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Practical AI, shown honestly

What does Marc-Tek's AI automation work look like?

These examples show how Marc-Tek turns a specific business bottleneck into a small, reviewable workflow. Private names, contact details, credentials, and operational data are excluded. Demonstrations are labeled as demonstrations; no fictional result is presented as a client outcome.

Representative demonstration

From a messy manufacturing process to a visible job workflow

Problem: Job information can be spread across notes, spreadsheets, and separate production systems, leaving people to reconstruct status manually.

DiscoverMap the real stages, exceptions, and handoffs with the people doing the work.
DefineTurn observations into requirements and human approval points.
PrototypeBuild a job-status view that exposes bottlenecks and missing information.
ValidateUse feedback to correct scope before committing to a larger build.

What it demonstrates: Marc-Tek does not begin with a generic chatbot. We begin by understanding the workflow, then prototype the smallest useful solution.

This is a representative, sanitized description of prototype work. It is not presented as a completed client deployment or a measured client result.
Marc-Tek internal example

Human-reviewed prospect outreach workspace

Problem: An approved prospect list is only useful if someone researches it, drafts relevant messages, follows up consistently, and keeps a clean record.

ImportBring in an approved CSV list or review organizations from selected public pages.
PrepareOrganize the prospect facts and flag missing personalization details.
DraftCreate a LinkedIn note, email, and scheduled follow-up using only supported facts.
ApproveA person edits, approves, skips, and exports; the system does not auto-send.

What it demonstrates: AI can reduce the repetitive preparation around outreach while preserving human judgment over who is contacted and what gets sent.

Public examples use demonstration records. Real prospect lists, contact information, and client campaign details are not published.
Working demonstration

Customer support grounded in approved company information

Problem: Teams repeatedly answer the same questions about hours, refunds, shipping, policies, and routine service details.

LoadAdd approved FAQs, policies, or reference documents to the knowledge base.
AskA customer writes a question in ordinary language.
AnswerThe agent responds from the approved material instead of inventing a policy.
EscalateUnusual or sensitive requests remain available for human review.

What it demonstrates: A narrow support agent can make approved information easier to use without giving an AI system unrestricted authority.

The current engineering demo remains private while Marc-Tek prepares a branded HTTPS version suitable for public use.
Marc-Tek internal example

Lead intake, triage, approval, and CRM handoff

Problem: Inquiries arrive through different channels, urgent opportunities blend into the queue, and follow-up depends on someone noticing in time.

NormalizeTurn form, email, voicemail, SMS, and call details into one consistent lead record.
PrioritizeClassify intent and urgency so the most important opportunities rise first.
DraftPrepare a response and follow-up without sending automatically.
Approve and recordThe owner approves, edits, or rejects before an approved lead reaches the CRM.

What it demonstrates: Marc-Tek can connect intake, AI-assisted decisions, human control, and downstream records in one focused workflow.

Open the protected Lead Desk demo →

The live demonstration is access-controlled. No customer credentials or production lead records are published on this page.

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