01
RAG Pipelines
Retrieval-Augmented Generation systems that connect LLMs to your proprietary data. Document ingestion, chunking strategies, embedding generation, vector storage, and retrieval tuning, all engineered for accuracy and latency.
Core capability
AI Infrastructure.
End-to-end AI system design and implementation. RAG pipelines, LLM orchestration, vector databases, evaluation harnesses, and production deployment for real-world scale.
Overview
The scope of AI infrastructure.
AI infrastructure is the complete technical stack that allows intelligent capabilities to operate at scale in production environments. It isn't a chatbot feature; it's the system that powers it.
Most organisations encounter AI at the surface: a widget, a demo, a proof of concept. What separates that from genuine business value is the infrastructure underneath: reliable data ingestion, properly indexed knowledge bases, orchestrated model calls, fallback logic, monitoring, and cost management.
We design and build that entire stack. From the first data architecture decision to the last deployment configuration, we treat AI as engineering discipline. Engineered, instead of mystified.
The result is AI that works the way your business actually operates: reliably, transparently, and with room to grow.
What we offer
What we build.
02
LLM Orchestration
Multi-step AI workflows with tool use, function calling, and model routing. We build the logic layer that turns a raw LLM call into a reliable, multi-step business process, with fallbacks, retries, and cost controls.
03
AI Agents
Autonomous AI systems that plan and execute multi-step tasks: browsing, writing, calling APIs, managing data. We design agent architectures with appropriate guardrails and human-in-the-loop checkpoints.
04
Knowledge Systems
Structured knowledge bases, entity graphs, and semantic search systems. We turn scattered documents, PDFs, emails, and databases into a queryable, AI-navigable knowledge layer for your organisation.
05
AI-Native Apis
Backend APIs designed for AI consumption: structured outputs, streaming responses, tool schemas, and semantic endpoints. Infrastructure that AI agents and LLM apps can reliably call and integrate with.
06
Observability & Evals
LLM tracing, cost dashboards, quality evaluation pipelines, and regression testing for AI systems. Because production AI without monitoring is just an expensive liability waiting to surface.
How it works
How we deliver.
01
Architecture First
Before any model call or API key, we map your data landscape, define retrieval strategies, and design the system architecture. Getting this right determines everything downstream: speed, cost, accuracy.
02
Data Preparation
We clean, structure, and embed your data into vector stores with appropriate chunking strategies. Most AI projects fail here, because poorly prepared data produces confidently wrong answers. We treat this phase with engineering rigour.
03
Core System Build
RAG pipelines, orchestration layers, API design, and integration work, all built iteratively with regular evaluation checkpoints. Quality gates at every stage, instead of only at the end.
04
Evaluation & Tuning
Systematic testing of retrieval accuracy, response quality, and latency. We measure before we ship, and we document the metrics so you can hold future improvements to the same standard.
05
Production Deployment
Containerized deployment with monitoring, alerting, and cost controls. We don't hand off a zip file. We deploy, observe, and stay engaged through the critical early-production period.
∞
AI that survives contact with users.
If you've prototyped something promising but can't get it to production, or you need an honest read on whether AI is the right tool, that's what we're here for.
Tell us what you're building