A team moves its AI prototype into a cloud-hosted application.
AWS & ML engineering
AWS AI engineering, from prototype to production.
An AI application needs more than a model. We build the data, cloud, deployment, and monitoring foundations that support its day-to-day operation, informed by AWS-certified expertise.
WHAT WE BUILD
Built to run beyond the proof of concept.
- AWS application and AI architecture
- Data pipelines and model deployment
- Monitoring and evaluation workflows
- Access controls and cost visibility
POSSIBLE APPLICATIONS
What this could look like for your team.
Illustrative scenarios. We shape the solution around your requirements.
A document pipeline connects storage, processing, retrieval, and an application API.
An existing ML workload gains repeatable deployment and operational monitoring.
A CLEAR SCOPE
A working system. A proper handover.
Your proposal defines the deliverables, acceptance criteria, dependencies, and ongoing support.
- Architecture and infrastructure plan
- Deployment and environment configuration
- Evaluation and observability setup
- Documentation and operational handover
WHAT WE ASSESS FIRST
The context behind a useful implementation.
These are discovery inputs, not prerequisites for your first conversation. We identify gaps and dependencies with you.
- Current application, cloud, data, and deployment context
- Operational, security, and ownership requirements
- An agreed reliability, evaluation, and handover need
A clear path to production
Good engineering starts with understanding.
Your workflow comes first. The technology follows.
01
Understand your business
We learn how your team works, where the friction is, and what a useful outcome looks like.
02
Design the right solution
We map the workflow, assess your data, and agree the scope, integrations, and success criteria.
03
Build, test, refine
You see working progress. We test with real scenarios, including the exceptions and human handovers.
04
Launch with a plan
We deploy, document, and hand over your system, with support and improvement options agreed together.
BEFORE YOU BUILD
AWS RAG architecture: a production planning checklist
Plan a retrieval-augmented generation system on AWS: knowledge ingestion, access-aware retrieval, evaluation, cost controls and operational handover.
Which AWS certifications does your team hold?
Our expertise includes AWS Certified Machine Learning – Specialty and AWS Certified Cloud Practitioner. These individual credentials support our engineering capability; solution architecture is selected around the requirements of each engagement.
Let’s make it work for you
Big possibilities. A practical first step.
Tell us where you want your business to go. We’ll explore how AI can help you get there.