When new tokens launch onchain, finding the contract address is only the beginning.
The harder problem is turning raw blockchain activity into structured, reliable, real-time data that other systems can actually use.
That's what I'm building right now: a real-time token scanner for Robinhood Chain, starting with token activity around Pons.
The basic pipeline is:
Chain events → ingestion → decoding → normalization → filtering → actionable data
The individual steps are straightforward.
Making the entire pipeline reliable is where the interesting engineering problems begin.
The Architecture
The first version is intentionally simple:
Robinhood Chain
│
▼
Event Listener
│
▼
Event Decoder
│
▼
Token Normalizer
│
▼
Database / Cache
│
▼
Scanner API
│
▼
Alerts / Analytics
Each layer has a specific responsibility.
The listener deals with the chain.
The decoder turns raw events into application-level information.
The normalizer creates a consistent internal representation.
The database provides persistence and querying.
The API exposes the resulting data to downstream systems.
Keeping these responsibilities separated makes the system easier to test, debug, and extend.
1. Listening for Onchain Activity
The first challenge is detecting relevant activity as it happens.
A scanner shouldn't need to repeatedly ask:
"Did anything happen?"
Instead, the system should consume blockchain events continuously.
Conceptually:
New block
↓
Relevant event
↓
Decode
↓
Validate
↓
Store
For a real-time system, the listener also needs to handle the unhappy paths.
Connections can drop.
RPC providers can temporarily fail.
The same event can potentially be observed more than once.
A production scanner therefore needs reconnection and retry handling from the beginning rather than treating them as future optimizations.
2. Decoding Events
Raw blockchain events aren't particularly useful to an application by themselves.
The next step is decoding them into structured fields.
The process looks roughly like:
Raw Event
↓
Contract Identification
↓
Event Identification
↓
Parameter Decoding
↓
Validation
↓
Structured Record
At this stage, correctness matters more than cleverness.
If an event is incorrectly interpreted, every downstream component is working with bad data.
That's why validation belongs close to the ingestion layer.
3. Normalizing Token Data
Once events are decoded, they need to be converted into a consistent internal representation.
A simplified token record might contain:
Token
├── address
├── chain
├── creator
├── timestamp
├── liquidity
├── volume
└── metadata
The exact schema can evolve.
The important principle is that raw blockchain complexity shouldn't leak into every downstream component.
Normalization gives the rest of the system a stable interface.
It also makes it easier to add additional data sources later.
4. Filtering the Noise
A chain can produce a lot of activity.
Not every contract or transaction deserves to become a scanner result.
The filtering layer can handle things such as:
- contract validation
- liquidity thresholds
- activity thresholds
- metadata availability
- incomplete records
- malformed data
- duplicate detection
One architectural decision is particularly important here:
the scanner should not be the trading strategy.
The scanner answers:
What is happening?
A separate strategy layer can answer:
What should I do about it?
Keeping those concerns separate makes the infrastructure reusable.
5. Making Event Processing Idempotent
Event-driven systems need to assume that duplicate processing can happen.
Instead of relying on:
"This event will only arrive once."
the ingestion pipeline should be designed to be idempotent.
In practice, that means processing the same event again shouldn't create duplicate logical records or corrupt state.
A simplified approach is to derive a deterministic identifier from the chain data and enforce uniqueness at the persistence layer.
Event
↓
Deterministic ID
↓
Already processed?
├── Yes → Ignore
└── No → Process
This becomes particularly important after restarts or when multiple workers are processing data concurrently.
6. Reliability Before Optimization
It's tempting to focus on latency first.
But a scanner that is extremely fast and occasionally misses events isn't particularly useful.
I'd rather establish:
correctness → recoverability → observability → optimization
before trying to optimize every millisecond.
That means thinking about:
- reconnect handling
- retry policies
- idempotent writes
- checkpoints
- structured error logging
- health checks
- basic metrics
The system should be able to recover from ordinary failures without requiring manual intervention.
7. Real-Time Processing
Once ingestion is reliable, latency becomes interesting.
The target pipeline looks roughly like:
Onchain Event
↓
Listener
↓
Decoder
↓
Normalizer
↓
Persistence
↓
API / Alert
Every stage adds some amount of latency.
That means performance isn't simply about making one function faster.
It's about understanding where the time is actually being spent.
Potential sources include:
- RPC response latency
- event decoding
- database writes
- serialization
- network calls
- downstream processing
Profiling is therefore more useful than guessing.
8. Concurrency
A real-time scanner naturally becomes a concurrency problem.
The system may need to:
- process multiple events
- perform independent lookups
- write records
- update caches
- serve API requests
Those operations shouldn't unnecessarily block each other.
Asynchronous processing and bounded concurrency can help here.
The important part isn't simply maximizing parallelism.
It's controlling it.
Unbounded concurrency can turn a temporary spike in chain activity into an internal overload.
9. What This Infrastructure Enables
Once raw blockchain activity has been converted into reliable structured data, many downstream applications become possible.
Token Detection
↓
Structured Data
├── Search
├── Analytics
├── Alerts
├── Monitoring
└── Trading Systems
The scanner itself doesn't need to make trading decisions.
That's deliberate.
A clean data layer can support multiple consumers without coupling the ingestion system to one particular strategy.
10. What's Next
The first version is focused on getting the data pipeline right.
From there, the system can evolve toward:
- richer token metadata
- historical token analytics
- real-time alerts
- token activity scoring
- more efficient indexing
- API access
- additional onchain signals
The interesting part isn't just detecting a new token.
It's building infrastructure that can reliably understand what happened onchain and make that information available quickly.
That's the direction I'm exploring with Robinhood Chain and Pons.
The scanner is only the first layer.
