AI features that work in production. Cloud bills that don't surprise you.
There's a large gap between an AI demo that impresses in a pitch deck and an AI feature your users actually rely on. We close that gap — designing LLM integrations with fallbacks, cost controls, and evaluation loops, and deploying them on cloud infrastructure that scales without billing surprises. We've seen what goes wrong when this is done carelessly. We build it carefully.
What's included
- LLM & RAG integration for production
- AI workflow & process automation
- Cloud architecture & migration (AWS/Azure/GCP)
- AI chatbot & virtual assistant development
- MLOps & model serving infrastructure
- Data engineering & warehouse architecture
- Cloud cost optimisation & FinOps
- Cloud security hardening & compliance
How we deliver
- 1AI feasibility & use-case assessment
- 2Cloud blueprint & architecture design
- 3Integration build & testing
- 4Observability, monitoring & alerting
Technologies we use
- OpenAI API
- Anthropic Claude
- LangChain
- LlamaIndex
- AWS
- Azure
- GCP
- Terraform
- Pinecone
- Weaviate
- Redis
- Kubernetes
Why Origin for AI & Cloud Integration
AI built for production, not demos
Fallbacks, structured outputs, evaluation pipelines, and cost controls are designed in from the start — not bolted on when the demo breaks in production.
Cost-conscious cloud architecture
We tag every resource, right-size instances, and set up billing alerts before anything goes live. Cloud cost surprises happen when nobody is watching — we watch.
Cloud-agnostic recommendations
We don't have AWS or Azure partnership incentives to meet. We recommend the provider that actually fits your workload and your organisation.
How we can work together
Choose the engagement model that fits your situation.
Fixed Scope Integration
A defined AI or cloud integration project — feasibility, architecture, implementation, and monitoring setup — delivered to a fixed scope.
Best for: Well-defined AI features or cloud migration projects
Managed Cloud
Ongoing infrastructure management — monitoring, cost optimisation, security patching, and scaling — on a monthly retainer.
Best for: Teams that want their cloud managed without building an ops team
AI Consulting
Technical advisory on AI strategy, model selection, data pipeline design, and responsible AI practices — without a full implementation commitment.
Best for: Organisations evaluating AI investments before committing to a build
Industries we serve
“Our previous attempt at adding AI ended up costing three times what we expected and didn't work reliably. Origin's approach was completely different — they designed the failure modes before the happy path. The feature has been running in production for six months without an incident.”
Frequently asked questions
- We want to add AI to our product — where do we start?
- With a feasibility and use-case workshop. We map your product against proven LLM use cases (document Q&A, summarisation, classification, generation, extraction) and identify where AI adds real value versus where it adds complexity without payoff. Most products have one or two high-value AI applications — not ten. We help you find them before you build anything.
- How do you handle AI hallucinations in a production feature?
- With system design, not hope. For factual use cases (document Q&A, data extraction), we use retrieval-augmented generation (RAG) to ground responses in your actual data. We add structured output schemas so the model can't produce arbitrary responses. We build evaluation pipelines to catch regressions. And we design the UX to set appropriate expectations — AI confidence signals, human review queues for high-stakes outputs.
- Which cloud provider do you recommend — AWS, Azure, or GCP?
- AWS is our default — the broadest service catalogue, the most mature tooling, and the largest talent pool if you ever hire internally. Azure is the right call if your organisation is already deep in Microsoft (Active Directory, Office 365, Dynamics) and wants single-vendor management. GCP makes sense for data-heavy workloads with BigQuery at the centre. We work across all three and won't push you towards one for commercial reasons.
- How do you stop our cloud bill from spiralling?
- By designing for cost from the start. Right-sizing instances, using spot/preemptible capacity for batch workloads, implementing auto-scaling with sensible floors, tagging every resource so costs are attributable, and setting up billing alerts. We also run a monthly cost review during the first six months. Cloud bill surprises are almost always the result of things nobody was watching — we make sure someone is watching.
- Can you migrate our on-premise infrastructure to the cloud without downtime?
- Yes — but we won't oversell how smooth it is. Cloud migrations always surface undocumented dependencies, licensing issues, and performance differences that only appear at scale. We use a phased approach: non-critical workloads first, then stateful systems with blue-green cutovers, then decommission. You stay running throughout. The timeline depends on how complex and how well-documented your current setup is.
Specialisations
Dive deeper into specific areas of AI & Cloud Integration.
LLM Integration & RAG Development
AI features that work reliably in production — not just in the demo.
AI Workflow Automation
Automate the work your team does every day — reliably, not experimentally.
Cloud Architecture & Migration
Cloud infrastructure that scales cleanly and costs what it should.
AI Chatbot & Virtual Assistant Development
Chatbots that answer questions accurately — not that deflect them creatively.
MLOps & AI Infrastructure
ML models that get to production — and stay reliable once they're there.
Data Engineering & Analytics
Trustworthy data, in the right place, at the right time.
Cloud Cost Optimisation
Cut your cloud bill without cutting your reliability.
Cloud Security & Compliance
Security posture your auditors accept and your engineers can maintain.