Production AI on AWS. From strategy to shipped.
For technology leaders who need AI running in production, not another slide deck. The people who scope your system are the people who build it, deploy it and keep it fast.
- Anthropic Partner
- AWS Partner Network member
- 60+ years combined experience
Engagement axes
- Phase
- 4 / 4 · Scale & optimize
- You get
- Telemetry, evals and cost tuning
- Team
- Same senior teamConstant
- Handoffs
- 0Constant
Four phases. The same people in every one.
Most AI projects stall at the handoff between the firm that writes the strategy and the team that has to build it. We don’t hand off. Pick a phase to see what exists at that point.
| Phase | You get | Team | Handoffs |
|---|---|---|---|
| Discovery & strategy | Ranked use cases and a roadmap with target metrics | Same senior team | 0 |
| We audit your data and infrastructure, rank the AI use cases worth building and set a roadmap with target metrics. | |||
| Architecture & prototype | A working prototype on your own data | Same senior team | 0 |
| We design the cloud topology, choose the foundation models and build a working prototype that proves performance on your data. | |||
| Fine-tuning & deployment | A production system on AWS | Same senior team | 0 |
| We fine-tune on your proprietary data, set up vector index pipelines and deploy secure services into production on AWS. | |||
| Scale & optimize | Telemetry, evals and cost tuning | Same senior team | 0 |
| Continuous telemetry, latency and cost tuning, and automated model evaluation keep the system improving after launch. | |||
Three disciplines, one team.
Each discipline has a place on the axis. The same people carry the work from one to the next.
-
Strategy
Strategic AI consultingPhase 1
Feasibility roadmaps, ROI modelling and architecture designs tied to your core business goals.
-
Models & RAG
Custom model & RAG developmentPhases 2–3
Fine-tuning models such as Claude and Llama, and retrieval-augmented generation engines built on your own data.
-
AWS infrastructure
AWS cloud infrastructurePhases 3–4
Secure, cost-optimized architecture on Amazon Bedrock and SageMaker for AI workloads that have to stay up.
Our own product, in production.
VarnaGold is till and stockroom software for gold jewellery stores, designed, built and run by Day One Cloud. Each morning, staff photograph their display cases and VarnaGold checks every piece against the books in about six minutes.
The film opens on the VarnaGold name and shows gold priced by the gram, staff photographing each display case before opening while AI counts the pieces and names which ones are missing, a photo of the scale becoming a weight, typing a sale the way staff say it, and the rest of the shop: missing item analysis, karat board, cash drawer, reports, and English and Arabic. It closes on “Every gram, accounted for.”
VarnaGold
- Built for
- Gold jewellery stores in Canada
- The hard part
- Reading every piece off 14 morning photos and reconciling it with the day’s sales and trade-ins
- Stack
- Claude · AWS
- Also ships
- Pricing per karat at the local gold rate, GST/PST, trade-ins, staff PINs, on any device
- Team
- Day One Cloud, strategy to production
60+
Engineers and strategists for the AI era.
We are cloud architects, machine learning engineers and strategists. You talk to the people doing the work, from the first call to the production release.
Our name is our operating principle: every day is Day One. The technology moves too fast for a partner that stops learning.
- Anthropic PartnerBuilding on Claude models
- AWS Partner Network memberBuilding on Amazon Bedrock and SageMaker
What CTOs ask us first.
Straight answers to the questions that come up in every first call.
What AI consulting services does Day One Cloud provide?
Day One Cloud provides end-to-end strategic AI consulting, custom LLM fine-tuning, retrieval-augmented generation (RAG) architecture and AWS cloud infrastructure optimization for high-scale applications.
How does Day One Cloud work with Anthropic and AWS?
We are an Anthropic Partner and an AWS Partner Network member. We build on Claude models and Amazon Bedrock to deliver secure, production-ready AI systems.
How long does a typical AI integration project take?
It depends on scope, so we don’t quote a timeline before we understand the work. We scope it with you in the first call, and the strategy roadmap includes a phase-by-phase plan.
How is client data privacy and security handled for AI models?
Security is built in from the first phase. Data is encrypted at rest and in transit, access is governed by role-based permissions, and we apply zero data retention.
Book a Day 1 call.
Tell us what you want running in production. We’ll reply by email to set up a first conversation with the people who would do the work.
Or write to hello@dayonecloud.com