00Agentic 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.
Designed by engineers with 15+ years of end-to-end data projects.
- 18agent roles
- 4layers: platform, data, ML, AI
- 3hyperscale clouds
- 15+years of data projects
The workforce: 18 agent roles in four layers
One team, seen two ways: by the layer of the platform it works on, or by where it sits in the lifecycle. Every role does a human job and hands back something your team can read, review and merge.
Fig. 01 — Four layers, stacked. Platform is the base; AI sits on top.
AI
Assistants and agents on your governed data.
KNWKnowledge AgentThe knowledge engineer
Prepares documents, embeddings and retrieval indexes for AI applications.
Hands backRetrieval indexes, source coverage report
Works with
Business lines
AIEAI AgentThe AI engineer
Builds assistants and agent workflows on your governed data.
Hands backAI services with their evaluation suites
Works with
Business lines
EVLEvaluation AgentThe evaluator
Tests models and AI applications against evaluation sets and flags regressions.
Hands backEvaluation reports, regression findings
Works with
Business lines
ML
Features, training and models in production.
FEAFeature AgentThe feature engineer
Builds and maintains the feature store that models train and serve from.
Hands backFeature pipelines, feature documentation
Works with
MLEML AgentThe ML engineer
Builds training pipelines and runs experiments.
Hands backModel candidates with an evaluation report
Works with
Business lines
MLOMLOps AgentThe MLOps engineer
Deploys models, watches drift and triggers retraining.
Hands backRelease records, drift reports
Works with
Data
Pipelines, models, quality and governance.
MIGMigration AgentThe migration lead
Reads your legacy warehouse — SQL, stored procedures, ETL jobs — and rebuilds it on the target platform.
Hands backConverted models, row-level reconciliation report
Works with
PIPPipeline AgentThe data engineer
Builds and maintains ingestion and transformation pipelines as reviewed pull requests.
Hands backdbt models, orchestration DAGs, pull requests
Works with
ANAAnalytics AgentThe analytics engineer
Models the semantic layer, answers business questions and drafts dashboards.
Hands backAnswers with the query shown, dashboards
Works with
Business lines
QAQuality AgentThe data quality engineer
Writes the tests nobody has time for and watches freshness and anomalies.
Hands backTest suites, data quality findings
Works with
GOVGovernance AgentThe data steward
Catalogs, classifies sensitive data, documents lineage, checks policy on every change.
Hands backCatalog entries, lineage, policy findings
Works with
Business lines
PRFPerformance AgentThe performance engineer
Finds slow queries and jobs and tunes them.
Hands backTuning pull requests with the query plans
Works with
RFXRefactor AgentThe maintainer
Pays down technical debt: unused models, duplicated logic, overdue upgrades.
Hands backCleanup pull requests, deprecation plan
Works with
Platform
The cloud foundation everything else runs on.
ARCArchitect AgentThe solution architect
Turns your requirements into a target architecture on AWS, Azure or Google Cloud.
Hands backReference architecture, decision records
Works with
Business lines
INFInfrastructure AgentThe platform engineer
Provisions environments, networking and identity as code.
Hands backInfrastructure-as-code pull requests, runbooks
Works with
Business lines
SECSecurity AgentThe security engineer
Sets up access, secrets and network policy, then checks every change against them.
Hands backAccess model, policy findings
Works with
Business lines
SREReliability AgentThe on-call engineer
Watches pipelines and platform, triages incidents, keeps the runbooks current.
Hands backIncident notes with root cause, fixes as pull requests
Works with
Business lines
FINFinOps AgentThe FinOps analyst
Tracks cloud cost, finds the expensive workloads, proposes the fix.
Hands backCost report, right-sizing pull requests
Works with
Business lines
Start with one role where the work is safe to hand over. Add the next when your team trusts the first.
How it works: four steps
- Step one
Assess
We map your platform, your backlog and where agents can safely take work.
- Step two
Deploy
Agents get a role, tools and guardrails inside your own cloud tenant.
- Step three
Supervise
Your team approves. Every action is logged and reversible.
- Step four
Scale
Add roles as trust grows. Our engineers stay accountable for the outcome.
Guardrails: why an enterprise can say yes
- G1
Runs in your environment
Your tenant, your data, your access model.
- G2
Human approval on every change
Agents propose through pull requests and tickets. People merge.
- G3
Everything is auditable
Each action, query and decision is logged.
- G4
Platform-native
Works with the tools you already run. No new lock-in.
One workforce. All three hyperscale clouds.
The workforce works natively on AWS, Microsoft Azure and Google Cloud — single-cloud, multi-cloud or hybrid — 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 source on any cloud or on-prem
- dbt
- Trino
- Apache Iceberg
- Apache Spark
- Apache Flink
- Apache Superset
- Prefect
- Apache NiFi
- Keycloak
- MLflow
Deployment Data solutions at any scale, anywhere, any tool
- SaaS
- PaaS
- IaaS
- On-prem
Company: engineers who stand behind it
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. NEXT generation solutions for a new ERA
- Established
- 2020
- Years of end-to-end data projects
- 15+
- Industries
- 01e-Commerce
- 02Finance
- 03Telco
- 04FMCG
- 05Manufacturing
Also delivered by our engineers
Consultancy, for the work that still needs a team in the room.
- 01Cloud data transformation & migration
- 02Data architecture & engineering
- 03DWH modeling, re-engineering, re-factoring
- 04Data lakehouse
- 05Data governance & data quality
- 06Business intelligence
- 07Near-realtime warehousing
- 08DataOps & MLOps
- 09Customer analytics & forecasting
- 10Fraud detection & computer vision
Talk to us
- Office
- Fenerbahçe Mah. İğrip Sk. No: 13/1
Kadıköy, 34726 İstanbul, TürkiyeOpen in Maps ↗