AHK.AI's make automation service pairs certified consultants with solution architects to design high-volume scenarios using routers, iterators, aggregators, and the HTTP module. We handle complex data transformations that break Zapier—processing arrays, conditional branching, and API pagination. Every bundle is documented with error routes and monitoring.
What You'll Get
Professional Make scenarios with visual logic
Advanced data transformation and mapping
API integration via HTTP and Webhooks
Routers for conditional branching
Iterators/Aggregators for array processing
Error handling with notifications
Data stores for stateful automation
Scenario blueprints and documentation
How We Deliver This Service
Our consultant manages every step to ensure success:
1
Discovery: Map your data flow and integration points
2
Design: Architect scenario structure and error handling
3
Build: Configure modules with proper transformations
4
Test: Run with real data and edge cases
5
Deploy: Launch with monitoring and documentation
Technologies & Tools
Make.com REST APIs GraphQL Webhooks JSON/XML Data Stores Custom Apps
Frequently Asked Questions
What's the difference between Make and Zapier?
Make (formerly Integromat) excels at complex workflows: visual data flow, array iteration, conditional branching, and direct API calls. Zapier is simpler for basic automations. Make is also more cost-effective for high-volume use (operations vs. tasks). We recommend Make for technical workflows and data processing.
Do I need a paid Make account?
Make offers a generous free tier with 1,000 operations/month. Most business workflows need the Core plan ($9/month for 10,000 ops) or Pro ($16/month) for advanced features like full-text search and custom apps. Enterprise is available for SSO and teams.
Can Make integrate with apps not on their platform?
Yes! Make's HTTP module lets us call any REST or GraphQL API directly. We handle authentication (API keys, OAuth, JWT), pagination, and error handling. If it has an API, we can integrate it.
What are routers and iterators?
Routers split a scenario into multiple branches based on conditions—like an if/else. Iterators loop through arrays, processing each item separately. Aggregators collect items back into an array. These are Make's killer features for complex data workflows.
How do you handle errors in Make?
We build dedicated error routes that catch failures, log details, notify your team (Slack/email), and optionally retry. Make's error handling is visual—you can see exactly where failures occur and what data caused them.
What are Make Data Stores?
Data Stores are built-in databases for storing data between scenario runs—like tracking processed records, storing lookup tables, or maintaining state. We set up Data Stores with proper structure and connect them to your scenarios.
Can you migrate my Zaps to Make?
Yes! We regularly help teams migrate from Zapier to Make for cost savings and advanced features. We recreate your workflow logic in Make, often improving it with features Zapier doesn't support. Migration typically takes 2-5 days per workflow set.
How do you handle high-volume scenarios?
Make handles volume well but needs tuning. We implement: batch processing to reduce operations, queue management for rate-limited APIs, scheduled runs during off-peak hours, and execution limits to prevent runaway costs.
Can you build custom Make apps?
Yes! If a native connector doesn't exist, we can build a custom Make app with full authentication, actions, triggers, and searches. Custom apps are available in Premium package or as standalone service.
How long does Make automation take?
Simple scenarios: 2-4 days. Complex multi-branch workflows: 1-2 weeks. Enterprise integrations with custom apps: 2-4 weeks. We provide timeline estimates after the discovery call.
Client Reviews
★★★★ 4.9
based on 98 reviews
★★★★★ 5
Orders Finally Synced
We were hitting limits with our previous automations whenever Shopify line items came through as arrays. AHK.AI built a Make scenario with iterators and aggregators that normalizes bundles, discounts, and multi-warehouse fulfillments before pushing into NetSuite. The router logic handles edge cases like backorders and split shipments, and the HTTP module pulls carrier rates with pagination. The documentation and error routes made it easy for our ops team to monitor without guessing.
Project: Shopify + NetSuite order/fulfillment automation with carrier-rate API pagination and array-based line item transformation in Make
★★★★★ 5
Clean Lead Lifecycle
Our product-led growth pipeline needed conditional branching based on plan type, trial status, and in-app events. They designed a high-volume Make setup using routers to split paths, plus HTTP calls to our internal API for enrichment. The scenario processes event arrays, dedupes users, and writes clean records into HubSpot with the right lifecycle stage. The monitoring and error handling are legit—failed calls get retried and logged with enough context to debug quickly.
Project: Segment event ingestion to HubSpot with internal API enrichment, array processing, and conditional routing in Make
★★★★ 4.5
HIPAA-Friendly Automation
We needed to automate intake without exposing PHI in random places. AHK.AI created a Make scenario that pulls secure form submissions, validates required fields, and routes exceptions to a manual review queue. They used webhooks and the HTTP module to push data into our EHR endpoint, including pagination for patient lookups. The error routes are well thought out, and the documentation is clear enough for compliance review. Only minor tweaks were needed after go-live.
Project: Secure intake workflow: webhook ingestion, field validation, EHR API integration with pagination, and exception routing in Make
★★★★★ 5
Leads Routed Perfectly
Our team was manually sorting leads from Zillow, Realtor.com, and website forms. They built a Make scenario that scores leads, checks duplicates, and routes them to the right agent based on zip code and property type. The iterator/aggregator setup handled multiple inquiries in one payload, which used to break our old workflow. We also got a clean error path that alerts the admin when an MLS lookup fails. It’s been a noticeable time saver.
Project: Multi-source lead intake with dedupe, MLS data lookup via HTTP, agent routing by territory, and monitoring in Make
★★★★ 4
Solid Reconciliation Flow
We needed an automation to reconcile daily transactions between our payment processor and our accounting system. The team used Make routers to separate refunds, chargebacks, and settlements, then mapped everything into QuickBooks with proper memo fields and classes. The HTTP module handled pagination on the processor API and did some transformations on nested arrays. Overall it’s stable and well documented. I’d like slightly more dashboard-style reporting, but the core workflow is dependable.
Project: Daily payment reconciliation: processor API pagination, transaction categorization via routers, and QuickBooks posting in Make
★★★★★ 5
Reporting Without Chaos
As an agency, we juggle multiple ad accounts and client CRMs. AHK.AI built a Make scenario that pulls campaign metrics via HTTP, paginates through accounts, and aggregates results into a single Google Sheet per client. The router logic also tags anomalies (spend spikes, zero conversions) and posts alerts to Slack. The mapping is clean, and the error routes prevent half-written reports. This is the first time our weekly reporting hasn’t felt fragile.
Project: Multi-client ad reporting automation with API pagination, aggregation, anomaly routing, and Slack alerts in Make
★★★★ 4.5
Inventory Updates Automated
We run a small plant and needed inventory movements to sync between our ERP and a barcode scanning app. The payloads come in as arrays of line items, and Zapier kept choking on them. Their Make scenario iterates through each scan, validates SKU/location, and aggregates movements into a batch post to our ERP API. Exception paths are routed to a “needs review” queue with full context. Setup was smooth and the documentation is actually useful.
Project: Barcode scan ingestion with array processing, SKU validation, batch posting to ERP via HTTP, and exception handling in Make
★★★★★ 5
Client Onboarding Streamlined
We needed a repeatable onboarding workflow across clients, but each engagement has different deliverables and approval steps. AHK.AI designed a Make scenario with routers for conditional branching (retainer vs. project, NDA required vs. not) and webhooks to capture intake data. They also built a transformation layer to standardize fields before creating tasks in ClickUp and folders in Drive. The monitoring and error routes gave us confidence to roll it out firm-wide.
Project: Consulting client onboarding automation with conditional routing, standardized field mapping, ClickUp task creation, and monitoring in Make
★★★★ 4.5
Enrollment Data Cleaned
Our enrollment exports were messy—duplicate students, inconsistent program codes, and multi-select fields coming through as arrays. They built a Make workflow that normalizes records, splits multi-select values with iterators, and aggregates updates into our SIS via an HTTP integration. The router handles special cases like international students needing extra documentation. We also appreciated the detailed runbook and error handling so our registrar can troubleshoot without engineering help.
Project: Enrollment data normalization and SIS sync with array handling, conditional routing, and API integration via HTTP in Make
★★★★★ 5
Tracking Updates In Sync
We manage shipments across multiple carriers, and tracking events arrive in batches with deep nested structures. AHK.AI implemented a Make scenario using iterators to process event arrays, then routers to branch by status (exception, delivered, in-transit). The HTTP module paginates carrier endpoints and posts updates into our TMS while also notifying customers via email on exceptions. The error routes and monitoring reduced “where’s my package” tickets almost immediately.
Project: Carrier tracking ingestion with nested array processing, status-based routing, TMS updates via HTTP, and customer exception notifications in Make
