Automation & AI

Automation engineered to keep running.

We build Zapier, Make and n8n workflows with error handling designed in from the start, and AI agents on Claude and OpenAI that carry real workload. Every run is monitored, and a person is alerted when something needs attention.

ZapierZapier MakeMake n8nn8n ClaudeClaude OpenAIOpenAI FirecrawlFirecrawl
retry, three tries Trigger webhook or schedule AI step checks Done checks passed Human review needs judgment Alert failed three times
Fig. 01Checks gate every run. Confident runs finish on their own, judgment calls go to a person, and anything unresolved is escalated within minutes.

Why this exists

The gap between a demo and a system.

Automation tools demo well, but in production webhooks change, APIs rate-limit and data arrives in the wrong format. We engineer for those conditions, so every failure is caught, logged and handled.

The silent failure

A workflow without error handling can stop moving records and give no sign. Every build we ship logs each run and alerts a person on failure.

The fragile stack

Forty Zaps built by four people over three years become hard to change safely. We map, prune and document inherited stacks so they can be maintained with confidence.

Misapplied AI

AI pays off when it is pointed at real workload, like the inbox triage that would save ten hours a week. We scope every agent around the hours it replaces.

What we deliver

Builds with reliability engineered in.

Error handling, logging, documentation and a human review path are in every quote by default. That is the difference you are paying for.

Talk through your workflow

Workflow builds: Zapier, Make and n8n, chosen for your case, not our preference

Rescues and consolidation: the forty-Zap stack mapped, pruned and documented

AI agents: inbox triage, lead enrichment, research and reporting on Claude or OpenAI, with human review gates

Scrapers and data pipelines: market and funder research with Firecrawl and Apify, deduped into your CRM

Custom glue: webhooks, API work and small scripts where no-code tools reach their limits

Error architecture: retries, dead-letter queues, alert routing, and a run log you can audit

Cost control: task and operation budgets that keep the tool bill predictable

Documentation: every workflow named, mapped and explained so your team can maintain it

Fig. 02A workflow the way we ship it: checks on every step, a run history anyone can read, and escalation to a person built in.

Representative scenarios

Where automation engagements usually start.

Every build gets its own written quote after a 30-minute call. These are the shapes we see most.

4 TO 8 HRS · $600 TO $1,200

One workflow, done properly

The quote-to-invoice flow, the lead router, the report assembler. Built, tested against bad data, alarmed and documented.

8 TO 15 HRS · $1,200 TO $2,250

An AI agent that earns its keep

Triage, drafting, enrichment or research, wired into your tools with review gates. Measured against the hours it replaces.

10 TO 20 HRS · $1,500 TO $3,000

The automation rescue

Your existing stack mapped, failure points fixed, redundant workflows retired, the rest documented and alarmed.

Hours billed as actuals against a written cap. Tool subscriptions stay in your accounts, on your cards.

See a real one: shift allocation parsed by AI, from hours of coordination to near-instant

Platform honesty

Zapier, Make or n8n: a measured choice.

We build on all three and resell none. The honest short version:

PlatformUsually right whenWatch out for
ZapierNon-technical team, common apps, speed matters mostTask pricing gets expensive at volume
MakeComplex branching logic on a budgetA learning curve your team must climb
n8nHigh volume, custom code, data control or self-hostingSomeone has to own the instance
The full comparison, with 2026 pricing and a worked example

Automation questions

Asked before most automation engagements.

Will AI agents work with our data? Is it safe? +

Agents run inside your accounts with API keys you control, and we design for data minimization: the model sees only what the task needs. For Canadian orgs we work within PIPEDA expectations, and we will walk through exactly what data goes where before anything is built.

Should every AI output be reviewed by a human? +

For anything customer-facing or money-touching, yes, and we build the review gate in. For internal classification and research tasks, sampling is usually enough. We will tell you which is which rather than sell you blanket autonomy.

What happens when an automation breaks after you leave? +

First, you will know, because every build alerts a person the moment a run fails. Anything broken within 30 days of handoff is fixed free. After that, most fixes are under an hour, and the documentation means many never reach us at all.

Can you take over automations someone else built? +

Yes. We start by mapping what exists and what each piece does, because half of most inherited stacks is redundant. You get the map either way; it is useful even if you stop there.

Do we need n8n self-hosted for privacy? +

Sometimes, and less often than the internet suggests. Self-hosting gives you data control and lower per-run costs, but someone has to own updates and uptime. We will price both paths honestly, including the maintenance time self-hosting really takes.

Bring us your most time-consuming task.

Thirty minutes. We will tell you if it can be automated, what it takes, and whether the hours pencil out.

$150/hr flat · scope in writing before you sign · you own everything