AI Infrastructure

The Gap Between Demo and Production

Getting a language model to generate impressive output in a notebook takes an afternoon. Getting that same model to serve 10,000 concurrent users with consistent latency, reasonable cost, and reliable outputs takes serious infrastructure work. That's where most teams get stuck.

We've built AI systems that handle millions of inference requests daily. The patterns that work in production look nothing like the quickstart tutorials.

What We Build

Inference Infrastructure

Model serving at scale requires thoughtful architecture:

• GPU orchestration — scheduling inference workloads across GPU instances, managing cold starts, balancing cost against latency SLAs
• Batching strategies — dynamic batching that groups requests to maximize throughput without exceeding latency budgets
• Model versioning — deploying new model versions with canary routing, A/B testing responses, and automated rollback when quality metrics degrade
• Multi-model serving — running multiple models on shared infrastructure, routing requests based on complexity, cost, and capability

The goal is predictable performance at manageable cost. Most teams we work with reduce their per-inference cost by 40-60% through proper infrastructure optimization, without sacrificing response quality.

RAG Architectures

Retrieval-augmented generation is the most practical pattern for production AI systems that need to work with proprietary data. We build RAG pipelines that are:

• Fast — sub-200ms retrieval through optimized vector indexes, hybrid search combining semantic and keyword matching
• Accurate — chunking strategies tuned to your content, re-ranking models that surface the most relevant context, evaluation frameworks that catch retrieval failures before users do
• Maintainable — automated ingestion pipelines that keep your knowledge base current, version-controlled embedding configurations, clear monitoring for retrieval quality over time

Data Pipelines

AI systems are only as good as their data. We build pipelines that:

• Ingest reliably — handling schema changes, partial failures, and late-arriving data without manual intervention
• Transform efficiently — feature computation that runs in minutes, not hours, using the right tool for the job (stream processing for real-time features, batch for historical)
• Serve consistently — feature stores that provide the same feature values in training and inference, eliminating the training-serving skew that silently degrades model performance

Cost Management

AI infrastructure costs can spiral quickly. GPU instances are expensive, and inference costs scale linearly with traffic. We help teams build cost awareness into their AI systems from day one:

• Right-sizing GPU allocation — matching instance types to model requirements, using spot instances for batch workloads
• Caching layers — semantic caching that serves identical or near-identical requests from cache instead of running inference again
• Model distillation — when appropriate, training smaller models that approximate the behavior of larger ones at a fraction of the inference cost
• Usage monitoring — dashboards that show cost per request, cost per user, and cost per feature so your team can make informed trade-offs

Our Approach

We don't start with the model. We start with the problem. What does your product need the AI system to do? What latency is acceptable? What's your budget per inference? What happens when the model gets it wrong?

Those constraints shape every infrastructure decision. A real-time recommendation system has completely different requirements than a batch document processing pipeline, and trying to use the same architecture for both is how you end up with a system that's too expensive for one and too slow for the other.

Technology Choices

We're model-agnostic and cloud-agnostic. We've deployed systems on:

• Open-source models (Llama, Mistral) on self-hosted GPU infrastructure for teams that need data privacy or cost control
• API providers (OpenAI, Anthropic, Google) for teams that want to move fast and can accept the vendor dependency
• Hybrid architectures that route different request types to different providers based on complexity, cost, and latency requirements

The right answer depends on your constraints. We'll help you figure out which ones matter most.