and suppression.

Predictive Incident Management

Detect anomalies in time-series data before they become outages. Predict failure patterns.

Automated Remediation

Runbook automation and self-healing workflows that resolve known issues without human intervention.

DynatraceMoogsoftBigPanda Splunk ITSIServiceNowPagerDuty ElasticDatadog
RAG Pipeline Architecture

Retrieval-Augmented Generation systems with vector stores, chunking strategies, and re-ranking.

Fine-Tuning & Adaptation

Domain-specific model fine-tuning using LoRA, QLoRA, and PEFT techniques for enterprise use cases.

LLM Observability & Evaluation

Track latency, cost, hallucination rate, and output quality across model versions in production.

LangChainLlamaIndexvLLM Vertex AIBedrockPinecone WeaviateLoRA / QLoRALangSmith

LLMs Built to Last
in Production.

Deploying an LLM is the easy part. Making it reliable, cost-efficient, and safe at enterprise scale is where Crecita steps in. We handle the full LLM operations lifecycle.

  • LLM deployment on cloud, on-prem, and edge
  • RAG pipeline design with vector database integration
  • Fine-tuning and instruction tuning workflows
  • Prompt engineering and version management
  • LLM observability: latency, cost, hallucination tracking
  • Model serving optimisation with quantisation and batching
Build Your LLM Stack

Autonomous Agents.
Orchestrated at Scale.

The next frontier of operations is agentic - AI systems that plan, reason, and act across your infrastructure autonomously. We design, deploy, and operate multi-agent frameworks for real enterprise workloads.

  • Multi-agent system architecture and design
  • Autonomous incident detection, triage, and remediation agents
  • Agentic workflows for IT operations and DevSecOps
  • Tool-use and API integration for agents
  • Agent observability, tracing, and guardrails
  • Human-in-the-loop approval workflows
Explore Agentic AI
Multi-Agent Architecture

Design supervisor-worker agent hierarchies that coordinate complex, multi-step tasks across systems.

Tool-Use & API Integration

Equip agents with real-world tools: search, code execution, API calls, databases, and more.

Agent Observability & Safety

Full tracing, logging, and guardrails to ensure agents behave predictably and safely in production.

AutoGenCrewAILangGraph LangChainOpenAI APIAnthropic LangSmithTemporal
Responsible AI Governance

Model cards, bias audits, fairness metrics, and governance frameworks aligned to EU AI Act and enterprise policy.

Enterprise GenAI Deployment

Scalable deployment of generative AI models with cost control, rate limiting, and multi-tenant isolation.

Output Guardrails & Safety

Input/output filtering, jailbreak detection, PII redaction, and policy-enforcement layers for GenAI systems.

Azure OpenAIAmazon BedrockGoogle Gemini Guardrails AINeMo GuardrailsMLflow Weights & BiasesVertex AI

Generative AI.
Governed. Scalable. Safe.

GenAI is moving fast - and most organisations are deploying it faster than they can govern it. Crecita brings operational rigour to generative AI: deployment, monitoring, safety, and compliance.

  • Enterprise GenAI platform deployment and configuration
  • Responsible AI framework implementation
  • Model cards, risk assessments, and bias audits
  • Output safety guardrails and content filtering
  • Cost governance and token usage optimisation
  • Regulatory alignment: EU AI Act, NIST AI RMF
Emerging practice: GenAIOps is a frontier discipline. Crecita is among the first firms to formalise it as a structured service offering - giving our clients a head start on AI governance.
Govern Your GenAI

Data Pipelines That
Actually Work in Prod.

Bad data upstream means broken models downstream. We bring DevOps-style engineering rigour to your data infrastructure - automated pipelines, observable quality checks, and governed lineage from source to consumption.

  • Data pipeline orchestration with Apache Airflow & Prefect
  • Data quality engineering with Great Expectations & dbt tests
  • Data lineage and catalog with OpenMetadata & Atlan
  • Streaming pipelines with Kafka and Flink
  • Lakehouse architecture on Delta Lake, Apache Iceberg
  • Data observability and SLA monitoring
Discuss Your Data Stack
Pipeline Orchestration

Reliable, monitored data workflows that handle failures gracefully and alert on SLA breaches.

Data Quality & Testing

Automated quality gates that catch bad data before it reaches models or dashboards.

Lineage & Cataloguing

Full end-to-end data lineage so every analyst and engineer knows where the data came from and what touched it.

Apache AirflowdbtPrefect Apache KafkaDelta LakeApache Iceberg Great ExpectationsOpenMetadataSpark

ISACA Certified.
Enterprise Grade.

Our cybersecurity practice is backed by resources holding the most respected certifications in the industry. From governance to penetration testing - we protect what matters most.

CISA - Certified Information Systems Auditor
CISM - Certified Information Security Manager
CRISC - Certified in Risk & Info Systems Control
AAIA - Advanced in AI Audit
  • GRC - Governance, Risk & Compliance frameworks
  • Penetration testing (network, web, cloud, API)
  • SOC design, implementation, and operations
  • SIEM setup, tuning, and threat hunting
  • Cloud security posture management (CSPM)
  • AI security - securing ML pipelines and LLM applications
Security Assessment
GRC & Compliance

ISO 27001, SOC 2, GDPR, HIPAA, PCI-DSS compliance programs designed and managed by certified professionals.

Penetration Testing

Red team exercises, web application testing, cloud configuration review, and API security assessments.

SOC & SIEM Operations

24/7 security operations centre setup with Splunk, QRadar, or Microsoft Sentinel - threat detection and response.

SplunkQRadarMicrosoft Sentinel TenableQualysCrowdStrike WizAWS Security HubPalo Alto

Not sure where to start?

Let's have a 30-minute conversation about your infrastructure, AI roadmap, or security posture. No pitch - just honest advice.

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