AI systems / automation / software

We build AI systems that work.

Autonomous agents, intelligent workflows, AI applications, data systems and custom automation designed around how your business actually operates.

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System 01 / Living architecture

Find qualified enterprise leads

Illustrative input

Agent / Data

WEBCRMDATABASEAPIEMAIL

Create qualified lead

Ready

24 qualified prospects

Illustrative output

Illustrative system trace / 8F4A

INPUT42ms

REASONING184ms

WEB312ms

CRM96ms

ACTION141ms

✓ COMPLETEDDEMO

System log / illustrative

14:32:01 request.received

14:32:02 agent.plan.created

14:32:02 tool.web.selected

14:32:03 crm.record.created

14:32:03 workflow.completed

Select a node to inspect how the architecture works.

Permissions · Policies · Human approval · Guardrails

Built for operational work

Customer operationsSales systemsKnowledge workData processes

01Our approach

The model is not the system.

Anyone can call a model. The engineering is everything around it: what triggers it, what data it reads, which tools it’s allowed to touch, what happens when it gets something wrong, and the record of what it did.

The problem picks the stack.

We select models, tools and infrastructure around the work — not the other way around.

Running is not finished.

A system that acts on your business needs logs, traces and metrics from the first day, or nobody can say what happened.

Autonomy needs boundaries.

Permissions, policies and human approval are part of the architecture, not something bolted on afterwards.

02Capabilities

From one workflow to an autonomous system.

03System architecture

Intelligence works when everything connects.

Not isolated tools. A coordinated operating layer that understands context and carries work through.

Selected component / 02

Agent

Interprets intent, plans the task and decides what happens next.

04Process

One signal, end to end.

A request enters, the system reasons about it, selects the tools it needs, performs the operation and produces a result.

Control

Permissions / identity

Policies / guardrails

Human approval

Audit log

Observability

Logs

Traces

Metrics

Monitoring

Stage detail

Hover a stage for context, or select one for detail.

Illustrative example — demonstration values, not company results.

05Autonomous agents

Systems that don’t wait for instructions.

Agents observe what is happening, reason through the next step, use tools, verify outcomes and involve people when judgment is needed.

Observe

Reason

Plan

Act

Verify

Continue

06Workflow in motion

One input. A complete chain of work.

01Lead receivedA new opportunity enters the system.

02AI qualificationThe system completes and validates this step.

03Company researchThe system completes and validates this step.

04CRM updateThe system completes and validates this step.

05Personalized responseThe system completes and validates this step.

06Sales notificationA person takes over with full context.

07Operational shift

Before

Manual research
Repeated data entry
Recurring support questions
Spreadsheet workflows
Disconnected systems

After

AI-assisted research
Automated extraction
Knowledge-grounded support
Agent-driven workflows
Connected operations

08Concept / reference architecture

Systems we build.

Practical architectures for the work businesses are trying to automate now.

Concept / reference architecture

Industry

B2B SaaS / Professional services

The gap

Sales teams lose hours to prospect discovery, account research, contact enrichment, personalized outreach and CRM administration.

Reference architecture

Lead sources

Web scraper

Data enrichment

Research agent

Lead scoring

Personalization

CRM

Outreach

Human review

What GM Holdings builds

+Prospect discovery

+Company research

+Contact enrichment

+Lead scoring

+Personalized messaging

+CRM synchronization

+Approval gates

Intended outcome

Reduce repetitive prospecting work while giving sales teams better researched, continuously updated opportunities.

Concept / reference architecture

Industry

E-commerce / SaaS / Services

The gap

Support teams repeatedly search documentation, check customer records and perform routine actions across disconnected systems.

Reference architecture

Customer

AI conversation

Intent

Knowledge retrieval

Agent

Business systems

Action

Verification

Customer

What GM Holdings builds

+Knowledge search

+Order tracking

+Permitted actions

+Ticket creation

+Conversation summaries

+CRM updates

+Human escalation

Intended outcome

Move beyond question answering toward an action-capable service system with clear boundaries and escalation paths.

Concept / reference architecture

Industry

Consulting / Investment / Marketing / Strategy

The gap

Teams repeatedly collect and reconcile information from websites, documents and databases before they can make a decision.

Reference architecture

Research request

Planner agent

Sources

Extraction

Source validation

Synthesis

Report

Human review

What GM Holdings builds

+Multi-source research

+Web collection

+Document analysis

+Cross-source comparison

+Report generation

+Citation tracking

+Scheduled monitoring

Intended outcome

Turn recurring manual research into a continuously operating, source-aware intelligence workflow.

Concept / reference architecture

Industry

Customer-facing businesses

The gap

Most websites collect interest but cannot answer with business context, retrieve live information or complete useful work.

Reference architecture

Website

Chat / search / forms

AI agent

Knowledge

CRM

APIs

Actions

What GM Holdings builds

+Conversational interface

+Intelligent search

+Lead qualification

+Knowledge retrieval

+CRM actions

+Workflow initiation

+Customer handoff

Intended outcome

Transform the website from a brochure into an intelligent interface connected to knowledge, workflows and business systems.

Concept / reference architecture

Industry

E-commerce / Real estate / Recruitment / Market intelligence

The gap

High-value market information lives across changing public sources, making manual collection incomplete and difficult to maintain.

Reference architecture

Target sources

Crawl

Extract

Clean

Structure

Deduplicate

Enrich

Database

Analysis

Alert / API

What GM Holdings builds

+Competitor monitoring

+Pricing intelligence

+Listing collection

+Supplier discovery

+Catalog building

+Change detection

+Data delivery

Intended outcome

Convert fragmented public information into a structured, continuously updated business dataset.

Concept / reference architecture

Industry

Finance / Legal / Insurance / Operations

The gap

Teams repeatedly read, classify and interpret large volumes of contracts, invoices, policies, reports and operational documents.

Reference architecture

Documents

Ingestion

OCR / parsing

Classification

Extraction

Knowledge store

AI reasoning

Workflow

Approval

What GM Holdings builds

+Document ingestion

+OCR

+Classification

+Structured extraction

+Semantic search

+Comparison

+Workflow triggers

Intended outcome

Move document work from isolated reading and chat toward structured extraction, reasoning and controlled downstream action.

Concept / reference architecture

Industry

Finance / Operations / Administration

The gap

Shared inboxes and administrative queues require people to classify requests, copy data, check rules and update internal systems.

Reference architecture

Email / request

Classify

Extract

Check rules

Look up data

Decide

Execute

Log

Escalate

What GM Holdings builds

+Inbox monitoring

+Request classification

+Data extraction

+Rules validation

+System updates

+Audit logging

+Exception handling

Intended outcome

Automate repeatable back-office work while routing judgment calls and exceptions to the right person.

Concept / reference architecture

Industry

Recruitment / HR

The gap

Recruiters spend significant time sourcing, reading profiles, matching candidates, drafting outreach and coordinating interviews.

Reference architecture

Job description

Sourcing agent

Web / databases

Profile analysis

Matching

Ranking

Outreach

Scheduling

Human review

What GM Holdings builds

+Candidate discovery

+CV parsing

+Job matching

+Candidate research

+Outreach drafting

+Interview scheduling

+ATS integration

Intended outcome

Reduce repetitive coordination and research while keeping candidate assessment and hiring decisions with people.

Concept / reference architecture

Industry

B2B / Enterprise services

The gap

Marketing, sales, support and retention operate in separate tools, leaving revenue teams without a continuous view of the customer lifecycle.

Reference architecture

Marketing

Leads

Sales

CRM

Customer

Support

Retention

Analytics

What GM Holdings builds

+Opportunity detection

+Account research

+Sales preparation

+CRM orchestration

+Customer support

+Activity monitoring

+Lifecycle signals

Intended outcome

Connect specialized agents and workflows across the revenue lifecycle instead of deploying isolated automations.

Control layer

Autonomy with a paper trail.

Every autonomous action passes through the same path: an identity that owns it, a policy that permits it, a verification that confirms it, and a log that records it. Where the stakes justify it, a human approves before it runs.

Reference architecture

Agent

Policies / identity

Tool access

Action

Verification

Audit log

Human escalation

IdentityPermissionExecutionEvidenceEscalation

09How we work

Discover

Understand the business problem.

Architect

Design the complete AI system.

Build

Develop agents, workflows and software.

Integrate

Connect existing infrastructure.

Deploy

Launch into production.

Optimize

Monitor, improve and expand.

10Technologies we work with

Built on the right stack.

We select models, tools, data systems and infrastructure around the problem—not the other way around.

OpenAI → n8n → Supabase → Next.js → Cloudflare

01Models

02Orchestration

03Data

04Applications

05Infrastructure

No single model. No fixed stack. The architecture follows the problem.

Reference architecture

11Principles

Business first

We start with the problem, not the technology.

Systems, not tools

We connect AI, software, data and automation.

Built to operate

Designed for production and real-world workflows.

Human when needed

Automation should know when to involve people.

Built around you

No unnecessary one-size-fits-all solutions.

12Start a project

What should your business stop doing manually?

Tell us what takes too much time, requires too many people or doesn’t scale. We’ll design the system around it.