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Profitec AI

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AI automation & AI search visibility for B2B

Profitec AI is an Israel-based AI automation company for B2B. We design, build, and operate controlled automations that replace manual work across CRM updates, lead handling, follow-ups, reporting, and document processing — wiring your existing tools (HubSpot, Salesforce, Pipedrive, Monday, n8n, Make, Zapier, Slack) into one running pipeline with human-in-the-loop checkpoints, monitoring, and fallback rules.

  • Min project size
    $5,000+
  • Hourly rate
    $100 - $149 / hr
  • Employees
    2 - 9
  • Year founded
    Founded 2020

Service Lines
AI Agents
15%
Search Engine Optimization
15%
AI Consulting
10%
AI Development
10%
CRM Consulting and SI
10%
API Development
5%
BI & Big Data Consulting & SI
5%
Custom Software Development
5%
Generative AI
5%
IT Managed Services
5%
IT Strategy Consulting
5%
Low/No Code Development
5%
Robotics Process Automation
5%

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Industries

No Industries have been added yet…

Clients
Small Business (<$10M)
70%
Midmarket ($10M - $1B)
25%
Enterprise (>$1B)
5%

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Our Story

Profitec AI was created for businesses that know manual operations are slowing them down, but do not need another generic software subscription. We build practical AI automation systems: CRM workflows, reporting, lead management, document processing, integrations, and internal tools. Our team combines strategy, engineering, and process thinking to turn fragmented operations into reliable systems that save time, improve visibility, and support growth.

What Sets Us Apart

Visibility + automation ship together

Most vendors do one or the other. We connect AI search visibility (AIO/GEO) directly to the CRM, lead, and reporting automations we build — so a prospect who finds you via ChatGPT lands in a pipeline that captures and routes them. Not a standalone report.

We redesign before we automate

We refuse to automate broken processes. Every engagement starts by redesigning the workflow — clearer handoffs, fewer manual steps, measurable checkpoints — before any code is written. You don't just speed up inefficient work.

Controlled systems, not black boxes

Every automation ships with human-in-the-loop checkpoints, monitoring, and fallback rules. Sensitive actions — legal, financial, customer-facing — pause for approval. You get systems you can trust in production, not unsupervised AI.

Quick Facts

6 years in business

Tools and Technology

Slack
Zoom
Google Meet
Discord
Jira
Trello
Notion
Airtable
Zoho Projects
Miro
GitHub
GitLab

Commonly Asked Questions

We're model-agnostic and pick per task. We build with Anthropic Claude, OpenAI GPT, Google, and Mistral models, orchestrated with frameworks like CrewAI for multi-agent systems and n8n, Make, and Zapier for workflow automation. We wire these into your existing stack — HubSpot, Salesforce, Pipedrive, Monday, Notion, Airtable, Google Workspace, Slack, and Stripe — rather than forcing a rip-and-replace. The right model for a step is the one that's accurate, fast, and cost-effective there, not one vendor everywhere.

We ground models in your real data instead of letting them free-associate. That means retrieval-augmented generation (RAG) over your documents and systems, structured tool calls instead of open-ended prompts, and validation rules on every output. High-stakes actions — legal, financial, customer-facing — pass through human-in-the-loop approval gates before anything executes. We monitor outputs in production and add fallback rules, so a low-confidence result escalates to a person rather than shipping a wrong answer.

Yes — it's core to what we do. For one trading-floor client we built an eight-agent CrewAI system with a LightGBM machine-learning model that produces daily market intelligence in a hierarchical workflow. For an insurance client we built a five-stage, seven-agent claims pipeline with a human-in-the-loop escalation gate that compressed a 34-day cycle to 20 days (−41%). We design agents with clear roles, controls, and handoffs — not a single unsupervised model.

RAG is how we make AI answer from your knowledge instead of guessing. We index your documents, tickets, and records, retrieve the relevant context at query time, and have the model generate an answer grounded in those sources — with citations back to the original. It powers internal knowledge assistants, document processing, and support automation. The same grounding principle drives our AI search visibility work: structured, citable content is what gets retrieved and cited, whether by your assistant or by ChatGPT.

We make your company citable inside AI engines. For an estate-planning client we rewrote the schema chain, opened crawler access for GPTBot and ClaudeBot, and restructured content into citable answers — moving citation rate from 16.1% to 62% across ChatGPT, Perplexity, Gemini, and Claude in eight weeks, measured over 30 query patterns. You get prompt market mapping, a competitor citation audit, entity graph repair, schema deployment, and a live dashboard tracking citation rate and competitor share per engine.

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