Backed by Y Combinator
Utilize Skilled Developers for Secure Data Evaluation
Efficiently replicate any Postgres database in less than 6 seconds, enabling coding agents to validate their code in a production environment.
Features
Try out coding agents on actual data
without any worries!
$ ardent branch create feature-pricing-logic ✓ Branch 'feature-pricing-logic' created and checked out in 5.4s $ claude Claude Code v2.1.63 Opus 4.6 · Claude Max ~/Documents/ArdentAI/The_Build/mono > Use the branch to validate the sensor alert code functions as expected in production ? for shortcuts Connecting to branch feature-pricing-logic... postgresql://...routing.tryardent.com:5432/testdb5432/testdb Running sensor alert validation suite... ✓ Alert triggers fire at correct thresholds (temp > 85°F) ✓ Escalation logic chains properly (warning → critical → page) ✓ Cooldown periods respected — no duplicate alerts within 5m ✓ Edge case: rapid sensor fluctuation handled without false positives ✓ 9,845 historical readings re-evaluated — all alerts match expected All tests passed. Feature is safe to merge to production.
$ psql -c "SELECT pg_size_pretty(pg_database_size('prod'));"pg_size_pretty(pg_database_size('prod'));" 1.2 TB $ ardent branch create perf-testing ✓ Branch 'perf-testing' created in 4.7s Source: 1.2 TB production database Storage used: 0 MB (copy-on-write) Compute: dedicated container $ ardent branch info perf-testing Branch: perf-testing Status: ready Tables: 847 Rows: 2.4B Size: 1.2 TB (zero additional storage) Endpoint: postgresql://...routing.tryardent.com:54325432
$ ardent branch create backfill-missing-regions ✓ Branch 'backfill-missing-regions' created in 5.4s $ claude Claude Code v2.1.63 Opus 4.6 · Claude Max ~/app > Find all orders with NULL region_id and backfill them from the stores table Scanning for NULL region_id across 847K orders... Found 12,403 rows with missing region data Backfilling via stores.region_id join... ✓ 12,403 rows updated ✓ Referential integrity validated — 0 orphaned FKs ✓ Spot check: 50 random samples all correct Production rows modified: 0 $ ardent branch diff backfill-missing-regions + 12,403 rows changed 0 deleted 0 schema changes
$ ardent branch create feature-pricing-logic ✓ Branch 'feature-pricing-logic' created and checked out in 5.4s $ claude Claude Code v2.1.63 Opus 4.6 · Claude Max ~/Documents/ArdentAI/The_Build/mono > Use the branch to validate the sensor alert code functions as expected in production ? for shortcuts Connecting to branch feature-pricing-logic... postgresql://...routing.tryardent.com:5432/testdb5432/testdb Running sensor alert validation suite... ✓ Alert triggers fire at correct thresholds (temp > 85°F) ✓ Escalation logic chains properly (warning → critical → page) ✓ Cooldown periods respected — no duplicate alerts within 5m ✓ Edge case: rapid sensor fluctuation handled without false positives ✓ 9,845 historical readings re-evaluated — all alerts match expected All tests passed. Feature is safe to merge to production.
$ ardent branch create feature-pricing-logic ✓ Branch 'feature-pricing-logic' created and checked out in 5.4s $ claude Claude Code v2.1.63 Opus 4.6 · Claude Max ~/Documents/ArdentAI/The_Build/mono > Use the branch to validate the sensor alert code functions as expected in production ? for shortcuts Connecting to branch feature-pricing-logic... postgresql://...routing.tryardent.com:5432/testdb5432/testdb Running sensor alert validation suite... ✓ Alert triggers fire at correct thresholds (temp > 85°F) ✓ Escalation logic chains properly (warning → critical → page) ✓ Cooldown periods respected — no duplicate alerts within 5m ✓ Edge case: rapid sensor fluctuation handled without false positives ✓ 9,845 historical readings re-evaluated — all alerts match expected All tests passed. Feature is safe to merge to production.
$ psql -c "SELECT pg_size_pretty(pg_database_size('prod'));"pg_size_pretty(pg_database_size('prod'));" 1.2 TB $ ardent branch create perf-testing ✓ Branch 'perf-testing' created in 4.7s Source: 1.2 TB production database Storage used: 0 MB (copy-on-write) Compute: dedicated container $ ardent branch info perf-testing Branch: perf-testing Status: ready Tables: 847 Rows: 2.4B Size: 1.2 TB (zero additional storage) Endpoint: postgresql://...routing.tryardent.com:54325432
$ psql -c "SELECT pg_size_pretty(pg_database_size('prod'));"pg_size_pretty(pg_database_size('prod'));" 1.2 TB $ ardent branch create perf-testing ✓ Branch 'perf-testing' created in 4.7s Source: 1.2 TB production database Storage used: 0 MB (copy-on-write) Compute: dedicated container $ ardent branch info perf-testing Branch: perf-testing Status: ready Tables: 847 Rows: 2.4B Size: 1.2 TB (zero additional storage) Endpoint: postgresql://...routing.tryardent.com:54325432
$ ardent branch create backfill-missing-regions ✓ Branch 'backfill-missing-regions' created in 5.4s $ claude Claude Code v2.1.63 Opus 4.6 · Claude Max ~/app > Find all orders with NULL region_id and backfill them from the stores table Scanning for NULL region_id across 847K orders... Found 12,403 rows with missing region data Backfilling via stores.region_id join... ✓ 12,403 rows updated ✓ Referential integrity validated — 0 orphaned FKs ✓ Spot check: 50 random samples all correct Production rows modified: 0 $ ardent branch diff backfill-missing-regions + 12,403 rows changed 0 deleted 0 schema changes
$ ardent branch create backfill-missing-regions ✓ Branch 'backfill-missing-regions' created in 5.4s $ claude Claude Code v2.1.63 Opus 4.6 · Claude Max ~/app > Find all orders with NULL region_id and backfill them from the stores table Scanning for NULL region_id across 847K orders... Found 12,403 rows with missing region data Backfilling via stores.region_id join... ✓ 12,403 rows updated ✓ Referential integrity validated — 0 orphaned FKs ✓ Spot check: 50 random samples all correct Production rows modified: 0 $ ardent branch diff backfill-missing-regions + 12,403 rows changed 0 deleted 0 schema changes
Ensure Timely Delivery while Maintaining Product Integrity.
Replicate any Postgres database in under 6 seconds, enabling coding agents to validate their code in production.
solutions
Built to be Infinitely
Scalable and Fast
Ardent clones load in seconds and are massively storage and
compute efficient even at terabyte scale
30,960x
30,960x
Cloning Speed
30,960X faster cloning per TB
Extreme storage efficiency
Only pay for changes made
Scalable computing for agents
No overprovisioning. Automatic scale to 0
Comparison
Why AI Native Data Teams
Use byts
Why AI Native Data Teams Use byts
Clone Creation
Storage
Compute
Database management
Clone limit
Git style team collaboration
Traditional Replica
Hours / Days
Entire DB per clone
Always on
Manual resizing
15-20
byts
<6s
Only Changes made
What you use
Autoscale
Infinite
CASE study
Built for Teams Moving Fast with
AI at Production Scale
"We used to waste hours testing our AI database code with bad seed files. But with Byts, we can test in seconds and there's no drift from production!"
Roberto Zabalini
CTO Zotta International
Downtime
Release speed
Saved per release
"We used to waste hours testing our AI database code with bad seed files. But with Byts, we can test in seconds and there's no drift from production!"
Roberto Zabalini
CTO Zotta International
Downtime
Release speed
Saved per release
"We used to waste hours testing our AI database code with bad seed files. But with Byts, we can test in seconds and there's no drift from production!"
Roberto Zabalini
CTO Zotta International
Downtime
Release speed
Saved per release
"We used to waste hours testing our AI database code with bad seed files. But with Byts, we can test in seconds and there's no drift from production!"
Roberto Zabalini
CTO Zotta International
Downtime
Release speed
Saved per release
"We used to waste hours testing our AI database code with bad seed files. But with Byts, we can test in seconds and there's no drift from production!"
Roberto Zabalini
CTO Zotta International
Downtime
Release speed
Saved per release
"We used to waste hours testing our AI database code with bad seed files. But with Byts, we can test in seconds and there's no drift from production!"
Roberto Zabalini
CTO Zotta International
Downtime
Release speed
Saved per release
"Previously, we spent countless hours testing our AI database code with faulty seed files. Now, thanks to Byts, we can complete tests in mere seconds without any discrepancies from production!"
Sarah Biskova
CEO Gogo
"In the past, we wasted so much time testing our AI database code using poor seed files. With Byts, we can now run tests in seconds, ensuring there's no drift from production!"
Michael Smith
CEO Gogo
"We used to spend hours on end testing our AI database code with subpar seed files. But with Byts, testing takes just seconds, and there's no drift from production!"
Luna Stone
CEO Gogo
"We often wasted hours testing our AI database code with inadequate seed files. Now, with Byts, we can test in seconds and avoid any drift from production!"
Zabareta
CEO Gogo
"Before, we spent hours testing our AI database code with ineffective seed files. Thanks to Byts, we can now test in seconds without any drift from production!"
Konde Modor
CEO Gogo
"We used to lose hours testing our AI database code with unreliable seed files. But now, with Byts, we can test in seconds, ensuring no drift from production!"
John Borton
CEO Gogo
Ship Fast and Don't Break Things
Efficiently replicate any Postgres database in less than 6 seconds, enabling coding agents to validate their code in a production environment.
Ship Fast and Don't Break Things
Efficiently replicate any Postgres database in less than 6 seconds, enabling coding agents to validate their code in a production environment.
byts
Quickly duplicate any Postgres database in less than 6 seconds, allowing developers to test their code in a production-like environment.
© 2026 byts
byts
Quickly duplicate any Postgres database in less than 6 seconds, allowing developers to test their code in a production-like environment.
© 2026 byts
byts
Quickly duplicate any Postgres database in less than 6 seconds, allowing developers to test their code in a production-like environment.
© 2026 byts