Intelligent retry engine that recovers 95% of failed enterprise payments
Teams building fintech products for enterprises suffer transaction failures at scale due to unreliable APIs from payment processors.
RetryForge intercepts failed transactions and retries them exponentially with backoff, processor switching, and idempotency. It learns from past failures to optimize sequences. Fintech teams reduce failures without custom code.
Development teams at fintech companies building payment solutions for enterprise clients
Adaptive retry strategies per processor/error code, with built-in idempotency keys.
friendly
Automatic queuing of failed txns
UI to define retry rules per error
Generates/manages keys to prevent duplicates
Track recovery rates and costs
Retries webhooks too
Test retry strategies
Manual retry batches
Test without real API calls
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| created_at | timestamp | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| name | text | No |
| rules | text | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| strategy_id | uuid | No |
| original_txn_id | text | No |
| attempts | int | No |
| status | text | No |
| updated_at | timestamp | No |
Relationships:
/api/strategiesCreate retry strategy
/api/retries/failedSubmit failed txn
/api/analyticsRecovery stats
Basic rules
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 120 | 2.5% | $135 | $1,620 |
| Month 6 | 900 | 9% | $4,095 | $49,140 |
Smart retries boost success from 90% to 99% with zero effort.
Post case study tweet thread on payment retry pains, convert commenters. Offer free audits to 30 LinkedIn fintech contacts. Launch on PH with beta invites.
Native
Single processor, basic
Multi-processor, customizable
Generic queues
No payment smarts
Tailored for payments
Historical retry data improves algorithms via network effects
Rising txn volumes expose retry gaps; low-code trend favors plug-ins.
Queue backlog under load
Rate limits + scaling
Complex strategies bug-prone
UI validation + tests
Duplicate charge disputes
Strict idempotency
Success: 7/10 have custom code
Success: >90% recovery
Success: 10% referral rate
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