Home›Services›Process Automation

Solutions · Process Automation

AI automation studio · business process automation · production-grade

AI automation and business process automation that actually runs in production — error handling, retries, observability built in

Most "AI automation" builds are happy-path demos — they work the day they ship and break the first time an API rate-limits. We are an AI automation studio (not an agency): branch-and-merge instead of linear flows, error workflows for every critical path, retry-with-backoff on flaky APIs, observability so you can debug what you cannot see. n8n / Make / Zapier — picked based on your volume and operational appetite.

Book the free audit→See pricing

99.5%+

production reliability

5–10×

build time vs happy-path

Native

observability + retries

process.dgcore — Production-grade workflow

LIVE

▸ Branch-and-merge · auto retry-with-backoff

✓Webhook fired0.3s

API call · Stripequeued

API call · CRMqueued

Retry succeeded · attempt 2queued

Branch · enrichmentqueued

Slack notificationqueued

Reliability

99.5%+

Retries handled

4 / day

Certified on the platforms you already use80+ builds shipped

GoHighLevel

HubSpot

n8n

Make.com

Zapier

Klaviyo

Airtable

▸ Our verdict on Process Automation

Process automation pays back when (1) the work is repeatable enough to define, (2) the volume is high enough that human cost exceeds build cost, and (3) the error tolerance is real (not "it broke and we lost data"). All three matter. The first is obvious; the second drives ROI math; the third is what separates demo automations from production ones.

What we deliver

What our Process Automation engagements cover

Standard scope. Custom scope available on the audit.

◆

Branch-and-merge architecture

Workflows designed to handle multiple paths and edge cases natively. Not "if A fail, exit" — "if A fail, route to B, log, alert, retry".

⌬

Error workflows + retry

Every critical path has an error workflow. Retry-with-backoff on flaky APIs. Alerting when failure rate crosses threshold. Not "we noticed when a customer complained".

✦

Observability

Per-step traces, latency tracking, error rate dashboards. You can debug what you can see; observability is what makes that possible.

⌘

Platform selection

n8n for high volume + custom code. Make for medium + visual collaboration. Zapier for speed + simple flows. We pick based on your shape.

Engagement model

From audit to live Process Automation build in 4 steps

Same engagement shape as every digicore101 build. Predictable timeline, predictable cost, no scope creep.

017 days

AI Audit

60-min strategy session, stack map, leak analysis, costed roadmap. Vendor-neutral — yours to keep.

·Architecture diagram
·Build sequence
·Cost + timeline lock

025–10 days

Architecture

Process Automation schema, automations on paper, integration map, AI agent personas.

·Approved schema
·Sign-off on flows
·Migration plan if applicable

032–6 weeks

Build & Deploy

Weekly demos, staged rollout, full handoff documentation. You own everything.

·Live system
·Loom walkthroughs
·Team training session

04Ongoing

Train & Support

Retainer keeps the Process Automation stack tuned, monitored, and improving — not just running.

·Slack channel
·Weekly tune cycle
·Monthly reporting

The math

Happy-path automation vs production-grade · 12-month TCO

Happy-path automation looks cheap on day one and breaks the first time an API rate-limits. Production-grade automation costs more upfront and dramatically less in production failure cost.

✓Happy-path build: $500 · 95% reliability · ~30 hr/yr founder firefighting

✓Production build: $1,997+ · 99.5%+ reliability · 0 hr founder firefighting

✓12-month founder cost of firefighting: $6k+

✓Plus customer experience cost when automations fail silently

Production cost · 12 months

Happy-path build$6,500

Build + founder firefighting

Production-grade$2,000

Build only · zero firefighting

Reliability gain+4.5pts

95% → 99.5%+

The math

−$4.5k/yr

plus customer experience saved

Default vs Process Automation

How happy-path automation compares to production-grade

Honest comparison. We will tell you when the simpler answer is right.

CapabilityHappy-path automationProduction-grade

Error handlingEmail alert · manual retryBranch-and-merge · auto retry-with-backoff

ObservabilityConsole logsPer-step traces · alerting on threshold

Edge-case handlingFalls overDesigned for · tested before deploy

Reliability target~95%99.5%+

Build time1 day1–2 weeks

Cost$500$1,997+

Recent automation builds

How real teams used this

Names anonymized where requested.

AI workflow

B2B · multi-agent AI workflow on n8n

LangChain agents with shared memory + RAG over 12k internal docs.

Multi-agentRAGProduction

Sync

DTC · Stripe ↔ HubSpot bidirectional

Production-grade sync with conflict resolution and dead-letter queue.

Bi-directionalProductionDLQ

Reactivation

Coach · CRM → AI → outbound

CRM-triggered AI workflow that drafts personalized re-engagement and queues for founder review.

CRM-triggeredAI re-engageFounder review

Cleanup

Agency · Make scenario audit

Audit found 41% of operations were dead branches. Cleaned and consolidated. Net spend down 38%.

Audit−41% bloat−38% spend

When this fits

Honest scope — and who shouldn't engage

Production-grade automation pays back when reliability matters and volume is real.

✓ Engage when

Workflow runs 50+ times/mo
Reliability compounds at this volume.
Failure cost is real
Lost customer · lost data · lost trust — production-grade is non-negotiable.
AI workflows
AI workflows fail more than rule-based. Observability + error handling are mandatory.

✗ Don't engage when

Internal-only happy-path workflows
Cheaper builds work fine when failure is invisible to customers.
Under 10 runs/month
Volume too low to justify production-grade.
You want it shipped today
Production-grade takes 1–2 weeks. Happy-path takes 1 day.

AI automation — operator's guide

What is AI automation? The 5 patterns that run in production

AI automation combines machine learning, natural language processing, and workflow tools to handle business processes that previously required manual work at every step.

6 sections · long readRead the full guide→

Pricing depends on scope

Every Process Automation build is a different shape.

We don't quote off a feature checklist — we quote off your stack, your bottleneck, and the build phases that actually move revenue. The audit is the front door: free, 7-day costed roadmap, vendor-neutral.

Book the free audit→See full pricing

FAQ

Questions before we start

Why does this cost more than a basic automation build?+

Because production-grade automation is real engineering work. Error handling, observability, retry logic, and edge-case testing take 5–10× the build time of a happy-path demo. The math: you pay 4× upfront for 95% less production breakage over 12 months.

How do you pick n8n vs Make vs Zapier?+

Volume + operational appetite. Above 5,000 ops/mo with willingness to host: n8n. 500–5,000 ops/mo, zero ops: Make. Under 500 ops/mo or quick wins: Zapier. We will recommend on the audit.

What about AI workflows?+

Yes — most production automation now includes AI nodes. We build LangChain agents on n8n, OpenAI modules on Make, AI Zaps on Zapier — with proper error handling and budget tracking that toy demos do not have.

How long is a typical build?+

1–2 weeks for a single workflow. 4–8 weeks for a full automation layer (10–20 workflows with shared infrastructure). Retainer $797+/mo to keep tuning + ship new ones.

What does it cost?+

Full Build $1,997+ (production-grade with error handling). Retainer $797+/mo for ongoing builds + tuning.

Keep exploring

Where Process Automation fits in the bigger picture

Most engagements layer 2–3 platforms with a service shape. These pages map the surrounding territory.

Ready when you are

Ready to scope your Process Automation build?

Book the free AI System Audit. We map your stack, find the leaks, and deliver a build roadmap in 7 days. Vendor-neutral.

Book the free audit→See pricing →