AI and cloud automation, starting from the process
AI agents, workflow automation, SaaS engineering and cloud DevOps that save real hours.
Overview
We start from the process, not the model. We find the steps where people re-key data, read documents, chase approvals or answer the same questions, then apply the simplest reliable technology, which is sometimes AI and sometimes plain workflow automation, and measure the time saved.
For software companies and internal product teams, we engineer SaaS platforms with the foundations that matter later: tenancy, security, billing, observability and infrastructure as code.
How it usually goes
Someone in operations tells us three people spend the day typing invoice details into a system. We watch the work for an hour and ask where the mistakes happen. Sometimes the answer is a language model reading the invoices. Sometimes it is a plain script and a rule, no AI at all. We build the smaller thing first, run it next to the manual process, and compare real numbers. A data engineer for a Snowflake move starts the same way: what do you have today, who uses it, what can't go down. Then we work out the order and start with the least risky piece.
What you get
- AI agents & copilots
- LLM-powered assistants grounded in your own data with retrieval-augmented generation.
- Workflow automation
- End-to-end process automation connecting your apps, approvals and data.
- Cloud & DevOps
- Migrations, CI/CD pipelines, infrastructure as code and cost optimization.
- SaaS engineering
- Multi-tenant architecture, subscriptions, SSO and analytics built in.
How a project runs
Discover
Goals, users, systems and constraints, captured in a short discovery.
Design
Architecture, scope and a phased plan with clear costs and milestones.
Build
Working increments every sprint, reviewed with your stakeholders.
Launch
Testing, security review, deployment and handover documentation.
Support
Monitoring, improvements and the team to keep it running.
Frequently asked questions
How do we know which processes are worth automating with AI?
We rank candidate processes by volume, time per task, error cost and data availability. The best first projects are frequent, rules-heavy and measurable, so you see a return quickly and can expand with confidence.
Is our data safe when using AI models?
We design automations so your data stays within your cloud tenancy or with enterprise model providers under no-training terms, apply role-based access, and log every AI action for review.
Which cloud platforms do you support?
Amazon Web Services, Microsoft Azure and Google Cloud, including containers, serverless, managed databases and infrastructure as code with tools such as Terraform.
Can you build a SaaS product from scratch?
Yes. We take products from discovery and MVP through launch and scale, and can provide the engineering team to keep developing them.
Cloud and AI skills in demand
AWS and Azure are among the five skills named most often in the tech postings we track, and Kubernetes and machine learning both sit in the top dozen.
The Bureau of Labor Statistics projects data scientist jobs to grow 35% from 2025 to 2035, with a median wage of $120,000.
See also: Web & Mobile Development · IT Consulting · Staff Augmentation
Related reading: Closing the IT skills gap without over-hiring
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Have a project in mind?
Call and a recruiter picks up, or send the project and we will call you.
Or call +1 (773) 541-4424, Monday – Friday, 9:00 AM – 5:00 PM Central