Service
AI Engineering & Integration
AI capabilities designed as system components — not bolted on as features. We build inference pipelines, retrieval systems, and model integrations that are reliable, observable, and production-grade.
The Problem We Solve
AI that breaks under production conditions.
The most common failure mode in AI integration is treating model inference as a reliable, stateless API call. Under real production conditions — concurrent requests, latency constraints, token budget pressure, and retrieval quality degradation — systems that were not architected for these realities break in ways that are difficult to diagnose and expensive to fix.
Prompt engineering is not a substitute for architecture. A RAG system without well-designed chunking, retrieval tuning, and re-ranking will produce inconsistent results regardless of how the prompt is written. An LLM integration without fallback strategies and response validation will become a reliability liability at scale.
We design AI systems the same way we design any other backend: with defined interfaces, observable behavior, failure modes identified in advance, and performance characteristics validated before deployment.
What We Deliver
AI capabilities engineered for production.
LLM Integration Architecture
Provider-agnostic integration layers, model routing logic, latency budget design, token cost management, response validation, and fallback strategies for when model providers degrade or fail.
RAG System Design
Document ingestion pipelines, chunking strategy, embedding model selection, vector database architecture, hybrid retrieval, re-ranking, and context window management optimized for retrieval quality.
Inference Pipeline Engineering
Async inference pipelines, request batching, streaming response handling, model output caching, and structured output enforcement. Built to perform under concurrent load, not just in demos.
Self-Hosted Model Serving
GPU-accelerated inference on Kubernetes with vLLM or TGI, model quantization strategies, autoscaling based on queue depth, and multi-model serving for cost and latency optimization.
AI Observability
Prompt and completion logging, latency and cost tracking per request, retrieval quality metrics, user feedback integration, and evaluation pipelines for continuous quality monitoring in production.
AI Agent Architecture
Multi-step agent orchestration with tool use, memory management, and guardrails. We design agent systems that are deterministic enough to be trustworthy in production — not just impressive in a demo.
Data & Embedding Pipelines
Structured and unstructured data ingestion, real-time and batch embedding generation, vector store synchronization, and semantic search backends integrated with existing application data layers.
Ideal Client Profile
Who this engagement is for.
This service is the right fit if you are building an AI-powered product or adding AI capabilities to an existing platform — and you need engineers who understand both the AI layer and the backend systems it depends on.
You may have a prototype that works in testing but degrades under load, produces inconsistent results, or carries unacceptable latency in production. You may be evaluating self-hosted models for cost or data residency reasons. Or you may be building a net-new AI product and want to design the system correctly from the start.
In all cases, you need engineers who treat AI as an engineering discipline — not a collection of API calls wrapped in optimism.
AI capabilities that hold up in production.
Describe your system and what you're trying to build. We'll tell you where the architectural risks are and how we would approach them.