STEP 01
Scenario discovery
Map the manual process: triggers, decisions, system touchpoints, exceptions, and current failure modes.
Make.com / Integromat / Workflow Automation
Make.com automation agency for production scenarios, AI workflows, and reliable integrations
Profitec AI designs, builds, and monitors Make.com scenarios so business teams get reliable cross-tool automation without scenarios that silently break on a public holiday.
A Make.com automation agency designs, builds, and operates Make scenarios on behalf of business teams. Make.com (formerly Integromat) is a visual workflow automation platform that connects business tools through scenarios — flows of modules that move and transform data across systems. Profitec AI is a Make.com automation agency that builds scenarios for CRM hygiene, lead handling, AI summarization, document processing, reporting, and cross-tool sync — with router branches, error handlers, data stores, and audit trails around every scenario so they survive in production.
Where the workflow breaks
Where Make scenarios usually break
01
Scenarios run without error handlers and break silently when an API changes.
02
Operations budget gets eaten by overscheduled scenarios.
03
Webhook scenarios are stuck waiting because data shape changed upstream.
04
Routers are nested deep without filters, so wrong branches fire.
05
Data stores are misused as a database when they should be a CRM lookup.
06
AI modules cost more than expected because there are no guardrails.
What Profitec builds
What a Make automation system actually delivers
Production-ready Make scenarios: routers, filters, error handlers, data stores, and audit trails — built so business teams can keep operating them without being rescued every week.
CRM Pipeline Consolelive pipeline
STAGE 01
Lead capture
Acme Robotics · web form
STAGE 02
Enrichment
Industry · size · intent ✓
STAGE 03
Owner assignment
D. Cohen · round-robin
STAGE 04
Follow-up task
Email draft · due in 1h
Stale deal alert
3 deals idle > 7 days → escalated to manager
CRM field completion
92%Pipeline
How a Make automation engagement works
Input
Processing
AI / logic
Human control
Output
Measurement
STEP 02
Make architecture
Decide on plan tier, scenario decomposition, data store usage, webhook patterns, and error handling strategy.
STEP 03
Scenario build
Build scenarios with routers, filters, data stores, and error handlers in a staging team or organization.
STEP 04
Testing
Run end-to-end tests with malformed inputs, API failures, rate limits, and AI cost guardrails.
STEP 05
Deployment
Promote scenarios to the production organization with monitoring, alerting, and runbooks.
STEP 06
Monitoring
Scenario success rate, operations consumption, AI cost, and error patterns surfaced as dashboards and alerts.
STEP 07
Iteration
Refactor common logic into shared scenarios, retire unused branches, and expand to adjacent processes.
Integrations
Built around the tools you already run.
CRM
HubSpotSalesforcePipedriveZoho
Communication
EmailSlackTelegramWhatsApp
Storage
Google DriveDropboxAirtableNotion
AI
OpenAIAnthropicMake AI modules
Custom
HTTPWebhooksData stores
Operations
StripeQuickBooksXeroJira
Tooling is illustrative. The automation is designed around the systems you already use, connected through APIs and orchestration layers such as n8n and Make.
What improves
Metrics we measure against a baseline.
Scenario success rate
/01Percentage of runs that complete without manual intervention.
Operations efficiency
/02Operations consumed per useful outcome — kept inside budget.
Manual hours displaced
/03Hours per week removed from the human team.
Mean time to recover
/04Time from scenario failure to detection and fix.
AI cost per outcome
/05Token spend per useful action — kept inside guardrails.
Operational coverage
/06Percentage of the manual process now handled by Make scenarios.
Controls
Controls & risk
Make is fast to ship and slow to operate without discipline. Every scenario we deliver includes error handling, observability, and a runbook so internal teams can keep it alive.
- Error handlers (commit, rollback, ignore) on every module
- Filters and routers to prevent wrong branches from firing
- Signature validation and schema checks on inbound webhooks
- Data store usage limits and TTL strategy
- AI module cost caps and model fallback rules
- Operations consumption alerting before billing surprise
Implementation
A controlled path from audit to monitoring.
01
Process audit
Map the manual process, owners, systems, exceptions, and known failure modes. Decide what should not be automated.
02
Architecture
Plan tier, organization structure, scenario decomposition, data stores, error handling, and observability.
03
Build
Scenarios shipped with routers, filters, error handlers, and audit logging. Tested in staging organization.
04
Pilot
Run live for a controlled scope, measure success rate and exceptions, and adjust before broader rollout.
05
Rollout
Expand to full scope with monitoring and runbooks for the operating team.
06
Operate
Optional ongoing operation: monitoring, incident response, scenario refactors, and roadmap of next scenarios.
Common questions
What teams ask before we start.
01What is Make.com?
Make.com (formerly Integromat) is a visual workflow automation platform. It connects business tools through scenarios — flows of modules that move and transform data across systems. Compared to Zapier, Make has more flexible routers, filters, data stores, and error handling, at the cost of a steeper learning curve.
02When should we choose Make over Zapier or n8n?
Choose Make when you need router-based branching, data stores for lookup state, complex error handling, or aggregator/iterator patterns that Zapier cannot express. Choose n8n instead when you need self-hosting, custom code nodes, or version control. Choose Zapier when you only need simple linear automations.
03How is this different from a freelance Make builder?
A freelancer ships a scenario. A Make.com automation agency engagement ships a system: scenario + error handling + observability + runbooks + monitoring. The difference shows up when an upstream API breaks and the business needs the scenario back online fast.
04Do you build AI scenarios on Make?
Yes. We wire OpenAI, Anthropic, and other LLMs into Make scenarios with controlled prompts, cost guardrails, and human approval on sensitive output — for classification, summarization, drafting, and routing.
05Can you take over and fix an existing Make setup?
Yes. We audit current scenarios, error patterns, operations consumption, and data store usage, then propose a stabilization plan before extending. We do not start by rebuilding everything.
06How do you control Make operations cost?
We design for operations efficiency: prefer instant triggers over polling, use filters early to avoid wasted modules, deduplicate webhooks, and add operations consumption alerts. The result is more outcome per operation.
07Do you migrate from Make to n8n or vice versa?
Yes — when there is a real reason. Make is faster to ship; n8n is more flexible and self-hostable. We migrate in either direction when the operating constraints justify it, not as a default.
08Will we be locked into Profitec AI long-term?
No. Every scenario is documented and built so an internal team or another partner can take over. Optional ongoing operation is an offer, not a dependency.
Next step
Build Make scenarios that survive in production.
A focused Make review maps your current scenarios, exception patterns, and operations budget — then proposes the first controlled scenario worth shipping or stabilizing.