Vibe Coding, Demonstrated: We Rebuilt 1981's Defender From One Prompt
We handed an AI agent a one-page brief and got back a playable remaster of the 1981 arcade classic Defender. Play it, read the full prompt, build your own.
read →We architect AI systems the way good engineers actually want them built.
Hyper Mind Technologies designs and ships generative AI, agentic systems, and Azure cloud platforms for startups and scale-ups — with the rigor a CTO would apply if they had the time.
01
// how we ship AI
Same ops discipline. New class of workload.
The playbook that moved mailboxes, virtualized farms, and held 99.95% VDI estates now points at retrieval, agents, and evals — production first, demos second.
step 01
Scope the failure modes
What must never hallucinate, who approves irreversible actions, where the data may not go.
step 02
Architecture + contracts
RAG boundaries, tool surfaces, identity, cost and latency budgets — written before the feature flag.
step 03
Evals and traces
Goldens, regression gates, OpenTelemetry. If you cannot measure it, you cannot ship it.
step 04
Handoff you can run
IaC, runbooks, ownership. We leave systems your team maintains — not a black box retainership.
02
// practices
Six disciplines drawn from one playbook.
PRACTICE 01
Generative AI Solutions
RAG, fine-tuning, evals, prompt design, model selection (Grok, Claude, GPT) — engineered for the load, not the demo.
spec →PRACTICE 02
Intelligent Automation
Durable workflows plus Copilot Cowork, Claude Cowork, and Grok on the desk work — LLMs where they help, deterministic rules where they don't.
spec →PRACTICE 03
Azure Cloud Architecture
Landing zones, Bicep / Terraform IaC, AKS, well-architected reviews, FinOps.
spec →PRACTICE 04
Cloud & AI Security
Threat modeling for LLM systems, prompt-injection defense, IAM, SOC 2 / ISO 27001 readiness.
spec →PRACTICE 05
AI Agents & Agentic Systems
OpenClaw, Hermes, LangGraph, Azure AI Foundry, Copilot Cowork, Claude Cowork. Day-to-day business automation with evals, tools, and human-in-the-loop.
spec →PRACTICE 06
Modernisation & Advisory
Architecture reviews, build-vs-buy, hiring scorecards, executive briefings — grounded in 16 years of enterprise migrations.
spec →03
// signals
What the work measures up to.
AI systems
prod
RAG and agentic systems shipped behind feature flags with eval gates — not demo-only prototypes.
migrations
15k+
Mailboxes and workloads moved to Microsoft 365 and Azure — the discipline behind today's AI work.
availability
99.95%
Sustained on VDI and unified-communications estates serving 10,000+ concurrent users.
finops
35%
Average infrastructure cost reduction across hybrid and cloud-native migration programs.
04
// selected work
Proof across three eras.
AI · automation · ops
AI on the boring office work — so owners get back to growing the business
The highest-ROI AI work is rarely the flashy chatbot. It is the repetitive office load — intake, document triage, invoice and form extraction, status chase, first-draft replies — that quietly burns owner and admin hours every week. We put proven copilots and cowork agents (Copilot Cowork, Claude Cowork, Grok where it fits) on that grind so leadership could spend time on expansion, customers, and hiring instead of copy-paste ops.
case study →AI · agents · openclaw · hermes
OpenClaw and Hermes agents that actually run day-to-day business work
Open-source agent platforms like OpenClaw and Hermes are no longer demos — they can research, draft, schedule, update systems, and chase routine work on a loop. The opportunity for businesses is real: day-to-day activities that used to need a human at the keyboard can be delegated safely. The risk is also real: unbounded tools, weak identity, and no evals. We help teams pick the right agent stack, wire it to real workflows, and ship it under control — so agents help the business instead of becoming another unmanaged intern with production credentials.
case study →heritage · m&a · tenant-to-tenant
Friday: two companies. Monday: one Microsoft 365 tenant
Imagine Friday you still have two separate companies. By Monday morning the acquired firm is live in the parent’s Microsoft 365 tenant — mail flowing under the new identity, historical email and files available, and the old tenant no longer the place work happens. Minimal user-facing downtime; the hard work was everything we pre-staged before the weekend.
case study →AI · agents · evals
Production RAG + agent loop with evals before the feature flag flipped
A product team had a demo that impressed sales and scared engineering. We replaced “prompt hope” with retrieval contracts, golden evals, multi-model selection (Grok, Claude, GPT where each won), and an agent loop that could fail closed.
case study →platform · azure
Azure landing zone and FinOps that cut hybrid infrastructure cost ~35%
Lift-and-shift pressure met a honest sizing exercise: right-size before migrate, retire what the cloud made redundant, and put cost ownership in the architecture — not a quarterly spreadsheet.
case study →heritage · migration
15,000-mailbox Microsoft 365 migration without a weekend firefight
A multi-domain government estate needed out of Exchange 2010 before support ended — with free/busy, mail flow, and identity surviving every cutover wave.
case study →05
// reference stack
What we build on, by default.
PRODUCTYour application UX · feature flags · A/BCLIENT
AGENT LOOPLangGraph · OpenClaw · Hermes · OpenAI Agents SDK · Azure AI Foundry · Copilot Cowork · Claude CoworkHM
EVAL / OBSGoldens · LLM-as-judge · OpenTelemetry · Arize / LangfuseHM
MODELSGrok · Claude · GPT-4o · open-source via Azure / vLLMEXT
DATAPostgres + pgvector · Azure AI Search · Snowflake / DatabricksHM
WORKFLOWTemporal / Azure Durable FunctionsHM
PLATFORMAKS · Bicep / Terraform · GitHub Actions · Azure MonitorHM
SECURITYEntra ID · Key Vault · Private Endpoints · OWASP LLM Top 10HM
06
// notes
From the field — current practice and the archive.
Yearfold, Month Two: Shipping Was the Easy Part
AI compressed the build from a quarter to a month. It did not compress distribution. An honest June update on finding users for a product that works.
read →Building Yearfold: From Idea to a Live Product in 30 Days
On May 2 it was a name in a document. By May 31 it was a live retirement planner running 10,000-path simulations. The honest case study of an AI-built product.
read →07
// faq
Straight answers before the call.
Who is a good fit?
Engineering-led startups and scale-ups shipping AI or Azure platforms, and enterprises that want senior practitioners rather than a pyramid delivery model. If you need a 40-person factory or a pure staff-aug bench, we will say so and point you elsewhere.
Who is not a good fit?
Teams that only want a slide deck, a weekend prototype with no path to production, or the lowest bid on a large RFP. We also decline work outside our competence rather than learning on your critical path.
How do engagements start?
A short written read on fit (within one business day), then a fixed-scope discovery (typically 2–3 weeks) that produces architecture options, risks, and a costed plan. Build work is milestone-based; advisory can be a monthly retainer.
What does discovery usually cost?
Most discoveries land in the lower budget bands on our contact form (often under $30k–$80k depending on system complexity and access). We publish rates in the SOW — no mystery line items.
Who does the work?
A team of four senior experts. Engagements are staffed by people who have shipped similar systems — not juniors supervised from a distance after kickoff.
// next step
Bring us the system you're trying to ship.
We reply within one business day with a short, honest read on whether we're the right team for it — and what a discovery would actually look like.