AI-powered data infrastructure

Production data infrastructure.
On day one.

Describe your pipeline — cloud, source, sink, transforms. Day 1 Stack generates the Terraform, sets up orchestration, and deploys AI agents that handle monitoring, optimization, and troubleshooting while you focus on your product.

See how it works

⚡ Deploy in minutes 🤖 AI-managed ops ☁️ Any cloud

            pipeline.yaml
            
          

# Describe your pipeline in plain YAML
pipeline:
  name: events-to-warehouse

  source:
    type: postgres
    tables: [events, users, sessions]

  sink:
    type: bigquery
    dataset: analytics_prod

  cloud:    gcp
  schedule: "@hourly"
  transforms:
    - normalize_events
    - deduplicate
    - enrich_with_user_data
          

The bottleneck

Most startups spend months on infrastructure before they run a single useful query.

Setting up a production data pipeline — cloud infrastructure, ingestion, orchestration, error handling — typically takes a dedicated data engineering team 3 to 6 months. Teams without that expertise take longer, and get it wrong more often.

Day 1 Stack eliminates the bottleneck — so you can move at product speed without needing a dedicated data engineering team on day one. Describe what you need, get production-ready infrastructure code, and let AI agents handle the operational work.

3–6

months average time to production data infrastructure at seed stage

$190k

average annual cost of a senior data engineer in the US

The platform

Deploy. Operate. Scale.

Simple pipeline creation meets intelligent automation. Ship infrastructure in minutes, then let AI agents handle what comes next.

⚡

CEO-Friendly Deployment

Choose cloud, source, sink, and transforms. Get production-ready Terraform or Pulumi with orchestration setup included — no data engineering expertise required.

Your data is in BigQuery before your next standup. No Terraform expertise, no waiting on a data engineer hire.

🤖

AI Agent Suite

Four agents handle monitoring, optimization, issue detection, and natural language pipeline creation — automatically, after deployment.

Your pipeline runs itself. Issues get caught and fixed before your team notices anything is wrong.

☁️

Your Cloud, Your Choice

AWS, GCP, or Azure. Self-hosted Kubernetes. Hybrid or multi-cloud. Works with your existing infrastructure or helps you choose from scratch.

No vendor lock-in baked into day one. Switch clouds with a config change, not a six-month migration.

🔧

Production-Ready Pipelines

Batch and streaming. Snowflake, BigQuery, Redshift outputs. Auto-scaling, error recovery, and cost optimization built in from the start.

Show investors real uptime numbers on day one. This is production infrastructure, not a prototype to replace later.

            terraform/main.tf
            
          

# Generated by Day 1 Stack

resource "google_bigquery_dataset" "analytics" {
  dataset_id = "analytics_prod"
  location   = "US"
}

resource "google_cloud_run_v2_job" "pipeline" {
  name     = "events-to-warehouse"
  location = "us-central1"

  template {
    task_count = 1
    template {
      max_retries   = 3
      timeout       = "600s"
    }
  }
}

resource "google_cloud_scheduler_job" "trigger" {
  name      = "events-hourly"
  schedule  = "0 * * * *"
  time_zone = "America/New_York"
}
            

# Monitoring Agent — live output

pipeline:  events-to-warehouse
status:    ● healthy
last_run:  4 minutes ago  (2.3s)

──────────────────────────────────────
⚠  anomaly detected
──────────────────────────────────────
stage:    normalize_events
latency:  340ms → 1.2s  (3.5× spike)

recommendation:
  Increase worker count:  2 → 4
  Est. cost impact:   +$11 / mo
  Est. latency gain:  ~68% reduction

action: apply? [y/N]
            

# Same config, any cloud

# GCP
cloud:    gcp
region:   us-central1
sink:     bigquery://analytics_prod

# AWS
cloud:    aws
region:   us-east-1
sink:     redshift://prod/analytics

# Azure
cloud:    azure
region:   eastus
sink:     synapse://workspace/analytics

# Self-hosted
cloud:    kubernetes
namespace: data-platform
sink:     snowflake://acct/analytics
            

# Pipeline health — 30-day report

name:    events-to-warehouse
status:  ● running

──────────────────────────────────────
batch
  events / run:  3.2M
  avg duration:  2.3s
  p99 latency:   340ms

streaming
  throughput:    45k events / sec
  consumer lag:  0.8s

reliability
  uptime:        99.94%
  cost / GB:     $0.0023
            

The team

Built by people who've felt the pain.

Day 1 Stack was built by data infrastructure engineers who spent years watching startups struggle with the same bottleneck.

Founder & CEO

Ashutosh Chaudhary

Data engineering and cloud infrastructure leader with 11+ years of experience. Currently heads data engineering at Helika.io. Previously led engineering teams at Zenoti. Has built scalable data systems processing hundreds of millions of events for enterprise clients.

Technical Advisory Board

Seasoned CTOs and infrastructure architects from successful startups and enterprise companies, providing guidance on product direction and technical architecture.

Advisors announced at launch.

Where we are

In active development.

MVP in progress, early infrastructure live, and already accepted into the AWS Activate program.

Development status

MVP in active development

Program membership

Accepted into AWS Activate

Early access

Early access open now

Skip the queue

Get production data infrastructure before you hire a data engineer.

Join the early access list. We're onboarding a small group of startups to shape the product before public launch.

Production data infrastructure.On day one.