Scale your LLM application without losing control of it.
Smicolon redesigns the bottlenecks around model calls, data, concurrency and failure handling so the product can support more users with visible cost and reliability.
Scale from measured constraints
We use production behavior and expected demand to find the limiting parts of the system before changing architecture.
01
01
Measure the workload
We trace latency, model usage, concurrency, token cost, queue behavior and failures across critical user workflows.
02
02
Redesign the bottlenecks
We introduce the right mix of caching, batching, queues, routing, fallbacks and data boundaries for the workload.
03
03
Prove and operate the change
We test realistic load, define operating thresholds and add the signals the team needs to manage scale over time.
Scaling an LLM app is a systems problem
Model choice matters, but throughput, data, product behavior and cost controls decide whether the whole application remains usable.
Latency with context
We optimize the full user workflow, including retrieval, tools, queues and downstream services.
Cost you can explain
Usage signals connect model and infrastructure spend to product workflows and operating decisions.
Resilient model workflows
Fallbacks, timeouts, retries and degraded modes protect user workflows when dependencies fail.
Quality under change
Evaluations and production signals show whether optimization preserves the behavior users need.
What we can improve
Throughput and latency
Concurrency control, asynchronous work, caching, batching and routing around real product demand.
Reliability and fallbacks
Timeouts, retry policies, provider resilience, queue recovery and useful degraded behavior.
Cost and quality visibility
Token and infrastructure attribution, evaluations, traces and thresholds for informed operating decisions.
Understand the limits before users find them.
We will inspect the current workload, bottlenecks and failure patterns, then define the changes that matter most.