Focus & Stack

AWS GCP Azure Kubernetes Terraform Redis Vector Search CI/CD API Design Docker

The engineering that takes AI from “works on my laptop” to “runs in production and the team can operate it.” System design, integrations, and orchestration that makes AI work cleanly inside real software and business environments. Not just making the model call. Everything around it.

What we architect

AI Services Layer

Model routing, prompt management, response caching, rate limiting, usage tracking, cost attribution, failover. Shared infrastructure that multiple AI features can use.

Application Integration

Synchronous request-response, streaming for conversations, async for batch operations, event-driven for triggers. Pattern matched to the use case.

Data Pipelines

Document ingestion for RAG, real-time data streams, output storage, feedback pipelines. The infrastructure feeding data to AI and storing results.

Multi-Model Orchestration

Complex workflows with multiple models. Classification by one, generation by another, quality check by a third. Routing, sequencing, parallel execution, error handling.

Vector Search Infrastructure

Embedding model selection, vector database deployment (Pinecone, Weaviate, pgvector), hybrid search, performance tuning.

How it works

Assess—Step 1

Current architecture, AI requirements, constraints, scaling needs.

Design—Step 2

AI services layer, integration patterns, data pipelines, deployment strategy.

Implement—Step 3

Build integrations, deploy to existing infrastructure.

Monitor & Scale—Step 4

Observability, cost tracking, scaling plan.

What you get

  • Architecture documentation with decision records
  • Implemented infrastructure deployed to your environment
  • API documentation and integration guides
  • Monitoring and observability setup
  • Operational runbooks
  • Cost modelling and scaling plan

Case Study

AI — Architecture — Integration

Sentient
Core integration and launch infrastructure for an AI-native platform — the architecture and integration layer beneath smart contracts, distribution and on-chain coordination.

Taking AI from prototype to production?

The gap between a working demo and a production system is authentication, load balancing, error recovery, cost tracking, latency monitoring, and graceful degradation. We bridge that gap.