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AI Agents & AutomationService

API Integrations & Workflow Automation

Connect systems that don't talk, replace manual exports with a pipeline, or build the internal tool that doesn't exist yet. Scoped and quoted in writing.

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The problem

When this is the right request

A few shapes of task come up often enough to name directly, each one hypothetical rather than a specific engagement:

Hypothetical example: a store’s CRM and its Shopify checkout don’t share data, so every new customer gets entered twice, and the two records drift apart within a month.

Hypothetical example: month-end reporting still means opening four exports — Shopify orders, ad spend from two platforms, a payment processor’s ledger — and pasting them into a spreadsheet that already has broken formulas. The task isn’t the spreadsheet; it’s that nothing produces the numbers on its own.

Hypothetical example: an operations team needs an internal tool that doesn’t exist yet — a page where a warehouse manager can look up an order across two systems that don’t otherwise talk, instead of asking an engineer to run a query each time.

Hypothetical example: an agency wants its own Shopify app, not one shared across unrelated merchant stores, with a clean way to install, update and retire it per client.

Hypothetical example: incoming emails and attachments are turned into a structured draft task, a person reviews it, and only then is it written to the CRM — see AI request processing: from an email to a reviewed task.

The common thread: two or more systems, or a system and a spreadsheet, that should exchange data and currently don’t. If the honest answer is “we don’t know what’s wrong yet” and the systems involved are GA4, Meta, Google Ads or Shopify order data, that’s the Tracking Health Check instead; otherwise it’s a plain discussion of the task.

If you have not yet decided whether to buy, connect or build, my guide to buying, integrating or building an internal tool shows how I compare workflow fit and hypothetical 12–24 month ownership costs.

A clear path to a checkable result.

  1. 01

    Agree the scope

    One written quote and acceptance criteria.

  2. 02

    Build separately

    Implementation in a separate workspace.

  3. 03

    Verify and hand over

    Test against the agreed criteria; document the change and rollback.

Systems

Input systems I work with

These are capabilities, not a fixed menu — what’s actually in scope depends on the task. The list below covers what comes up most often, from Shopify itself to endpoints with no named platform on either end.

Works with

  • Shopify — Admin API, GraphQL, webhooks, checkout/customer-events.
  • Measurement platforms — GA4/BigQuery exports, Meta and Google Ads APIs, where an integration reads or writes alongside existing tracking rather than duplicating it.
  • CRMs and e-mail platforms — via their APIs or webhooks.
  • Spreadsheets — as a source, a destination, or the thing a pipeline replaces once it’s earned that.
  • Internal databases you already run.
  • Generic HTTP/webhook endpoints and queues with no named platform on one or both ends.

In writing

The data contract, written before code

Before anything is built, I write down what’s actually moving: which fields, who owns each one, which direction data flows, how often, what makes one record the same as another, and what “done” means for the pipeline. Agreeing this in writing first guards against the two failures I see most often.

Those two: a field silently overwritten by the wrong system, and duplicate records from a run that fired twice. If you want to draft this yourself before we talk, the API integration brief walks through every field with a filled example and a blank template.

Data contract

Hypothetical example, a CRM-to-Shopify customer sync

Comparison table — scroll horizontally to see all columns

FieldOwnerDirectionCadenceIdempotency key
Customer emailCRMCRM → ShopifyReal-time (webhook)Email, lowercased
Order totalShopifyShopify → CRMReal-time (webhook)Shopify order ID
Fulfillment statusShopifyShopify → CRMEvery 15 min (polled)Order ID + status timestamp

“Done” here means every order with a matching CRM customer shows its total and fulfillment status in the CRM within the agreed window, and a re-sent webhook never creates a second record.

Operations

Error handling and operations

An integration that only works when nothing goes wrong isn’t finished. Error handling is matched to what the task needs, covering failure, visibility, and recovery rather than treating the happy path as the whole job.

Attached

The integration retry acceptance guide shows how I test duplicate, wrong-order, timeout and restart cases, with results from a local test harness.

Part of this is the discipline behind DeploTeka, my own lifecycle platform (not client work), built for provisioning one custom-distribution Shopify app per client store. The write-up shows how it runs in production for my own product, Fixel Pixel.

  • Retries with limits for transient failures, without retrying a permanent one into a loop.

  • A place where failed records wait for review when a run exhausts its retries, so a failure doesn’t disappear.

  • Alerts that reach a person, not just a log line.

  • Safe re-runs: if a job runs twice or stops halfway, the same record is never processed twice.

  • Logging that reconstructs what happened without logging secrets or customer data into it, with credentials held in a vault, never in code.

  • A documented rollback or kill switch that doesn’t require me on a call.

Handover

What you receive, and how it is verified

The deliverables are the ones listed above: the written data contract, the build itself in a workspace separate from anything live, matched error handling, a runbook, and verification against the acceptance criteria agreed before work started — not order-level reconciliation, which is specific to tracking work.

Acceptance means the pipeline does what the written scope said, checked against real or realistic data before it’s called done, with a documented way to undo the change. Defects in what was delivered, inside the original scope, are fixed free for 30 days after acceptance. New requirements, a third-party API changing its contract, and unrelated problems are separate scope, quoted on their own.

Handover kit

  • A written data contract — fields, direction, owner and cadence — agreed before code is written
  • The integration or automation itself: webhook, scheduled job, internal tool or API connection, built in a separate workspace
  • Failures don't disappear: limited retries, a place where failed records wait for review, and an alert that reaches a person
  • Safe re-runs: if a job runs twice or stops halfway, the same record is never processed twice
  • A short runbook — what the integration does, where secrets live, how to roll it back
  • Verification against the acceptance criteria agreed before work started

Pricing

How it is priced

Bounded work gets one fixed sum agreed before it starts; open-ended work is scoped and priced by result. Ongoing work runs as a monthly partnership instead of repeat one-off quotes.

Discussing the task and a first written view of scope are free. If a larger build needs real design work before it can be quoted, that step is priced separately, and you agree to it before it starts. See pricing for the full catalogue.

Price list

  1. Focused Fix from USD 450 One bounded integration or fix: connect two systems on a defined path, repair a broken pipeline, add one endpoint; one fixed sum and the acceptance criteria before work starts.
  2. Custom Engineering Project from USD 1,500 An application, a multi-system integration, an automation or a complex migration, priced by the result and the agreed scope.
  3. Technical Partnership USD 1,000/month For 8 engineering hours in an agreed area, one active task at a time; hours do not carry over.
  4. Technical Working Session USD 195 A genuinely small, well-defined question, worked through in one session instead of a project, with a short written summary.

Price and timeline

Discuss your project

If the task is clear, I quote it directly. If it isn’t yet, I’ll tell you what I need to see first.

Send one paragraph to next@taskfordaniel.com or use the contact page: the systems involved, what’s breaking or missing today, and what “done” would look like. I usually reply within one working day with whether I’d take it on, what I’d need to see first, and how I’d scope it. This is a direct quote, not a diagnosis. If your question turns out to be about tracking whose cause is unknown, the Tracking Health Check is one option.

QuoteFixed before work starts

Price
Focused Fix from USD 450 · Custom Engineering Project from USD 1,500 · quoted in writing
Turnaround
From a few days for a bounded integration; larger builds are scheduled against the agreed scope once it is defined
Describe your task

Questions

FAQ

Is this the same as the Tracking Health Check?

No. The Health Check is a read-only tracking diagnosis — it checks whether GA4, Meta, Google Ads and Shopify agree with your orders. Integration and automation work doesn't go through it at all: you describe the systems and the task, I answer with a scope and a quote. If the task turns out to be a tracking problem in disguise, I'll say so instead of quoting the wrong thing.

What if I don't know exactly what I need yet?

That's normal, and it's what the first step is for. Describe the systems involved and what isn't working — send an export, a screenshot of the manual process, or just what breaks — and I'll come back with what I'd need to see next and how I'd scope it, in writing, at no cost.

Do you write the data contract, or do I?

I write the first draft from what you tell me about the systems and the task, then we agree it together before any code is written. It's a short document, not a spec exercise — fields, who owns each one, which direction data moves, how often, and what counts as a duplicate.

What happens if a run fails halfway through?

That's designed for up front, not patched afterward. Where a job can be retried, it's built so a retry doesn't reprocess or double-send anything already handled — an idempotency key on the record, not a hope that it won't run twice. Where a run genuinely can't recover, it lands in a dead-letter path with an alert, not a silent gap in your data.

Can you build a custom Shopify app, not just connect existing ones?

Yes — that's Custom Engineering Project scope for the app itself. For distributing it per client store I built DeploTeka, the platform I founded and own: it provisions one custom-distribution app per client store as a tenant-scoped, idempotent, resumable run that fences out stale workers, rather than a script that hopes it finishes. It has run in production for my own product, Fixel Pixel, since July 2026. No outside vendor has completed the journey on DeploTeka yet; for your app, I build the same kind of per-store provisioning and release setup as engineering work.

Where to go next

Daniil Maximkin

Hi, I’m Daniil.

I work with you from defining the problem to implementation and handover. You talk to the person who does the work. I work in English and Russian.

My story My work

What do you need to get working?

Describe your task

The first answer is free, within one working day. Or write directly: next@taskfordaniel.com