Our Services - Quarkifi Consulting

Data Engineering

Modern businesses generate enormous volumes of data from transactions, IoT sensors, user interactions, and external APIs. Without a well-designed data infrastructure, that data is noise. We design and build the pipelines, schemas, and platforms that turn your raw data into reliable, queryable assets.

Whether you're starting from scratch or untangling a legacy spaghetti of scripts, we bring structure, observability, and scalability to your data layer.

Streaming pipeline design with Apache Kafka and Flink
Batch processing architecture using Apache Spark and Hadoop
Data lakehouse design on AWS S3, GCS, or Azure Data Lake
Data quality frameworks, validation, and SLA monitoring
Metadata management and data catalogue implementation
Schema design for OLAP (BigQuery, Snowflake, Redshift)
Orchestration with Apache Airflow or Prefect
DataOps CI/CD pipelines for infrastructure-as-code

Streaming Pipeline Active

Kafka → Spark → BigQuery • 12k events/sec

Data Quality Check

99.7% completeness • 0 schema violations

Airflow DAG Run

14 tasks succeeded • Duration: 4m 32s

Late Data Alert

Source: orders_api • SLA: 5 min exceeded

Storage Optimisation

Parquet partitioning • 62% cost reduction

AI Model Evaluation

Deploying an AI model without rigorous evaluation is a business risk. We provide independent, structured assessments of your machine learning and large language models — covering accuracy, robustness, fairness, latency, and production readiness.

We work with LLMs (GPT, Claude, Llama), vision models, tabular ML, and time-series forecasting systems. Our evaluations are reproducible, documented, and actionable.

LLM benchmark design and automated evaluation harnesses
Hallucination rate analysis and factual grounding assessment
Bias and fairness audits across demographic slices
Latency profiling and inference cost modelling
Model drift detection and monitoring framework design
Confusion matrix deep-dives and error analysis
Retrieval-Augmented Generation (RAG) pipeline evaluation
A/B test design for model comparison in production

LLM Accuracy Score

MMLU: 87.4% • HumanEval: 82.1%

Hallucination Rate

3.2% on domain corpus • Flagged for mitigation

Latency P95

420ms • Throughput: 340 req/s

Bias Analysis

2 demographic disparities • Action plan generated

Production Readiness

17/20 checks passed • 3 recommendations

Cloud Deployment Analysis

Cloud bills growing faster than your business? Services provisioned but underutilised? Security posture unclear? We analyse your cloud footprint across AWS, GCP, and Azure to give you an honest picture — and a clear roadmap to optimise it.

Our analysis covers cost, performance, reliability, security, and operational excellence — aligned to the AWS Well-Architected Framework and equivalent GCP/Azure frameworks.

Multi-cloud cost analysis and right-sizing recommendations
Reserved Instance and Savings Plan optimisation
Container workload analysis (ECS, GKE, AKS)
Serverless vs. containerised deployment trade-off analysis
IAM and security posture review (CIS benchmarks)
Network topology and egress cost reduction
Disaster recovery and multi-region strategy design
Infrastructure-as-Code migration (Terraform, CDK)

Cloud Spend This Month

$18,420 • 28% over budget

Savings Identified

$6,200/month • Right-sizing + RIs

Idle Resources

14 instances • 3 unused load balancers

Security Score

72/100 • 5 high-severity findings

Post-Optimisation Forecast

$12,200/month • 34% reduction

Architecture Review

Is your system ready for 10x traffic? Is your microservices mesh actually helping or hurting? Are you accumulating the kind of tech debt that will cost you dearly in 18 months? Our architecture reviews are independent, candid, and focused on what matters for your specific growth stage.

We review system diagrams, code repositories, deployment configurations, and observe production behaviour to give you a complete picture with a prioritised remediation plan.

Scalability and bottleneck analysis under projected load
Microservices vs. monolith trade-off assessment
Event-driven architecture design and review
API design quality and versioning strategy
Database selection and query performance review
Tech debt cataloguing and prioritised remediation roadmap
Observability and alerting completeness review
Resilience testing strategy (chaos engineering)

Single Point of Failure Found

Auth service • No replica & no circuit breaker

DB Query Review

3 N+1 queries • 2 missing indexes

Scalability Assessment

Handles 8x current load • Bottleneck at cache layer

Tech Debt Score

Medium • 12 items prioritised

Modernisation Roadmap

Q1: Auth | Q2: Cache | Q3: DB sharding

Data ETL Processes

The quality of your analytics is only as good as your data pipelines. We design and build ETL (Extract, Transform, Load) and ELT processes that are reliable, testable, versioned, and observable — so you can trust every number in your dashboards.

From legacy SSIS migrations to modern dbt-based transformation layers, we cover the full spectrum. We treat your data transformations as production software — with tests, documentation, and deployment pipelines to match.

Source system analysis and extraction strategy design
dbt model design — staging, intermediate, and mart layers
Incremental load and CDC (Change Data Capture) implementation
Data quality tests integrated into the CI/CD pipeline
Schema evolution strategy and backward compatibility
Master data management and deduplication logic
SLA monitoring and alerting on pipeline failures
Migration of legacy ETL tools to modern cloud-native stacks

dbt Run Complete

248 models built • 0 errors • 12m 04s

CDC Stream Active

Debezium → Kafka → Snowflake • 340ms lag

Data Quality Alert

customers.email: 1.2% null • Threshold exceeded

Schema Version

v3.4.1 deployed • Backward compatible

Lineage Graph

12 sources → 248 models → 34 reports

IoT & Device Engineering

From a single Raspberry Pi prototype to a fleet of 10,000 Arduino-based sensors in the field, we design, build, and connect IoT solutions end-to-end. We bridge the gap between embedded hardware and cloud platforms so your devices are always online, secure, and delivering actionable telemetry.

Our device engineering expertise spans firmware development, PCB selection, communication protocols (MQTT, CoAP, LoRa), gateway design, and cloud ingestion — with over-the-air (OTA) update capability built in from day one.

Raspberry Pi system design — OS, peripherals, headless deployment
Arduino & ESP32/ESP8266 firmware development in C/C++
Sensor integration: temperature, humidity, pressure, accelerometers, cameras
MQTT broker setup (Mosquitto, EMQX) and topic architecture design
LoRaWAN and low-power wide-area network deployments
Edge AI: TensorFlow Lite model deployment on Pi and microcontrollers
OTA firmware update pipeline design
Device management, fleet monitoring, and remote diagnostics

Device Fleet Online

142/144 devices active • 2 offline alerts sent

MQTT Telemetry

Temp: 24.3°C • Humidity: 61% • 30s interval

Edge AI Inference

TFLite on Pi • 94ms latency • 91% accuracy

OTA Update Pushed

Firmware v2.3.1 → 138/142 devices updated

Cloud Ingestion

AWS IoT Core → Kinesis → S3 • Zero loss

From Architecture to Deployment
Technology That Delivers

Data Engineering

AI Model Evaluation

Cloud Deployment Analysis

Architecture Review

Data ETL Processes

IoT & Device Engineering

Quarkifi Stream — Real-Time IoT Intelligence, Zero Code

Not sure which service fits your challenge?

From Architecture to DeploymentTechnology That Delivers

Data Engineering

AI Model Evaluation

Cloud Deployment Analysis

Architecture Review

Data ETL Processes

IoT & Device Engineering

Quarkifi Stream — Real-Time IoT Intelligence, Zero Code

Not sure which service fits your challenge?

From Architecture to Deployment
Technology That Delivers