NextEraLabs

Agentic workforce for data, ML & AI platforms

Your data & AI platform, staffed on day one.

NextEraLabs puts a full team of production-ready AI agents to work on your data, ML and AI platforms — building, running, developing and continuously improving them end to end, on AWS, Microsoft Azure and Google Cloud.

Meet the workforce Talk to us

18 agent roles4 layers: platform, data, ML, AI3 hyperscale clouds15+ years of data projects

AWSAZUREGOOGLE CLOUDPLATFORM · 5ARCINFSECSREFINDATA · 7ML · 3AI · 3KNWFEAMIGAIEMLEPIPEVLMLOANAQAGOVPRFRFXBUSINESS LINESFinanceSales & MarketingOperationsRisk & ComplianceProductIT
18 roles · 6 business linesFour layers, one connected team

Platform is the base, data and ML are the walls, AI is the roof — and every agent talks to its neighbours and to your business lines. Pick a layer, an agent or a business line.

01 — The workforce

Eighteen roles. Four layers. One platform lifecycle.

Not a chatbot bolted onto your warehouse. A full team that builds, runs, develops and keeps improving your data, ML and AI platform — each role with a job, the tools for it, and a deliverable your team can review.

Layer 01 / 04 · 5 roles

Platform

The cloud foundation everything else runs on.

ARCThe solution architect

Architect AgentBuild

Turns your requirements into a target architecture on AWS, Azure or Google Cloud.

Hands backReference architecture, decision records

Works withINFSECMIGIT

INFThe platform engineer

Infrastructure AgentBuild

Provisions environments, networking and identity as code.

Hands backInfrastructure-as-code pull requests, runbooks

Works withARCSECSREFINIT

SECThe security engineer

Security AgentBuild

Sets up access, secrets and network policy, then checks every change against them.

Hands backAccess model, policy findings

Works withARCINFGOVRisk & Compliance

SREThe on-call engineer

Reliability AgentRun

Watches pipelines and platform, triages incidents, keeps the runbooks current.

Hands backIncident notes with root cause, fixes as pull requests

Works withINFPIPMLOQAIT

FINThe FinOps analyst

FinOps AgentImprove

Tracks cloud cost, finds the expensive workloads, proposes the fix.

Hands backCost report, right-sizing pull requests

Works withINFPRFFinance

Layer 02 / 04 · 7 roles

Data

Pipelines, models, quality and governance.

MIGThe migration lead

Migration AgentBuild

Reads your legacy warehouse — SQL, stored procedures, ETL jobs — and rebuilds it on the target platform.

Hands backConverted models, row-level reconciliation report

Works withARCPIPQA

PIPThe data engineer

Pipeline AgentDevelop

Builds and maintains ingestion and transformation pipelines as reviewed pull requests.

Hands backdbt models, orchestration DAGs, pull requests

Works withSREMIGQAANAFEAPRFRFX

ANAThe analytics engineer

Analytics AgentDevelop

Models the semantic layer, answers business questions and drafts dashboards.

Hands backAnswers with the query shown, dashboards

Works withPIPGOVAIEFinanceSales & MarketingOperations

QAThe data quality engineer

Quality AgentRun

Writes the tests nobody has time for and watches freshness and anomalies.

Hands backTest suites, data quality findings

Works withSREMIGPIPGOV

GOVThe data steward

Governance AgentRun

Catalogs, classifies sensitive data, documents lineage, checks policy on every change.

Hands backCatalog entries, lineage, policy findings

Works withSECANAQAKNWRisk & Compliance

PRFThe performance engineer

Performance AgentImprove

Finds slow queries and jobs and tunes them.

Hands backTuning pull requests with the query plans

Works withPIPFINRFX

RFXThe maintainer

Refactor AgentImprove

Pays down technical debt: unused models, duplicated logic, overdue upgrades.

Hands backCleanup pull requests, deprecation plan

Works withPIPPRF

Layer 03 / 04 · 3 roles

ML

Features, training and models in production.

FEAThe feature engineer

Feature AgentDevelop

Builds and maintains the feature store that models train and serve from.

Hands backFeature pipelines, feature documentation

Works withPIPMLE

MLEThe ML engineer

ML AgentDevelop

Builds training pipelines and runs experiments.

Hands backModel candidates with an evaluation report

Works withFEAMLOEVLOperations

MLOThe MLOps engineer

MLOps AgentRun

Deploys models, watches drift and triggers retraining.

Hands backRelease records, drift reports

Works withSREMLEEVL

Layer 04 / 04 · 3 roles

AI

Assistants and agents on your governed data.

KNWThe knowledge engineer

Knowledge AgentDevelop

Prepares documents, embeddings and retrieval indexes for AI applications.

Hands backRetrieval indexes, source coverage report

Works withGOVAIEProduct

AIEThe AI engineer

AI AgentDevelop

Builds assistants and agent workflows on your governed data.

Hands backAI services with their evaluation suites

Works withANAKNWEVLSales & MarketingProduct

EVLThe evaluator

Evaluation AgentImprove

Tests models and AI applications against evaluation sets and flags regressions.

Hands backEvaluation reports, regression findings

Works withMLEMLOAIERisk & Compliance

02 — How it works

Start with one role. Add the next when you trust the first.

The crews do the work. Our engineers — 15+ years of end-to-end data projects — stay accountable for the outcome.

  1. Step 01

    Assess

    We map your platform, your backlog and where agents can safely take work.

  2. Step 02

    Deploy

    Agents get a role, tools and guardrails inside your own cloud tenant.

  3. Step 03

    Supervise

    Your team approves. Every action is logged and reversible.

  4. Step 04

    Scale

    Add roles as trust grows. Our engineers stay accountable for the outcome.

Runs in your environment

Your tenant, your data, your access model.

Human approval on every change

Agents propose through pull requests and tickets. People merge.

Everything is auditable

Each action, query and decision is logged.

Platform-native

Works with the tools you already run. No new lock-in.

03 — Platforms

One workforce. All three hyperscale clouds.

Single-cloud, multi-cloud or hybrid — the workforce works natively on AWS, Microsoft Azure and Google Cloud, and on the open-source stack underneath.

AWS

  • S3
  • Glue
  • Redshift
  • Athena
  • EMR
  • Kinesis
  • Lake Formation
  • SageMaker
  • Bedrock
  • QuickSight

Microsoft Azure

  • Fabric
  • OneLake
  • Synapse
  • Data Factory
  • Stream Analytics
  • Data Activator
  • Power BI
  • Azure Machine Learning
  • Azure AI Foundry

Google Cloud

  • BigQuery
  • Dataflow
  • Dataproc
  • Pub/Sub
  • Dataplex
  • Data Fusion
  • Composer
  • Looker
  • Vertex AI

Open sourceon any cloud or on-prem

  • dbt
  • Trino
  • Apache Iceberg
  • Apache Spark
  • Apache Flink
  • Apache Superset
  • Prefect
  • Apache NiFi
  • Keycloak
  • MLflow

Data solutions at any scale, anywhere, any tool — SaaS · PaaS · IaaS · On-prem

04 — Company

Next generation solutions for a new era.

NextEraLabs Information Technologies was established in 2020 in İstanbul. We are an expert team that has been implementing end-to-end data projects across many sectors for more than 15 years.

Industries

  • e-Commerce
  • Finance
  • Telco
  • FMCG
  • Manufacturing

Also delivered by our engineers

  • Cloud data transformation & migration
  • Data architecture & engineering
  • DWH modeling, re-engineering, re-factoring
  • Data lakehouse
  • Data governance & data quality
  • Business intelligence
  • Near-realtime warehousing
  • DataOps & MLOps
  • Customer analytics & forecasting
  • Fraud detection & computer vision

Contact

Tell us what is stuck in your data backlog.

info@nexteralabs.com
Fenerbahçe Mah. İğrip Sk. No: 13/1
Kadıköy, 34726 İstanbul, TürkiyeOpen in Maps ↗