Shaidow Technologies builds software for the operational problems organizations learn to work around: manual review that consumes days of staff time, institutional knowledge that cannot be located when it is needed, and processes that depend on a single person. We apply AI where it measurably improves the outcome, validate it against your own data, and deliver a system your team can operate and maintain.
Start a projectPractice areas we ship to production, from the first prototype through handoff.
AI systems that answer from your own knowledge with lineage back to the source, reasoning across text, images, and the structured data buried in your operations. Deployed wherever it needs to run: in the cloud, on-premise, or on secure infrastructure where the cloud is not an option.
Infrastructure that scales with your usage instead of your bill. Managed pipelines, on-demand compute for model serving, and services that ship the same way every time so nothing breaks between staging and production.
Full-stack websites and applications built end to end: a marketing site that loads fast and reads like your company, clean APIs, interfaces your team will actually use, and data pipelines that turn messy sources into structured knowledge you can query, visualize, and act on.
Cross-team execution that keeps engineering, stakeholders, and leadership aligned on the same outcome. Proposal and solution-architecture authoring for when the work has to be pitched before it can be built.
Applied AI delivered as outcomes. Voice agents, agentic workflows, and retrieval over your own data, from scope to a working POC in days, not a two-week planning deck. Iterated against real feedback, hardened toward production, and handed off running so your team owns what ships.
Custom models trained on your own data when a general-purpose model does not cut it, and honest side-by-side comparison so you actually know which option fits the job. On-premise and edge deployment for latency-sensitive environments and for places where the cloud is not the answer.
Four steps, in order, with something working in your hands early enough to change course.
A working session, not a discovery phase. We leave with the problem stated plainly, the data identified, and a target you will recognize when you see it.
A working proof of concept in days, running against your real data. Not a deck and not a mock. Something you can put in front of the people who would actually use it.
Evaluated against real inputs, failure modes closed, security and deployment constraints met. The prototype becomes a system your operations can lean on.
Documented, containerized, and running in your environment. Your team owns what ships. A monthly retainer is available when you would rather keep us on call for iteration, monitoring, and the next problem on the list.
Tell us the problem in plain language. If it is something we can build, you will hear back with how we would approach it and what the first week looks like.
Send a message