AI systems built around how your business actually works.
IntuitionMind.ai helps organizations improve customer communication, lead handling, internal workflows, and decision-making through practical AI, automation, and custom software. We combine founder-led business strategy with hands-on technical delivery from discovery through deployment.
Based in Brighton, Michigan. Available for focused implementations and larger custom builds.
Conceptual view · not a specific client system
Applied AI and automation
Practical systems for real operational problems
We help organizations identify where work is getting stuck, define the right intervention, and implement a system that fits the way their team operates. That may involve configuring existing tools, connecting disconnected systems, deploying an AI agent, or building a focused custom application.
01
Workflow and automation design
Map existing processes, identify costly handoffs or repetitive work, and design a practical system for improving them.
02
AI agents and customer operations
Build phone, web, and internal agents that use company knowledge, follow defined business rules, collect information, and take appropriate actions.
03
Data and business-system integrations
Connect lead sources, CRMs, calendars, communications tools, databases, and reporting systems so information moves where it is needed.
04
Custom applications and machine learning
Develop focused applications, decision-support tools, and machine-learning systems when standard software does not adequately fit the problem.
Improve what happens between an inquiry and an outcome
Service businesses often rely on a chain of calls, emails, calendars, staff decisions, and software handoffs to turn an inquiry into completed work. We help make those processes more consistent, visible, and manageable.
Capture, qualify, and route incoming opportunities
Connect lead sources with CRM and scheduling systems
Automate follow-up and internal handoffs
Apply service-area, priority, or escalation rules
Give staff useful information without requiring repeated data entry
Track what happened after a lead entered the business
The right project begins with understanding the existing workflow, the economic cost of the problem, and what a successful outcome would look like.
How we work
From operational problem to working system
01
Discover
Understand the workflow, people, systems, constraints, and business outcome involved.
02
Define
Choose a focused scope, technical approach, responsibilities, and acceptance criteria.
03
Build and test
Configure or develop the system, connect required tools, and test realistic scenarios before launch.
04
Deploy and improve
Launch carefully, monitor actual use, resolve gaps, and support agreed improvements.
Engagements can range from a focused implementation to a larger custom build. Scope, ownership, support, and ongoing maintenance are defined before development begins.
Selected builds
Selected builds and applied experiments
Working demonstration · Fictional company
AI Phone Agent for Field-Service Intake
A voice-agent demonstration showing how a service business can handle inbound inquiries through a structured workflow. The system identifies caller needs, responds to potential hazards, checks service-area eligibility through an external routing API, simulates estimate booking, and demonstrates calendar and CRM handoffs.
The fictional Austin’s Tree Service scenario demonstrates how an agent can be adapted to a company’s actual intake rules, systems, and escalation procedures.
Spirit Bot extends our earlier personalized chatbot work into a website-assistant platform grounded in an organization’s business knowledge and written communication style. The core platform is substantially built, with broader real-world testing still ahead.
Illustrative example · same facts, different written tone
Direct
“Returns are accepted within 30 days when the item is unused and in its original packaging.”
Warm
“Changed your mind? No problem. Send it back unused, in its original packaging, within 30 days.”
Production deployment · Anonymized client system
Sales & CRM automation
AI Lead Management Platform
An end-to-end platform automating the lead journey from capture to customer. It deduplicates leads from forms, email, WhatsApp, and CRM entries, extracts intent and buying signals grounded by retrieval over sales playbooks, scores leads with explainable weighted rules, and orchestrates CRM updates, follow-ups, and calendar booking as a guarded LangGraph workflow.
A support agent handling order, return, and product questions for a direct-to-consumer brand across web chat and WhatsApp. Order and return actions run through permission-scoped MCP tools with policy checks and customer confirmation before any write; answers are grounded in cited policy and catalog data, with human escalation on low confidence.
A financial-services assistant that recommends credit and loan products through schema-validated intent extraction, hybrid vector search, and custom reranking against eligibility requirements.
A legal contract-review assistant that compares clauses against a firm's negotiation playbook, returning risk levels, deviations, and cited redline suggestions.
Business strategy, client discovery, marketing, product direction, and project leadership. Austin previously founded and scaled My Restoration Leads, which served the disaster-restoration industry, and has led marketing teams and growth initiatives for technology and service businesses.
Rajesh Mane
Applied AI and software engineering, agentic workflows, systems integration, and technical delivery. Rajesh has built production AI systems across sales automation, e-commerce, fintech, and legal tech since 2018.
Austin and Rajesh work together to connect business requirements with technical implementation. Additional specialists may be involved when a project’s scope requires them.
Can physiological activity recorded before a stimulus—or before an outcome is revealed—contain reproducible predictive information?
This question anchors IntuitionMind.ai’s long-term research into presentiment, precognition, ECG data, and machine learning. We have built collection and modeling tools and conducted exploratory analyses. The ambition is substantial; the evidence remains early, and further validation is required.
Tell us where work is getting stuck, what systems are involved, and what a better outcome would look like. We’ll help determine the most practical path—from better use of existing software to automation, integration, or a focused custom build.
