01
Complex branching scenarios
Visual router lets you build flows that branch into 5–20 paths on one canvas. Zapier could never.
Make (Integromat) Automation
Visual workflow automation — powerful + affordable.
Make is the visual platform for teams that need complex branching, iterators, and routers — without Zapier's price tag at scale. We design and deploy enterprise-grade Make scenarios with built-in error handling.
From $4,000 · 1,000+ integrations · 3–5× cheaper than Zapier at volume
Use cases
Six places Make outperforms Zapier.
02
Iterators + aggregators
Process arrays, loop through records, aggregate results — Make handles batch operations natively.
03
Data transformation pipelines
Reshape, merge, split, transform between systems with built-in mapping tools. Cleaner than custom code.
04
Visual error handling
Drag-and-drop error routes, retry logic, fallback paths. See exactly where things break.
05
Multi-scenario orchestration
Chain scenarios via webhooks, schedules, data stores. Build modular automation architectures.
06
Scheduled batch processing
Sync inventories, generate reports, update dashboards on schedule — all visually designed.
When to pick Make
Make wins when complexity meets cost-sensitivity.
- Complex branching. 5+ path routers handled visually, not with workarounds.
- Array processing. Iterators and aggregators are first-class — no JSON-parse hacks.
- Cost matters at scale. Operations-based pricing typically 3–5× cheaper than Zapier for data-heavy flows.
- Visual error handling. Drag-and-drop error routes vs Zapier's after-the-fact retry config.
Frequently asked
Common questions.
When should I use n8n vs Zapier vs Make?
Zapier wins on simplicity and breadth — 6,000+ integrations, near-zero learning curve, good for marketers and non-engineers. Pricing scales aggressively with volume. Make wins on visual orchestration of medium-complexity flows — better than Zapier on conditional logic, cheaper at volume. n8n wins on engineer-grade workflows, self-hosting, custom code nodes, and AI-native features. Default rule: Zapier for <5 step flows owned by non-engineers, Make for medium complexity, n8n for engineer-owned production workflows.
How do you secure workflow automations against prompt injection?
Five layers. Input sanitization — strip or quarantine instruction-like text from user-controlled fields. Privilege separation — agents that read untrusted content cannot directly call high-privilege tools. Tool-call confirmation — high-stakes actions require human approval or a separate verification step. Output validation — every tool call's arguments validated against a strict schema; anomalies fail closed. Adversarial test suite — a CI test set of known prompt-injection attacks runs on every release.
How does pricing work — fixed-price, T&M, or retainer?
All three. Fixed-price for AI MVPs and agent builds where scope is well-defined after a discovery sprint. Time-and-materials for staff augmentation, billed monthly with a not-to-exceed ceiling. Retainer for ongoing optimization, eval-harness operations, and managed AI services — flat monthly fee for a defined scope of capacity.
How are deliverables handed off?
Every engagement ends with a handoff package: production deployment, architecture documentation, eval harness with golden test sets, observability dashboards with documented thresholds, on-call runbook, model upgrade procedure, and a recorded walkthrough. Plus a 30-day post-handoff window for questions and clarifications at no cost.
What does it cost to build an AI agent?
A production AI agent at AISD typically costs $40,000–$150,000 depending on complexity. Drivers: number of integrated systems, evaluation rigor required, compliance overhead, and ongoing operational scope. Prototypes alone are cheaper ($10k–$25k) but rarely worth it without a path to production.
How do you ensure AI features are reliable in production?
Five layers: an offline eval harness with golden test sets run on every PR; confidence thresholds and structured-output validation that gate downstream side effects; runtime observability — every model call logged with inputs, outputs, latency, cost; circuit breakers and deterministic fallbacks for every model dependency; and a weekly review ritual where prompt regressions get caught before they become incidents.
How is AISD different from a typical software development agency?
Three differences. First, every AISD engineer is senior — minimum 5 years building production software, with shipped AI features. Second, we publish hourly engagement bands and project ranges so you know roughly what an engagement costs before the first call. Third, we take fewer concurrent projects so a partner stays close to delivery.
How is AI consulting different from AI development?
Consulting produces decisions and plans; development produces working software. AISD does both, often in sequence: a consulting engagement scopes the architecture and roadmap, then a build engagement implements it. Consulting alone is right when you're early in the AI journey, evaluating vendors, or auditing existing work. Build alone is right when scope is already clear. Most AISD customers do a 2-week paid discovery sprint first — that's a consulting engagement that produces a fixed-price build proposal.