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AI agents · Workflow automation

Custom AI agents and workflow automation.

Connect your documents, CRM and internal tools with custom AI agents and workflow automation. Start with one process and a clear success measure.

★★★★☆ 4.5 on Clutch · 50+ projects · 20+ clients · since 2021

What we build

Agents, assistants, and automated pipelines.

Custom AI agents

Autonomous and human-in-the-loop agents that plan, call tools, and complete multi-step tasks — grounded in your data, with guardrails and full audit trails.

LLM assistants & copilots

Internal chat assistants that retrieve approved documents and help staff find answers. Scope source citations, access permissions and evaluation against representative questions.

Workflow & process automation

Event- and schedule-driven pipelines that move data between your CRM, docs, email, and internal tools — the n8n / Zapier-class integrations, built to production standards.

Document & data automation

Extraction, classification, summarization, and generation over invoices, contracts, tickets, and reports — turning unstructured piles into structured, actionable records.

Voice & conversational AI

Real-time voice agents and chatbots — an extension of our AI avatar work — that handle intake, triage, and support with a natural, on-brand voice.

Integration & internal tooling

The glue and the dashboards: secure API integrations, internal admin tools, and the observability to trust an automation in production.

Virtual Verse Studio builds custom AI agents, internal assistants and workflow automation for teams handling repetitive document, support and operations tasks. We connect the tools you already use and agree which actions need a person to review or approve them.

Start with a specific workflow: what triggers it, which systems it touches, where mistakes happen and how much work it creates. That brief helps us choose between a conventional automation, an assistant that retrieves your documents, and an agent that uses tools. We scope the integrations, evaluation and monitoring before a production build.

The work

Automation that plugs into your real stack.

PotionKeep — Virtual Verse Studio

PotionKeep — an AI-assisted app build

PotionKeep is our own medication tracker, built by one developer working with an AI coding agent and released on both app stores in roughly 48 hours. It demonstrates our AI-assisted development process. A client automation project needs a separate scope for its integrations, data access and reliability requirements.

We integrate directly into your version control, project management, and communication stack — operating as a seamless extension of your in-house team.
See the work →
How we work

From workflow review to production rollout.

  1. Discovery call

    A free 30-minute scoping conversation. We map the workflow, the tools it touches, the volume, and the outcome you need. No NDA required to start.

  2. Scope & proposal

    A detailed scope and fixed-fee or milestone proposal within a week — with the model choice, integration list, data-handling plan, and a clear success metric.

  3. Prototype

    A working agent or pipeline against your real (sandboxed) data in 2–3 weeks, so you can judge accuracy and fit before we commit to the full build.

  4. Build & harden

    Production engineering: guardrails, evaluation sets, human-in-the-loop checkpoints, logging, and monitoring — the difference between a demo and a system you can trust.

  5. Launch & lifetime support

    Deployment into your stack, team enablement, and ongoing tuning as your data and the models evolve — lifetime technical support on everything we ship.

Stack · platforms & tools

The technology behind it

  • OpenAI
  • Anthropic Claude
  • Google Gemini
  • Llama
  • LangChain
  • LlamaIndex
  • Vector databases
  • RAG
  • n8n
  • Zapier / Make
  • Python
  • TypeScript
  • AWS
  • Webhooks & REST
Trusted by enterprises, institutions & studios
  • National Bank of Kuwait
  • Meta
  • Road Safety Authority Ireland
  • Cubicle Ninjas
  • Nova Slides
  • Seat Unique
  • Immersive Exposure
  • Work Spatial
  • Veem
FAQ · questions

What XR buyers ask us

What does an AI automation agency actually build?
We build three broad things: custom AI agents (systems that reason and take actions across your tools), LLM assistants (chat and in-app copilots grounded in your data), and workflow automation (deterministic pipelines that move and transform data on triggers or schedules). Most engagements combine them — for example, an agent that reads incoming email, extracts the request, drafts a reply, and files the record, with a human approving the send.
How much does custom AI automation cost?
We scope every project as a fixed-fee or milestone engagement. A focused single-workflow automation or a scoped assistant typically starts in the low-to-mid five figures; multi-agent systems with deep integrations, evaluation, and production monitoring are larger. You always get a detailed proposal with a clear success metric before any work begins — no hourly surprises.
How long does an automation project take?
A working prototype against your real data usually lands in 2–3 weeks. A production-ready single-workflow automation ships in 6–10 weeks; larger multi-agent or multi-integration systems run 3–6 months with continuous delivery.
Will our data be used to train public models?
No. We architect around your data-handling requirements — enterprise API tiers with no-training guarantees, private or self-hosted models where policy demands it, and your own cloud tenancy when required. Data flows and retention are documented in the proposal and we sign mutual NDAs for protected data.
Do you use no-code tools like n8n and Zapier, or custom code?
Whichever is right for the problem. For straightforward integrations we build on n8n, Zapier, or Make so you can own and edit them; for anything requiring reasoning, custom logic, or scale we write code (Python / TypeScript) with proper testing and monitoring. We are transparent about the trade-offs and hand over documentation either way.
How do you keep an AI agent from making mistakes?
Reliability is engineering, not luck: retrieval grounding so answers cite your sources, evaluation sets that catch regressions before deploy, guardrails and allow-lists on the actions an agent can take, human-in-the-loop checkpoints for high-stakes steps, and logging plus monitoring so you can see exactly what ran and why.
Do you own the IP of what you build?
You do. All custom code and configuration transfer to you on final acceptance under a standard work-for-hire clause. Pre-existing tooling and third-party libraries remain under their own licenses, and we can operate entirely under your paperwork.
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Let’s scope your project.

Share your project brief, target users and timing. We can help define the deliverables, platform and budget.