Asdes Studio

What's eating your team's time?

Example result

“We manually pull data from 6 spreadsheets every Monday and compile a report for leadership.”

Six disconnected spreadsheets means six points of failure every Monday. The bottleneck is not the report - it is the manual data gathering, normalization across inconsistent formats, and the hour spent catching copy-paste errors before leadership sees it.

Data Extraction Agent (Spreadsheets)

Connects to all 6 spreadsheets on schedule, pulls raw data, normalizes column formats and units across sources.

Quality Scorer (Data Validation)

Validates extracted data against expected ranges, flags anomalies and missing fields before report compilation.

Report Builder (Leadership Format)

Compiles validated data into leadership report template with charts, summaries, and week-over-week deltas.

Notification Agent (Email)

Delivers the finished report to the leadership distribution list every Monday at 7am.

5-7 hours/week savedlow complexity1-2 weeks

Book a call to scope this

What We Run

Built for ourselves first.

We build our own AI infrastructure before we build yours. These systems run our studio daily - not demos, not proofs of concept.

01Built internally

Autonomous Operations Platform

Multi-agent orchestration with CEO coordination

A team of specialized AI agents coordinated by an orchestrator. Each agent owns a business domain with persistent memory, operational rules, and tiered autonomy governing independent versus human-approved decisions. Runs daily, managing real operations.

30+ daily workflows4-tier governancePersistent memory

Claude CodePythonSupabasepgvector

Abstract neural network visualization representing multi-agent orchestration

02Built internally

Intelligence Pipeline

Automated research, writing, and audio generation

Fully automated intelligence pipeline. AI agents gather data from 500+ sources overnight, a research model processes context, a writing model produces the script, and voice synthesis generates multi-voice audio - delivered every morning.

500+ sourcesZero manual stepsMulti-model pipeline

GPT-5.4Claude OpusElevenLabsMCP

Data streams flowing through an automated intelligence pipeline

03Built internally

Voice AI Command Center

Speech-to-agent control for AI infrastructure

Voice-controlled interface for managing an entire AI agent team. Real-time speech-to-text captures commands, routes them to the appropriate agent, and returns responses. Desktop overlay and iOS native - natural language control from a phone.

Sub-second dispatchDesktop + iOSMulti-model routing

SwiftSwiftUIWebSocketElevenLabsWhisper

Voice command interface with microphone and concentric ring visualization

04Built internally

Agentic AI Operating System

Unified command center for multi-agent infrastructure

A unified operating system spanning web and mobile. Chat interface for agent communication, workflow orchestration library, product tracker, briefing room with daily intelligence, and institutional memory. Every agent, every workflow, every decision - managed from one surface.

5 modulesPhased buildsFull CI/CD

Next.js 15ReactSwiftUISupabase

Workflow orchestration library with active pipelines

05Built internally

Content Distribution Engine

AI-powered multi-channel pipeline with quality scoring

AI-powered content pipeline handling the full lifecycle: community monitoring, trend analysis, content creation, platform-specific formatting, scheduling, and performance tracking. Content below quality threshold gets killed, not published.

8+ channelsAuto scoringCommunity monitoring

ClaudeSupabaseMDXNext.js

Branching distribution network for multi-channel content pipeline

06Built internally

Credential Management Server

Zero-trust credential broker for multi-agent AI infrastructure

Centralized credential management enforcing zero-trust between AI agents and external APIs. Agents never see credentials - they call named methods through scoped tokens while every action is forensically audited. Policy-driven query templates, per-agent rate limiting, and instant credential rotation without service restarts.

30+ API adaptersZero credential exposureAppend-only audit

PythonFastAPIMCPSupabase

Zero-trust credential vault with secured endpoint connections

07Built internally

Workflow Orchestration Engine

Graph-based workflow engine powering 24 autonomous pipelines

A workflow orchestration engine that defines multi-stage pipelines as directed graphs. Each node dispatches to a specialized AI agent with scoped context, model routing, and approval gates. 24 active workflows across build, research, freelancing, operations, and content - from product discovery through to launch.

24 active workflowsGraph-based executionMulti-model routing

TypeScriptNext.jsSupabaseClaudeGPT-5.4

Workflow library showing 24 active orchestration pipelines

Agent Topology

Watch it coordinate.

A CEO agent coordinates three domain specialists, each with persistent memory, tiered autonomy, and operational rules. Running right now.

CEO

Orchestrator

System Coordinator

Tier 12m ago

Pipeline

Business Development

Tier 214m ago

Content

Distribution & Marketing

Tier 28m ago

Infra

Infrastructure & Security

Tier 26m ago

Voice Interface

Talk to the system.

Natural language control of enterprise-grade AI infrastructure. Press, speak, get an answer.

Talk to our agent

Press to start

Listening

You said

“How would you automate my customer onboarding?”

Agent responding

I'd start with a triage agent that classifies each new customer by segment, then routes to specialized onboarding flows. High-value accounts get a dedicated sequence with check-ins. Self-serve accounts get an automated drip with escalation triggers if they stall.

Live Demos

Watch it work.

Two demos. One generates a real CEO briefing. The other runs a live agent task. Pick one.

How It Works

Built for production from day one.

Persistent memory, governance, monitoring - all built in from the first deployment. Not bolted on later.

Architecture First

Agent roles, data flows, integration points, failure modes. Nothing gets built until the architecture is clear and signed off.

Build With Guardrails

Error handling, rate limiting, credential management, and operational governance are first-class concerns - not afterthoughts.

Human Checkpoints

AI builds fast but drifts silently. Structured review points verify the system does what it was designed to do.

Outcome Over Output

Success measured by business outcomes: tasks completed, time saved, decisions informed. Not lines of code.

Services

What we build.

AI Automation & Agents

Multi-agent systems, autonomous workflows, scheduled operations, agent coordination. From single-purpose agents to full orchestration platforms with governance and persistent memory.

Multi-agent orchestrationScheduled operationsAutonomous workflows

Full-Stack Development

Web, mobile, and desktop. Products built through phased methodology with human testing at each phase - not one-shot AI generation.

Next.jsReactSwiftUITauri

AI Integration (MCP/API)

Model Context Protocol servers connecting AI to business infrastructure. Database access, API integrations, tool orchestration - the standard for how agents interact with real systems.

MCP serversAPI integrationTool orchestration

Architecture

See the architecture.

Toggle between pre-built agent architectures. Click any node for details.

Scanner

claude-haiku

Monitors multiple platforms for qualified opportunities

qualified leads

→

Spec Generator

claude-sonnet

Generates custom spec docs for each prospect

spec docs

→

Proposal Agent

claude-opus

Crafts tailored proposals with pricing

Model: Claude Opus

Tools: template_engine, pricing_calc, crm_update

Avg time: 4 min/proposal

Success rate: 34%

proposals

→

Follow-up Agent

claude-sonnet

Manages follow-ups and client communication

follow-ups

Want us to design an architecture for your business? Get in touch.

About

One founder. Unreasonable output.

Years in Big 4 consulting watching enterprises throw millions at AI proofs of concept that never made it to production. The problem was never the technology - it was the gap between what AI demos can do and what production systems require.

Asdes Studio exists to close that gap. Multi-agent AI infrastructure, full-time. Not as a side project, not as a consulting add-on. Every system is designed for the moment nobody is watching - persistent memory, governance rules, failure handling, and monitoring that runs around the clock.

Tech Stack

ClaudeGPTNext.jsSwiftUISupabasePythonTypeScriptMCP

Typical Engagement

2 - 6 weeks

From brief to production deployment

Writing

asdesbuilds.com →

Talk to an Agent

Ask anything. Right now.

Not a form. Not a chatbot. A real AI agent scoped to studio knowledge, answering live.

Every question answered by the same agents that built these systems.

Our consulting agent handles initial conversations. Scoped to studio capabilities, past work, technical approach, and timelines. Not a generic LLM - a purpose-built agent with knowledge of every system on this page.

Ask about pricing, process, technical stack, timelines, or specific capabilities. Get a real answer, not a form submission.

Sales Agent

Technical Agent

Asdes Studio Agent

Online - typically responds in 2s

Can you build a multi-agent system that handles our customer support pipeline?

Absolutely. That is squarely in what the studio does best. A support pipeline typically involves 3-4 agents: a triage agent that classifies and routes incoming tickets, a resolution agent that handles known patterns from your knowledge base, an escalation agent for edge cases that need human review, and a quality agent that scores every response before it goes out.

How long would something like that take?

For a 3-4 agent support system with persistent memory and governance - typically 3-4 weeks. Week 1 is architecture: agent roles, data flows, integration points with your existing ticket system. Weeks 2-3 are build with human checkpoint reviews. Week 4 is production hardening - monitoring, failure handling, everything that separates a demo from a production system.

What models would you use?

Depends on the workload profile. For triage and classification, a fast model like Claude Haiku or GPT-4o-mini keeps costs low at volume. The resolution agent usually runs Claude Sonnet for quality-to-cost balance. Escalation and quality scoring get Opus or GPT-5.4 because those decisions matter most. The studio routes models per task, not one-size-fits-all.

Systems that run while you sleep.