On a Tuesday morning, a credit card processor notices a wave of suspicious activity. Dozens of small charges are being tested against stolen cards. Each charge is only a few dollars, but within minutes the fraud could scale into thousands in losses if not stopped immediately.
Predictive AI is commonly used to detect fraud in almost every financial institution, but that is limited to a small set of very defined use cases on discrete sessions. Cross session analysis, which is very computationally intensive, has been very difficult to do before the advent of hosted Generative AI models. Using a self-hosted AI Agent, this can now be achieved.
With every transaction the model is asked a simple question: “Is this suspicious?” On its own, the model cannot know. Its training data is frozen months in the past and does not contain these card numbers, merchant IDs, or devices. What matters is not the training set but the live context. Were there other transactions seconds ago from the same IP address? Did the same device issue repeated charges against different accounts? If the system can answer these questions in real time, the cross-session fraud can be stopped as it happens. If not, the opportunity is gone.
If you’ve ever tried to build a real-time Generative AI system, the architecture will feel familiar. Transactions go into Postgres or MySQL. Streams run through Kafka or Flink. Analytics land in Snowflake or BigQuery. Embeddings are generated asynchronously and pushed into Pinecone or Weaviate then the hosted Generative AI model api is called. Logs are dumped into Elastic or OpenSearch. On paper, each tool is “best-in-class.” In code, it quickly becomes a mess.
For a developer, the pain is obvious: you’re not building fraud detection, you’re building plumbing. And the more plumbing you add, the less you trust the system to stop fraud in the milliseconds where it matters.
For the business, complexity is an unavoidable complication. Each of these systems has to be scaled, tuned, and monitored independently. Engineers must manage schema drift across databases, model drift across ml algorithms, job failures in pipelines, and synchronization between stores. Glue code becomes as large as the AI itself. The operational burden grows with every new feature, turning data freshness into a moving target rather than a guarantee.
Fragility is inherent. The more moving parts in the system, the more ways it can fail. If Pinecone is slow, embeddings cannot be retrieved. If Snowflake lags, aggregates are out of date. If a Kafka job crashes, downstream systems silently drift apart. Orchestration frameworks attempt to mask these failures but often become bottlenecks themselves. The architecture may function in controlled demos, but in live environments it struggles to deliver.
This also leads to feature and product lock in. Changing the system even just to patch one aspect can cause a chain reaction of failures that are difficult to recover from so ‘CHANGE NOTHING’ becomes the mantra. In the high-tech world, if you are standing still, you are falling behind. Soon your more agile competitor is beating you at every turn.
At some point, every engineer working on these systems has the same thought: “Why am I gluing five databases together just to answer one question?” You don’t actually care whether the answer comes from Postgres, Snowflake, or Pinecone. You care that when the model asks “Is this suspicious?”, it can see everything that matters — the transactions, the device, the IP address, the past history — instantly and in context.
That’s what a context lake gives you: one system designed to handle transactional events, time-series streams, logs, and embeddings in the same place, without fragile pipelines in between. Instead of juggling sync jobs, you get a single query layer where all the context is already fresh and aligned.
From a developer’s seat, the benefits are immediate:
For the developer, the shift is subtle but huge: you’re no longer building duct tape pipelines that simulate “real-time AI.” You’re actually giving the model live context at the moment it needs it. The system moves from demo-ready to production-grade.
Great — here’s how we can extend the developer-perspective section with a concrete, dev-facing fraud detection flow. I’ll keep it practical, like something you’d actually sketch out when evaluating a system:
A Developer’s View: Fraud Detection in a Context Lake
Here’s what it looks like in practice. In the old stack, you’d be juggling queries across Postgres, Kafka, Snowflake, and Pinecone just to answer a simple fraud question. In a context lake, it’s one query.
Say a new transaction comes in:
{
"card_id": "1234-5678-9012-3456",
"amount": 4.99,
"merchant_id": "M-482",
"device_id": "D-991",
"ip_address": "10.42.0.88",
"timestamp": "2025-08-27T13:22:01Z"
}
Instead of firing off five lookups, you write a single query:
SELECT t.card_id,
COUNT(*) FILTER (WHERE t.timestamp > now() - interval '60 seconds'
AND t.ip_address = '10.42.0.88') AS recent_ip_activity,
COUNT(*) FILTER (WHERE t.timestamp > now() - interval '60 seconds'
AND t.device_id = 'D-991') AS recent_device_activity,
VECTOR_SIMILARITY(t.embedding, $suspicious_patterns) AS anomaly_score
FROM transactions t
WHERE t.card_id = '1234-5678-9012-3456'
OR t.device_id = 'D-991'
OR t.ip_address = '10.42.0.88';
One query does three things at once:
All of it happens in milliseconds because the context lake holds the rows, the events, and the embeddings in the same system. No Kafka lag, no Snowflake delay, no Pinecone async jobs.
The output might look like this:
{
"card_id": "1234-5678-9012-3456",
"recent_ip_activity": 14,
"recent_device_activity": 22,
"anomaly_score": 0.91
}
From here, the fraud agent has live context:
Answering “Is this transaction suspicious?” becomes trivial — because the system surfaces the right context in real time. This is just one example with numeric examples. The context could literally be anything that can be sent to the AI Agent for it to infer whether any combination of context inputs could constitute fraud.
Fraud prevention is only one domain where Tacnode changes what is possible. In customer support, the Context Lake holds account histories, tickets, and knowledge base articles in one place, allowing an AI agent to deliver responses that are both personalized and current. In compliance, Tacnode ingests regulatory updates and contract changes as they occur, ensuring legal copilots query the most up-to-date information rather than stale indexes. In each case, the story is the same: stitched-together toolchains introduce latency, staleness, and fragility, while Tacnode provides a single, real-time substrate where models can retrieve truth at inference.
Generative AI cannot succeed in production on the strength of models alone. Without real-time context, answers remain brittle, generic, and untrustworthy. Multi-tool stacks try to provide context by piecing together databases, warehouses, and vector stores, but the result is latency, complexity, and staleness. Tacnode replaces that patchwork with a Context Lake: one system that ingests continuously, stores flexibly, and retrieves instantly.
If the goal is AI that is accurate, personalized, and grounded in live reality, Tacnode is the architecture that makes it possible.