Applied AI systems for business operations

AI systems for work that off-the-shelf software cannot handle.

I connect your data, software, files, and communications into observable workflows, internal tools, and agent operations built around the way your business actually runs.

Choose how to start See deployed systems

Founder-led delivery · Fixed-scope builds · Cloud, hybrid, or local deployment

ConnectedWorks across the tools and data you already use.

ObservableEvery action, handoff, and failure can be inspected.

ExtensibleBuilt to grow as the workflow and models change.

ControlledHuman approvals and local options where they matter.

The implementation gap

The bottleneck is not access to AI. It is getting AI all the way into production.

Most teams have tried models and copilots. The value stalls when useful context is scattered, systems do not connect, ownership is unclear, and nobody can see why an automated process succeeded or failed.

01 Your operating context Files · messages · SaaS · local software · web data

02 Your AI operating layer Models · agents · rules · approvals · logs

03 Work your team can use Actions · dashboards · reports · alerts · handoffs

Engagement menu

A clear path from one painful workflow to a working system.

Start small enough to measure. Build only what deserves to exist. Keep improving what proves valuable.

Find the leverage

AI Workflow Diagnostic

Map one expensive or fragile workflow, identify the systems and data involved, and define the smallest build that can create measurable value.

Workflow and opportunity map
Data, access, and security checklist
ROI hypothesis and risk scorecard
Fixed implementation proposal

5 business days $750

Establish the foundation

Secure AI Foundation

Give a founder, executive, or small team a reliable environment for agent work, remote access, data handling, and observable operations.

Dedicated cloud, hybrid, or local environment
Model and agent configuration
Secure access, backup, and recovery plan
Operator workspace, runbook, and training

1–2 weeks From $2.5k

Transform the operation

Business Operating System Build

Turn a high-value function or cross-team workflow into a connected system with live data, practical automation, and interfaces people can actually operate.

Application, file, message, and data integrations
AI workflows with human approvals
Dashboards, alerts, and audit trails
Testing, documentation, training, and handoff

4–8 weeks From $7.5k

Compound the value

AI Operations Retainer

Keep working systems reliable while models, tools, and business needs change. Expand only where usage and results justify it.

Monitoring and incident response
Workflow, prompt, and model improvements
New integrations and automations
Monthly system health and value review

Ongoing From $3k/mo

Not sure where to start? Bring one workflow that costs time, revenue, quality, or attention.

Describe the workflow

Book directly

Start with the conversation that fits the work.

New projects usually begin with an exploratory call. Existing clients can book advisory or build time, while larger implementations are scoped around outcomes rather than hourly blocks.

See every booking option

30-minute exploratory callA focused first conversation about goals, constraints, and fit. 30 minutes$100 View times Extended exploratory callMore room for complex workflows, background, and possible paths forward. 60 minutes$125 View times

Selected systems

Proof through working software, not AI theater.

Each example combines real interfaces, multiple data sources, AI behavior, and an operating model that a human can inspect and steer.

Operations command center showing urgent work, active projects, calendar items, completed work, and recent changes

Agent operations

One command surface for work, agents, clients, and system health.

A unified operational layer combining agenda, CRM, projects, scheduled activity, agent status, and system diagnostics.

Replaces: Scattered status checks and opaque agent activity
Demonstrates: Live ingestion, orchestration, observability, and human control
Deployment: Private, cloud-assisted, or hybrid

Explore four interactive dashboards

Self-hosted research library with document summaries, claims, source records, tags, and retrieval chunks

Knowledge operations

A private research system that makes an entire corpus usable.

A locally stored research library that ingests documents, websites, audio, and video; transcribes media; and exposes exact and semantic search to people and agents.

Replaces: Folders full of material that cannot be searched or reused
Demonstrates: Multimodal ingestion, local retrieval, summaries, and agent APIs
Deployment: Self-hosted with local-model options

LanguageCommand application with progress dashboard, mobile practice, lesson interface, and account creation

Full-stack AI product

LanguageCommand turns practice into evidence of real production.

A multimodal language-learning platform combining structured curriculum, speech assessment, writing and handwriting analysis, progress tracking, and proof-based review.

Replaces: Recognition-only practice and disconnected learning tools
Demonstrates: Product strategy, AI evaluation, multimodal UX, and billing
Deployment: Web application with mobile-responsive workflows

Visit LanguageCommand

Element interface with dedicated Matrix rooms for parallel AI agent work across business functions

Multi-agent coordination

Parallel AI work organized in familiar, inspectable rooms.

A self-hosted Matrix and Element command layer lets multiple agents, projects, and AI platforms communicate through dedicated rooms on desktop and mobile.

Replaces: One crowded chat and invisible parallel work
Demonstrates: Cross-platform agents, secure handoffs, and mobile steering

Security and control

Use the right deployment model for the work.

Sensitive workflows do not need the same architecture as public marketing automation. We scope data handling, access, approvals, and model choice before building.

Cloud Fastest delivery and broadest model access For low-sensitivity workflows and rapid pilots

Hybrid Local context and tools with selected cloud models For stronger control without giving up frontier capability

Local Models and data remain on client-controlled hardware Subject to hardware and open-model capability requirements

Operating thesis

Our Vision

The practice is built around a simple idea: the valuable part of applied AI is not the model demo. It is the disciplined work of connecting context, shipping reliable workflows, training operators, and improving the system after launch.

Start with one real workflow

Show us the work that should be easier, faster, or more reliable.

We will tell you whether it is worth automating, what a responsible first build looks like, and what it should take to put it into production.

Choose how to start