I have created a Make.com automation that runs daily to deliver customized industry research by email for multiple clients. The research is done by Google Gemini deep research. The automation also leverages Google Docs and Make database for persistence across automation runs (the research is aware of past findings). This is currently running, we would like to expand the functionality of this automation and make it more self-serve to easily set up to deliver to new clients that want to use this tool.
Meetings and communications for this job will be in PDT time zone.
We estimate that expanding this tool with take ~40 hours. Please name your rate.
Hello @Eli_Malone-Shkurkin , I can take this on. I’ve built and extended Make workflows that combine LLM research, document generation, persistence, and multi-client onboarding, so the next step here is to turn your current scenario into a cleaner client-ready system with reusable modules, structured inputs, and an easy setup flow for new accounts. I’d review the existing Make architecture, data model, Gemini prompt/research logic, and Google Docs outputs, then tighten reliability, client separation, and state handling across runs. If needed, I can also add an intake form/admin layer so onboarding a new client becomes mostly configuration rather than manual scenario edits. I work comfortably across Make, APIs, webhooks, Google Workspace, and AI workflow design, and I can collaborate in PDT. If you want, send over a brief overview or access to the current scenario and I can outline the fastest path to expansion. Feel free to book a meeting with me here to discuss this further.
Hello @Eli_Malone-Shkurkin , welcome to make.com community, I have worked and have experience with Make.com and l will love to collaborate with you on this you can schedule a call Here and you can checkout my upwork profile Here, for my pastworks and certifications
This is a solid system already. What you’re moving into now is more about making it scalable and self-serve rather than just extending the current automation.
I’ve worked on similar Make setups where we turn single workflows into reusable multi-client systems with proper config and onboarding.
To give you a clear timeline and quote, I’d need a bit of clarity:
• how are you handling client-specific inputs (prompts, industries)?
• how are new clients currently added?
• should each client’s data stay fully isolated or partially shared?
Once I have this, I can map a clean structure and estimate properly
Eli — the bottleneck for “self-serve onboard a new client” on a Gemini Deep Research stack usually isn’t the AI or the Make scenarios. It’s prompt templating: each new client industry (B2B SaaS vs consumer goods vs healthcare) needs different system instructions, output schemas, and seed examples. Without a per-client config layer, every new client becomes a 2-hour engineering task for you — which is why the system feels “almost self-serve” but never quite gets there.
The fix is a 3-layer pattern: (a) per-client config row in Make DB with system prompt + persona + output schema as fields, (b) one onboarding scenario that reads the row and spins up the per-client research workflow, (c) a backfill scenario that primes persistence with 7-10 days of research-output for the new client before going live — so day-one briefs already feel “aware of past findings” the way you described, not cold-start blank.
I’ve shipped multi-tenant + persistence patterns like this in two production systems: a multi-tenant text-to-SQL platform with industry-specific schemas, and a 9-agent Claude system running across Make + Supabase + Slack for a D2C client.
Reply and I’ll DM a 12-minute Loom walking through how I’d layer this on top of your existing setup, plus a sample Make DB config schema. PDT meetings are easy from IST.
I love this project because you’ve already tackled the hardest part—getting the AI to actually “think” and remember past research. Now, we just need to stop you from doing manual work so the system can scale to 100 clients as easily as it does for one.
Here’s exactly how I’ll help you scale this:
One “Brain” for all clients: I’ll restructure your Make database so it acts as a central hub. Instead of duplicating scenarios, we’ll have one master workflow that dynamically pulls the specific keywords, history, and schedules for each client based on their ID.
Self-serve onboarding: I’ll build a bridge so new clients can sign up via a simple form. Once they hit submit, the system will automatically create their Google Doc, set up their database entry, and trigger their first research run without you lifting a finger.
Smart memory management: Scaling “persistence” can get messy and expensive. I’ll optimize how past findings are retrieved so Gemini stays focused on what’s relevant, keeping the research sharp and the token costs low.
PDT availability: I’m completely comfortable with your time zone. I’ve worked with many US-based teams and can be online for our meetings and sync-ups during your business day.
Why me?
I’m Mikhail, a developer and automation specialist. I’ve built “Content Factories” and research pipelines that handle massive data flows for fintech and real estate. I don’t just “link nodes”—I build production-grade systems that include heavy error-handling and scalability as standard.
The Details:
Rate:$50/hour.
Availability: I have 25–35 hours a week free and can start immediately to hit your 40-hour roadmap.
