Why pay you when n8n and Make are no-code?

Because the no-code tool is the easy 20 percent of the problem. n8n and Make are genuinely good at moving data from A to B with a simple rule attached. They struggle with anything that needs real judgment. Consider a decision that depends on the content of a message rather than its format. Consider recovery when a step fails in a way that cannot simply be retried. Consider visibility into what hundreds of runs did across a day. Consider multi-step decisions that span several systems, and connections to internal tools that were never built with an integration in mind. Designing the agent's logic, writing prompts that hold up on real inputs, and building the safety nets around them is the actual work. It happens on top of whichever tool we choose, not instead of it.

Which workflow tool do you recommend?

It depends on what the specific workflow needs to do, not on which tool we prefer in general. n8n is our default for connecting tools and moving data: it is inexpensive, fast to build in, you can self-host it, and it has a large library of pre-built integrations. Make suits teams that prefer a visual drag-and-drop editor over n8n's node style. Temporal is the right choice for long-running workflows where reliability is non-negotiable, such as a financial close process or a multi-step compliance check that cannot be left half-done. Custom code is the answer when the workflow is core to how your business runs and you do not want to depend on any vendor's roadmap. We choose per workflow, not per client.

What about LangChain, LangGraph, or CrewAI?

We use these frameworks where they genuinely fit, but we do not build your system so that it depends on any one of them staying the same. AI frameworks change quickly, sometimes with breaking changes between minor versions. We write the integration layer so the framework underneath can be swapped without a full rebuild. What actually holds its value over time is the prompts, the set of tools the agent is allowed to call, and the logging around it, not the framework name on the box.

What if the agent does something it shouldn't?

Three layers of protection, all required before anything goes to production. First, any action that cannot be undone (deleting data, issuing a payment, sending a message externally) requires a human to approve it before it happens. Second, a new or changed agent version runs in a test mode against real data without taking live actions before it is allowed to go live. Third, every action is logged in enough detail to replay exactly what happened and fix a mistake after the fact. We do not ship an agent to production missing any one of the three.

What is a typical ROI?

It depends entirely on the workflow, and we do not have a reliable general figure to give you here. A workflow that runs 500 times a day and saves two minutes each time returns a very different number than one that runs 20 times a month and saves an hour each time. The honest way to answer this question is to run the actual math on your specific workflow, which is what we do in the proposal before you sign anything. If you want a number before that conversation, the two inputs that determine it are how often the workflow runs and how much human time it currently costs per run. Multiply those together and compare the result to the project price above.

AI Agents and Workflow Automation

AI agents and automated workflows, built to run in production

An AI agent, in this context, is a small piece of software. It reads a piece of information, such as an email, a support ticket, or a line in a spreadsheet. It decides what should happen to that information, and then it does that thing: it updates a record, sends a message, routes a task, or flags it for a human. Workflow automation is the plumbing around the agent. It is the tool that moves data between your systems and calls the agent at the right step. n8n, Make, Temporal, and plain custom code are the four we use most. Which one fits depends on the workflow, not on a preference.

Most companies end up with 20 to 30 SaaS tools and a scatter of Zaps holding them together. The gap between the tools is where a person's time goes. Someone copies a status from project management into the CRM. Someone pulls numbers from one report into another. Someone re-types what a support ticket said into a billing system. That work is repetitive by definition, and repetitive work is exactly what can be automated with an agent that reads the same fields a person would read and acts on them the same way.

This is a project, not a subscription to a platform. We design the agent. We write and test the logic. We wire it into your existing systems with your data. We hand over something that keeps running after we are gone: source code in your repository, or a workflow in your own n8n or Make account, plus a way to see what it did and why.

When this is the right service

Your team spends real hours a week on high-volume repetitive work in operations, sales, customer success, finance, or HR, and a Zapier or Make setup built for simple data-moving has hit its limit.
You tried building agents in-house and now maintain a system nobody fully understands.
You need agents that take real action, such as classifying, routing, or escalating to a human when unsure. You do not need a chatbot that only answers questions.
You are moving off Zapier or Make onto something more capable, and you want the architecture decided before you commit engineering time.
You can name the workflow that costs the most human time right now: invoice processing, lead enrichment, support ticket triage, compliance review, contract redlining, or something like them.

If your problem is documents arriving in bulk that need to be read, extracted, and routed, rather than a multi-step process across tools, start at AI Document Processing instead. This page covers the second kind of problem.

What a project includes

A typical engagement covers 1 to 4 specific high-value workflows, scoped to the size of the problem:

Tier	Price	Timeline	Scope
Single workflow	$25,000 to $50,000	4 to 6 weeks	1 workflow, up to 8 integrations, up to 3 AI decision points
Multi-workflow	$50,000 to $100,000	6 to 10 weeks	2 to 3 connected workflows, up to 15 integrations, up to 8 AI decision points
Department-wide	$100,000 to $150,000	10 to 12 weeks	4+ workflows for one department, up to 25 integrations, up to 15 AI decision points
Ongoing maintenance	$2,500 to $7,500 / month	Monthly, optional	Health checks, tuning, vendor upgrade support

Every tier includes the same five project stages, no matter its size:

Workflow audit. We identify the 1 to 4 highest-value repetitive tasks in your team's workflow, based on how often they happen and how much time each one costs.
Architecture design. A written decision on which tool to use (n8n, Make, Temporal, or custom code), exactly what each agent is and is not allowed to do, how you will see what it is doing, and what happens when something breaks.
Build. Full implementation inside your environment. Code lives in your repository or in your own n8n or Make account, never ours.
Human-in-the-loop fallback. Any action that cannot be undone, such as a payment, a deletion, or a message sent externally, waits for a human to approve it. Anything reversible, the agent does on its own.
Logs you can audit. Every action is logged. Every decision can be traced back to the prompt version that produced it. You can replay any run.

Tier detail

Single workflow ($25,000 to $50,000, 4 to 6 weeks)

For one high-value task your team repeats often. Examples: invoice processing, lead routing, support ticket triage, and compliance review of one document type.

What's included:

Discovery session (2 hours) and a written process map of the task as it works today.
Architecture document: which tool we use, why, and how it fits your existing stack.
Up to 8 system integrations (CRM, email, Slack, billing, internal database, and similar).
Up to 3 AI decision points in the workflow (for example: classify intent, pick a route, escalate to a human).
A logging dashboard so you can audit what the agent did and why.
Acceptance test on 100 of your real sample inputs (see below).
2 weeks of post-launch tuning.
Source code delivered into your repository. You own it outright.

Multi-workflow ($50,000 to $100,000, 6 to 10 weeks)

For 2 to 3 connected workflows where the output of one becomes the input of the next. Example: an inbound lead arrives, gets enriched, gets routed, and triggers first-touch outreach.

Everything in Single workflow, plus:

2 to 3 workflows that share state and pass data to each other.
Up to 15 system integrations.
Up to 8 AI decision points across the workflows.
Cross-workflow observability: you see the full journey a piece of work took, not just one step of it.
Workflow-level error handling and retry logic.
4 weeks of post-launch tuning.

Department-wide ($100,000 to $150,000, 10 to 12 weeks)

For 4 or more workflows that cover one department end-to-end. Example: all of customer support, all of accounts payable, or all of sales operations.

Everything in Multi-workflow, plus:

4 or more workflows for one department.
Up to 25 system integrations.
Up to 15 AI decision points.
A department-level dashboard: a manager's view of how every agent is performing.
Custom alerts routed to your team when an agent needs human review.
A training session for the department on what to expect from the system and how to give feedback on it.
6 weeks of post-launch tuning.

Ongoing maintenance ($2,500 to $7,500 per month)

Optional. Your team can also run its own maintenance after handoff, since you own the source code.

What's included:

Monthly health check report: success rate, drift, error patterns, and what is degrading.
Up to 4 hours of prompt and workflow tuning per month.
Vendor and model upgrade support (an AI provider ships a new model, or n8n changes a connector).
Same-week response when something stops working.
A quarterly, 1-hour architecture review.

How the acceptance test and guarantee work

Every project is accepted or rejected by a written test, not a judgment call. The mechanics are agreed before we start:

Step	What happens
1. Agree the target	Before the build starts, we agree the success rate the agent must hit, usually 85 to 95 percent depending on the workflow.
2. Collect real inputs	We use 100 of your actual sample inputs, not synthetic test cases.
3. Run it live, together	The test is written into the contract and run with you watching. No pass-fail decision happens behind closed doors.
4. Pass	The final invoice goes out.
5. Miss the target	We rebuild the workflow at our own cost until it hits the agreed number.

Payment terms

50 percent on contract signing, 25 percent at the project midpoint, 25 percent on acceptance. INR pricing is available on request for India-based clients.

Selected work

The pages below show the approach to each kind of automation in detail:

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Not sure if this is the right page? This page is for multi-step processes that move work between tools and systems. If your problem is high-volume documents arriving that need to be read, extracted, and routed, start at AI Document Processing instead.