Vyrenzo takes an idea from a rough brief to a working system — design, engineering, and AI, built as one continuous thing. Web platforms, RAG systems and AI agents, shipped with guardrails and a human in the loop. You see real screens in week one, not a slide deck.
Most of what goes wrong in software happens in the gaps between teams. We close those gaps by keeping design, engineering, and strategy inside one head, not three departments.
Research, wireframes, and high-fidelity screens built alongside working code — so what you approve is close to what ships.
Full-stack builds — dashboards, permissioning, data pipelines, integrations — engineered to hold up past the first demo.
PRDs, architecture decisions, and design systems that keep a product coherent as it grows past its first version.
We build AI into real systems, not demos. Assistants that answer from your own documents, agents that take actions inside your tools, and deep agents that plan and finish long, multi-step work — every one shipped with guardrails and a human in the loop.
Search and Q&A over your PDFs, SOPs, contracts and databases. Every answer cites the source it came from — and when the answer isn't in your data, the system says so instead of guessing.
AI wired into your tools — reading email, extracting data from documents, updating records, triggering workflows. Every action is scoped by permissions and logged, and anything that changes your data waits for a person to approve it.
For work that takes many steps: agents that break a goal into a plan, hand sub-tasks to specialised sub-agents, keep working notes, and pause at checkpoints for a person to sign off — research, reconciliation, report drafting.
AI drafts, a person decides. Output lands in a review screen beside the source; someone checks, corrects and approves it before anything is saved.
Models are told to copy, never guess. Every answer is checked against a schema like untrusted input, and failed checks block approval until they're fixed.
Agents get only the permissions their job needs. Every action — and every correction a reviewer makes — lands in an audit trail.
Where plain rules read a document reliably, rules go first and AI is the fallback. Answers cite their source, and sensitive text is never logged.
Most teams pay for a proposal, then wait. We think the proposal is the prototype — you should be able to click through a real screen before the first invoice.
The same people who scope the work are the ones who build it — so nothing gets lost translating between a strategist, a designer, and an engineer.
Not a questionnaire — a real walkthrough of how things work today, where they break, and what "done" looks like for the people who'll actually use this.
Screens get built alongside real interaction, not signed off as static mockups. What you review is close to what ships — fewer surprises later.
Every piece — a dashboard, a permission layer, an integration, an AI agent — lands as something you can click through, reviewed as it's built, not delivered as one big reveal.
Fixing what breaks, extending what works, and handing over documentation clean enough that you're never boxed in by how it was built.
The tools look different depending on the world they live in — and in every one of them, AI now carries part of the load. Hover an industry to see the systems we build there, AI included.
Payments, risk & compliance
Learning & institutional tools
Movement, tracking & visibility
Process, inventory & ops
Patient & clinical operations
Commerce & customer ops
Share a bit about the project and we'll get back to you — usually within a day.
No account manager relaying between you and the people building. Whoever you talk to is whoever is writing the code and making the design calls — so nothing gets diluted in translation.
Both. The process flexes — enterprise clients get more structure and sign-off gates, early-stage teams get faster, looser iteration.
Yes. We build retrieval-augmented (RAG) assistants that answer from your own documents, agents that take actions inside your existing tools, and deep agents that plan and complete long multi-step tasks. Same process as everything else — you see it working on your data early, not in a pitch deck.
A chatbot answers questions. An agent can also take actions — read an email, fill a record, call an API. A deep agent goes further: it makes a plan, splits the work across sub-agents, keeps notes as it goes, and sees a long task through to the end.
Two layers. Guardrails: answers are grounded in your sources and cite them, every model output is validated against a schema before it is used, and agents only get the permissions they need. Human-in-the-loop: anything that writes to your system waits for a person to review and approve it, and every action is logged.
The AI does the reading and drafting; a person makes the call. In a document-extraction system we are building now, AI reads a purchase order into a draft, a reviewer checks it side by side with the original PDF, fixes anything wrong and approves it. Approval is the only step that writes live data — and every correction is audited.
Whichever fits the job. Our systems are model-agnostic — the model is a configuration choice, not baked into the code — so you can switch providers as prices and capabilities change.