Midas Technologies is a US-based software engineering firm. We execute the difficult work — modernizing legacy systems, securing the cloud, and architecting custom AI-native platforms — for organizations that need software built to last, not just shipped.
Most engagements start with a problem, not a capability. Below are the five problem spaces where our engineering, security, and AI work compound most usefully — each one a defined practice with its own approach, methodology, and tech stack.
Old systems that block growth, cost too much to operate, and become harder to staff every year. We modernize them incrementally — without big-bang risk — into cloud-native architectures that are easier to run, easier to secure, and easier for your team to extend.
Learn moreSolution / 02Cloud migration with security built in, not bolted on after. Identity-based access, secure CI/CD, and continuous verification — aligned to OMB M-22-09 and the CISA Zero Trust Maturity Model — engineered from day one.
Learn moreSolution / 03Custom platforms with AI in the architecture, not stapled on. Multi-tenant, secure, production-grade — engineered to support the AI features your business actually needs.
Learn moreSolution / 04Legacy databases consolidated into modern, queryable, governed data platforms — with the pipelines, schemas, and observability that real analytics requires.
Learn moreSolution / 05Generative AI, RAG, and intelligent agents applied to the operational work your team is still doing by hand — integrated into the systems your business already runs on.
Learn moreWe are an engineering firm first. The work we are known for is custom application development, cloud and security architecture, and the AI integration that makes both of those investments compound. Each capability stands on its own; most engagements combine two or three.
We architect complex, scalable applications — Next.js and React on the frontend, Java and .NET on the enterprise backend, Python and Node where they fit best. The kind of software that runs core business operations and does not need a rescue project in three years.
We embed real AI into the workflows your business already depends on. RAG pipelines that work on production data, semantic search over enterprise content, LLM-driven architectures, and the evaluation and observability infrastructure that keeps it reliable past the demo stage.
We migrate workloads to AWS and Azure with Zero Trust architecture and DevSecOps practices in place from the first commit — not bolted on after the first audit. Identity-based access, secure CI/CD, continuous verification, and the runtime observability that catches problems before they escalate.
We move legacy data systems — Oracle, SQL Server, mainframe — to modern, queryable, governed platforms. We design the Python data pipelines that move information from where it is created to where decisions are made, and the schemas that let analytics and AI actually work on top of them.
For clients who need to scale internal capacity quickly, we provide vetted, senior-level technical talent to support active development lifecycles — without disrupting the way your team already works.
Learn moreWe run engagements as managed projects — owning the scope, the build, and the ship. Senior engineers lead from the first conversation through the last commit. Communication is direct, status is clear, and the price is the one we quoted.
We sit with your team, study the systems, and define what success actually looks like. The deliverable is a written assessment with a recommended architecture — yours to keep, even if you decide to walk.
Architecture documented. Threat model in place. A written proposal with scope, timeline, and a price you can plan around — no "to be determined" line items hiding the work we did not want to think through.
Two-week iterations led by senior engineers, with security gates in CI from the first commit. Working software at the end of every cycle. Weekly check-ins with the people writing the code — not a project manager reading a status template.
Documentation written by the engineers who built it. Runbooks for the on-call rotation. Knowledge transfer to your team, or a managed support agreement on the other side — your call. We are good with both paths.
We do better work in industries where the systems are real, the data is messy, and the cost of getting it wrong is high. These four are where our engineering, security, and data work compound most usefully.
The principles below are the ones we hold ourselves to most strictly. They show up in how every engagement is scoped, staffed, and shipped.
The architects and senior engineers on the proposal are the same people on the project. We run engagements as managed delivery — owning scope, schedule, and quality — not as bodies billed by the hour to someone else's plan.
We use AI throughout the engineering lifecycle — code generation, test generation, legacy reverse-engineering, documentation, observability. The result is faster delivery on the same scope, lower cost on the work that matters, and modernization timelines that were not realistic two years ago.
Threat modeling happens at design, not at audit. CI/CD pipelines have security gates from the first commit. Zero Trust is the default architectural posture on every engagement that touches sensitive data or production workloads.
Our proposals are written in language you can read without a glossary. The price is the price. Change orders happen — they always do — and when they come up, they are negotiated openly with the same transparency we bring to the original scope.
Software is not magic. It is craft. The best engineering teams treat it that way — naming the trade-offs honestly, building security in from the start, and shipping work clean enough that the next engineer thanks you instead of starting over.
No intake form theater. Send a paragraph about what you are working on and we will reply within one business day — with a 30-minute call, or with an honest "this is not the right fit; here is who you should call instead."
