AI, Data, & Systems Engineering
Build practical AI systems—LLM apps, retrieval, agents, data pipelines, and evaluation frameworks—focused on reliability and ROI.
Practical AI that earns its keep.
We design systems that are measurable, controllable, and maintainable—so they keep working when the hype cycle shifts.
TL;DR
- Build LLM apps with retrieval and tool use (RAG + agents).
- Evaluate performance: golden sets, hallucination rates, cost/latency benchmarks.
- Design data flows: ingestion, processing, embedding, serving.
- Operate at scale: inference infra with knobs for cost and control.
- Make it safe, observable, and vendor-portable.
What we build
- LLM applications with retrieval (RAG), tool use, and task routing.
- Evaluation frameworks using golden sets, hallucination detection, and cost/latency SLOs.
- Data pipelines for training, fine-tuning, or real-time inference.
- Embedding stores and vector search infrastructure.
- Inference systems deployed on cloud or on-prem, with controls for budget, performance, and usage.
Architecture principles
Determinism where possible, probabilistic where useful.
When you can't predict the output, at least predict the behavior of the system.Guardrails-first design.
Tool limits, schema validation, retries, circuit breakers—because even the smartest model is only half the system.Observability is not optional.
Track token usage, latency, quality metrics, and error modes. What gets measured gets managed.Modular and vendor-portable.
No lock-in. Abstractions for LLMs, vector stores, and pipelines so you can swap pieces without rebuilding.
Why it matters
As Rory Sutherland reminds us, not everything that makes sense in theory makes sense to people.
AI needs more than intelligence—it needs affordances. Interfaces that respect human context. Systems that behave, not just predict.
And as Deming said, “If you can’t describe what you are doing as a process, you don’t know what you’re doing.”
We engineer the process around the model—so it can be tested, repeated, and improved.
Who this is for
Teams building:
- Production LLM tools or copilots
- Evaluation systems to separate signal from noise
- Vector-native search or discovery experiences
- Cost-aware, safety-sensitive AI infrastructure
Related capabilities
- UI Engineering for LLM interfaces
- Behavioral modeling for AI product design
- Marketing & Analytics integrations
- Customer Experience Strategy for AI touchpoints
AI doesn’t need to be magical. It needs to be useful.
That starts with systems that can be trusted, understood, and improved.