Applied AI
Retrieval pipelines, agent systems, structured extraction, fine-tuning — scoped against an eval set, not a vibe.
Services
Engineering, end to end.
Three practices, one standard: if we build it, we can run it — and we can hand you the runbook that proves it.
What we take on
Data & Platform
The substrate models feed on: pipelines, vector stores, feature systems, governance that survives an audit.
Cloud Infrastructure
Landing zones, Kubernetes, IaC, networking and the cost model — architected before the first workload lands.
MLOps & Serving
Model CI, registries, canary deploys, GPU scheduling, token-level observability. Where AI meets pager.
Managed Operations
We keep running what we built — SLOs, on-call, upgrades, cost reviews — with a named engineer, not a queue.
Practice · 01
AI Services
We build AI systems against a written definition of "works": an eval set agreed before the first line of code, run on every change, reported without varnish.
Retrieval & RAG systems
Chunking, embedding, reranking, citation. Grounded answers over your corpus with the failure modes measured, not assumed.
Agent workflows
Tool-using agents with permission boundaries, spend caps and audit trails — autonomy budgeted like any other resource.
Evaluation harnesses
The part most teams skip. Golden sets, regression gates in CI, drift monitoring in production.
Fine-tuning & serving
When prompting stops being enough: data curation, training runs, and inference serving with real latency budgets.
Practice · 02
Infrastructure Services
Everything below the model: accounts, networks, clusters, pipelines, dashboards. Defined in code, deployed by machine, explained in writing.
Cloud architecture & landing zones
Multi-account foundations with identity, networking and guardrails designed before workload one.
Kubernetes & compute
Clusters sized for reality, GPU pools scheduled for utilisation, autoscaling tuned against your actual traffic.
CI/CD & IaC
Terraform end to end, progressive delivery, rollback measured in seconds. The console is for reading, not clicking.
Observability & FinOps
Traces that cross the model boundary, SLOs with error budgets, and cost per request on the same pane of glass.
Practice · 03
Managed Operations
The offer that keeps the other two honest: we stay on the pager for what we ship. Monthly, with a named engineer and a standing review.
SLO-backed operation
Availability and latency targets in the contract, error budgets reported monthly, postmortems you can read.
Upgrades & patching
Kubernetes versions, model migrations, dependency CVEs — handled on a cadence, not in a crisis.
Cost stewardship
Quarterly unit-economics reviews. When your bill drops because of our work, you hear it from us first.
How an engagement runs
Weeks, not quarters.
Week 0 — Framing
A paid discovery sprint. Output: a written architecture, an eval plan, a cost model, and a fixed quote for the slice.
Weeks 1–3 — The thin slice
A working path through the whole system on your infrastructure — small, but real: real data, real deploys, real metrics.
Weeks 4–8 — Hardening
Load, chaos, security review, runbooks, cost tuning. The slice becomes a platform.
Ongoing — Operate or hand over
Managed operations with SLOs, or a documented handover with your team trained on the pager. Your call.