Retrieval systems that are measured
Hybrid retrieval, version-aware chunking and reranking, with a golden set of real user questions and recall, faithfulness, latency and cost gated in the pipeline on every change.
AI & Cloud Engineering
AI & Cloud Engineering
AI & cloud solutions that survive a security review and a second month in production.
What this engagement actually is
We build LLM systems, data platforms and cloud native architecture for organisations that need measurable quality, predictable cost and an audit trail. AI features are engineered with evaluation harnesses in continuous integration, not shipped on the strength of a demo.
What you end up with
- Retrieval quality measured on a golden set, gated in CI
- Cost per request and p95 latency visible on the same dashboard as quality
- Everything inside your compliance boundary, with evidence for the audit
- A model-agnostic seam, so a provider change is a configuration change
Capabilities
What AI & Cloud Engineering covers
Each of these is staffed by people who have shipped it before. If a capability below is not relevant to your problem, we will take it out of the scope and the price.
Agentic workflows with guardrails
Tool-using systems with bounded permissions, deterministic fallbacks, full traceability of every step and a defined abstention path so the system stops instead of guessing.
Data platforms
Ingestion, lineage, quality checks and access control on top of a warehouse or lakehouse, so the AI layer is built on data somebody is accountable for.
Cloud native architecture
Kubernetes or serverless, infrastructure as code, multi-environment promotion, and cost per request published next to latency so trade-offs are made deliberately.
Platform and delivery engineering
Golden paths for service creation, pipelines that are fast enough to trust, and observability that answers questions instead of producing dashboards.
Delivery process
How the work runs, phase by phase
Dates, deliverables and exit criteria per phase. Nothing here is a placeholder. This is the plan we put in the statement of work.
- 01
Feasibility and evaluation design
Two to three weeks. We define what "good" means numerically, build the golden set from real user questions, and prove or disprove the riskiest assumption before anyone commits to a roadmap.
- 02
Reference architecture
Landing zone, network boundary, identity, secrets and model access designed against your compliance regime, with the security review scheduled early rather than discovered late.
- 03
Pipeline and harness
Retrieval and generation quality wired into continuous integration, with cost and latency reported per change, so every subsequent decision is evidence-based.
- 04
Production rollout
Progressive exposure by cohort, human-in-the-loop where the stakes require it, and monitoring that distinguishes a model regression from a data regression.
- 05
Operate and improve
Ongoing evaluation against a growing golden set, model and provider swaps behind a stable interface, and a monthly cost and quality review that produces backlog items.
Technology
The stack we bring to this work
Defaults, not dogma. If your organisation is standardised on something adjacent, we will work in it and tell you honestly where it will cost you.
- Python
- TypeScript
- FastAPI
- LangGraph
- pgvector
- OpenSearch
- Kafka
- dbt
- Kubernetes
- Terraform
- AWS
- Azure
- GCP
Questions
AI & Cloud Engineering: the questions we get asked first
The answers we would give you on a call, written down. More at the full FAQ page.
No. We deploy inside your tenancy with provider agreements that exclude training on your data, and where policy requires it we run open-weight models on infrastructure you control. The data flow is documented for your audit before the first request is made.
Cost per request is a first-class metric from the first week, with caching, model routing by task complexity and hard budget alarms per environment. We size the cheapest model that passes the evaluation bar rather than defaulting to the largest one available.
Usually not for a first system - a well-scoped retrieval assistant over documents you already have can ship in weeks. You will need one before AI becomes a dependable part of core operations, and we will tell you when that line is approaching rather than after you cross it.
Related services
What teams usually pair this with
Most engagements start in one service line and end up spanning two. Here is the rest of the catalogue.
AI & Cloud Engineering
Ready to talk about ai & cloud engineering?
Tell us the system, the constraint and the deadline. You will get a written response from an architect within one business day, and an honest answer about whether we are the right firm for it.
Direct line:moeed@moreinns.com