Capabilities
We engineer AI systems across the full stack — from data pipelines and model training to inference infrastructure, retrieval systems, agents, and evaluation. No layer is out of scope.
Before writing a line of code, we help you build the right AI strategy. We run technical discovery sessions, model landscape reviews, build-vs-buy analyses, and risk assessments to ensure your AI investment translates into measurable business value.
We work with engineering-first clients — startups defining their AI product foundations, and enterprises evaluating how LLMs, fine-tuned models, and AI agents fit into existing systems. We don't produce slide decks and disengage. We stay through delivery.
We design and build complete generative AI systems — from prompt engineering and chain-of-thought optimisation to structured output pipelines, function calling, tool-augmented LLMs, and multi-turn dialogue systems that run reliably in production.
Our LLM engineering covers the full lifecycle: system prompt design, API integration, safety guardrails, output parsing, fallback logic, and latency-aware deployment strategies across cloud and on-premise environments.
Inference cost and latency are the most underestimated problems in AI deployment. We build inference systems that are low-latency, high-throughput, and cost-efficient — with continuous batching, quantisation, speculative decoding, and GPU memory optimisation applied systematically.
We design API abstraction layers that allow switching between model backends without application changes, and serving clusters that scale gracefully under variable load.
General-purpose models rarely win in specialised domains. We fine-tune, align, and post-train foundation models on your data, in your domain, with your constraints — using parameter-efficient methods that keep compute costs manageable without sacrificing quality.
We build golden datasets, design evaluation frameworks, and establish domain benchmarks before training begins. Post-training is not just a one-time pass; we iterate based on evaluation results and downstream task performance.
We design retrieval-augmented generation systems that go far beyond naive top-K embedding search. Our RAG systems use hybrid retrieval, query rewriting, contextual compression, cross-encoder reranking, and knowledge graph integration to return the right context — reliably.
We build complete knowledge pipelines: document ingestion, chunking strategies, embedding model selection, metadata-aware retrieval, semantic caching, and guardrail layers that prevent hallucination and enforce citation accuracy.
We build agentic AI systems that reason, plan, and execute multi-step workflows — with structured tool use, memory systems, and reliable orchestration. Our agents are designed with production constraints around latency, reliability, cost, and observability in mind from day one.
Shipping a model is the beginning. We build the operational infrastructure that keeps AI systems reliable, measurable, and improvable over time: CI/CD for ML, automated evaluation pipelines, drift detection, A/B testing frameworks, and production monitoring.
Great AI starts with great data. We design and build the data foundations that AI systems depend on — modern Lakehouse architectures, streaming pipelines, feature stores, and data quality frameworks built for ML consumption, not just BI dashboards.
How we work
We define success criteria, evaluation frameworks, and baseline benchmarks before writing production code. Every engagement has a quality threshold established at kickoff.
We design for modularity, observability, and upgrade paths from the first session. Production constraints are not a phase-two concern — they shape every decision.
We prefer well-understood, reliable patterns over novel approaches that add complexity without proven benefit. Clever systems that fail silently are worse than boring systems that work.
We deploy to production early and iterate with real data. Every increment is validated against the eval framework. We don't save surprises for final delivery.
Tell us what you're working on. We scope the right engagement and move quickly.