Hi Make community
I’m looking for a senior Make.com (Integromat) expert to help me audit, optimize, and stabilize our existing automation setup.
Objective
We currently run around ~1,000,000 operations/month and want to reduce that to ~500,000 (target: 300,000) — while also reducing errors, improving reliability, and making scenarios easier to maintain.
Additionally, we have agentic/AI automations and want to reduce OpenAI usage/credits/costs as well (prompt/runtime optimization, fewer calls, smarter gating, caching, etc.).
This is not a “rebuild everything from scratch” project. I built the scenarios myself. I’m looking for someone who can challenge the architecture, identify the highest-impact improvements, and work with me in tandem to implement the biggest wins—while explaining why each change is recommended and what impact it has.
Hello @Dominik_Geissler , welcome to make.com community, I would love to collaborate with you on this.
I have reduced client ops from over 500k to 150k/month via early filtering, aggregation, Data Store caching, and webhook migration and cut OpenAI costs 60% through prompt compression, gating logic, model downgrade (GPT-3.5 vs GPT-4), caching, and batching
My Approach will be
Week 1: Audit execution logs, identify top operation consumers (80/20 rule), map error patterns
Weeks 2-3: Implement high-impact optimizations (early filtering, idempotency, enrichment logic, OpenAI gating/caching)
Week 4: Testing, monitoring, documentation
Experience: Make.com (3+ years), Apify, Clay, Close CRM, OpenAI optimization
I am available to start immediately, 30-40 hrs/week and my hourly rate is $30/hour
Hi Dominik, I’ve reviewed this and I understand the core issue you’re trying to solve.
I can help you design and implement a clean, reliable automation that fixes this without unnecessary rebuilding. I’ve worked on similar workflows involving Make.com , APIs, conditional logic, and production-grade error handling, and I can start immediately.
If it helps, I’m happy to do a quick review of your current setup and outline the exact fix before proceeding. Let me know if you’d like to jump on a short call here
This is a strong brief and exactly the kind of Make setup where small architectural changes can save a massive number of operations and AI costs.
I work mainly on auditing and optimizing existing Make scenarios, especially high-volume stacks with Apify, enrichment tools, CRMs, and LLMs. The focus is usually identifying the top operation drivers, reducing unnecessary module runs with early filtering and aggregation, adding idempotency and caching, and tightening error handling around missing records and invalid fields. For AI flows, I typically reduce spend by smarter gating, batching, caching deterministic outputs, and model selection.
You can check my website portfolio for relevant work. If this looks aligned, feel free to email me directly at folafoluwaolaneye@gmail.com and we can quickly assess scope, priorities, and the biggest wins before committing. I’m also happy to jump on a short call if that’s easier, and we can formalize things later via my Fiverr workspace if we move forward.
I’m excited to help optimize your automation setup, reduce operations, improve reliability, and cut OpenAI costs. As a senior Make.com expert, I’ll audit your current workflows, prioritize optimizations, and implement strategies like prompt compression, batching, and caching to reduce costs and improve performance. I’ll also focus on enhancing error handling and reliability.
1M → 300–500k ops/month is a 60–70% reduction, which almost always comes from three buckets: (1) scenarios running on every trigger when they should gate, (2) LLM calls that re-request the same context instead of caching it, and (3) retry storms on Apify / Clay / Close API failures with no dead-letter. Which of those drives most of the op count decides the whole plan.
Week-1 diagnostic I’d run:
Op-by-scenario audit: which 5–10 scenarios account for 80% of the monthly op count (it’s almost always Pareto)
Per-scenario dollar cost: LLM tokens + Apify compute + Clay credits — so “saving ops” is traded against real money, not just Make.com usage
Clay enrichment: cache by email hash so the same contact isn’t re-enriched every Close CRM pass
Apify: batched runs on a schedule instead of webhook-per-event
Make.com: router + filter gating so scenarios short-circuit when the payload doesn’t need full processing
Pair-optimization cadence suits this work — daily 30-min stand-ups alongside the build, the faster a change lands, the faster we re-scope the next one.
Reference repo showing the schema / audit pattern I apply to LLM orchestration:
DM to set up a call this week and run the Week-1 diagnostic past your current setup.
I would treat this as an operations-stabilization pass before trying to optimize cost.
First slice: instrument the highest-volume scenario, classify which operations are real work vs retries, polling, and duplicates, then remove duplicate searches, cache or enrich once, add run guards, and split the scenario where failures are causing expensive reruns. With Apify, Clay, and Close CRM, I would also lock the data contract and idempotency key before changing modules so the new version is measurable.
TinyOps Studio can quote a fixed diagnostic plus first stabilization pass, then a monthly maintenance lane if it lowers operations materially.
If this is still open, DM me the highest-volume scenario and current monthly operation count, and I can scope the first pass.
Check my profile:
Book a quick call: