AI that reads, decides, and writes back.

n8n workflows, typed API clients, and event-driven pipelines connecting your CRM, ERP, helpdesk, and data warehouse to AI that actually drives the system — bi-directional, audited, retried, production-hardened.

Book a discovery call How we build it

The integration layer

One layer to connect every system, and the AI that runs them.

AI doesn't do much when it can't see your data and can't write back to your systems. We wire it up — CRM, ERP, helpdesk, data warehouse, internal APIs — through n8n workflows, typed API clients, and event-driven pipelines, so the AI actually drives the system.

Everything is version-controlled in git, hardened with retries and dead-letter queues, observable through dashboards your ops team can read, and built to clear your security review the first time.

n8n workflows — self-hosted, visually editable, git-tracked
Typed API clients for the systems n8n doesn’t cover
Event-driven pipelines replace polling-on-a-cron
Audit logs + automatic retries + dead-letter queues
Observability dashboards for ops, not just engineering
Cloud-native deploys — AWS, GCP, Azure, or Vercel

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customer_signup_to_crm

active · 2,431 runs today

⚡
Webhook
signup.created
✓ ok
🧠
AI Lead Score
OpenAI · gpt-4o
✓ ok
🟠
HubSpot
Create + tag
✓ ok
💬
Slack
#sales-leads
✓ ok

p95 latency

180 ms

Success

99.7%

Retries

auto

Engagement model

Map the systems, ship the integration layer.

01
Map the surface
List every system, event, and field that needs to flow. Draw the graph before writing a single connector — alignment is the cheap part.
02
Build connectors
n8n workflows for the standard stuff, custom typed clients for the rest. Auth, retries, and contracts standardized across the integration layer.
03
Wire events
Event-driven pipelines replace polling. AI agents subscribe to the stream and react when something actually changes — no cron-based catch-up.
04
Harden + ship
Observability dashboards, audit logs, dead-letter queues, retry policies — production-grade ops before traffic flips.

By the numbers

Integration scale, measured in production.

400+
Connectors
n8n catalog + custom typed clients for everything else.
180 ms
p95 latency
Typical end-to-end latency across event-driven pipelines.
12.4k/d
Calls handled
Per-integration daily volume on a typical mid-market deploy.
Auto
Retry + DLQ
Failed calls retry; permanently failed messages land in a queue.

What you get

One integration layer for every system you already run.

n8n workflows
Self-hosted, visually editable, and version-controlled in git. The integration logic is not locked into a vendor.
Custom APIs
Typed clients with shared auth, retry, and contract handling — when off-the-shelf connectors don’t exist.
Event-driven
Webhook in, webhook out — agents react to real-world events instead of polling on a cron.
Data warehouse aware
Read context for decisions; write outcomes back so analytics stays current.
Audit + retries
Every call logged, every failure retried with backoff, dead messages queued. No silent drops.
Observability
Dashboards for latency, error rate, retry depth — the boring stuff that prevents ops paging you.
Versioned
Workflows promote dev → stage → prod via CI. Rollback is one revert away.
Cloud-native
Runs on your AWS, GCP, Azure, or Vercel — no fourth-vendor sprawl.
Bi-directional sync
Read context from systems, write outcomes back. AI in the loop, not on top of the loop.

Built for these teams

Where "is the data wired up yet" is the blocker.

Operations Teams
CRM ↔ ERP ↔ data warehouse sync with AI on top
Decisions made in one place stop being invisible in another. The data finally agrees.
Sales + RevOps
Lead enrichment, scoring, and CRM hygiene agents
CRM stops being a graveyard; reps act on real-time signal instead of stale fields.
Customer Success
Health scores + churn signals wired to outreach
CSMs get pinged about real accounts; the spray-and-pray QBR cadence dies.
Finance + Billing
Invoice routing, reconciliation, exception triage
Month-end close compresses; finance stops being the bottleneck for everyone else.
IT + Internal Ops
Ticket routing, SaaS-tool sync, employee onboarding
New-hire week 1 productivity stops depending on "did IT remember to provision X?".
Data + Analytics
Reverse ETL with AI insights flowing back into ops
Insights stop dying in dashboards; they get written back into operational systems.

Common questions

What teams ask before they wire up their stack.

What systems can you connect to?
Anything with an API. We have first-class patterns for HubSpot, Salesforce, Pipedrive, Zendesk, Intercom, Front, Slack, Notion, Linear, Jira, Postgres, MySQL, Snowflake, BigQuery, S3, Stripe, and Shopify. For internal / custom systems we write typed API clients with the same retry + auth + audit contract everything else uses. n8n covers ~400 connectors out of the box; for the rest we extend.
Why n8n over Zapier or Make?
Three reasons. (1) Self-hosted — workflows live in your infra, not a third-party SaaS. (2) Visually editable AND version-controlled — workflow files commit to git, review in PR, promote through CI. (3) Code escape hatch — when the visual node falls short, you drop into TypeScript/Python inside the workflow. Zapier and Make are quick for prototypes but break down at production scale and security review.
How are retries and failures handled?
Every external call is wrapped in exponential-backoff retries with a configurable max attempt count. Permanent failures land in a dead-letter queue with the full context — operator can replay manually or after a fix. Transient failures (rate limits, network blips) self-heal. All retries + DLQ events stream to your observability stack so you see patterns, not just individual failures.
Can the AI actually write back to production systems?
Yes — that's the whole point. Read-only AI is decorative. Production AI updates CRM records, creates tickets, sends Slack messages, posts API requests, modifies database rows. We scope the write permissions explicitly per workflow + ship audit logs of every write, so your security team can defend it.
How are sensitive credentials handled?
Vault by default — HashiCorp Vault, AWS Secrets Manager, GCP Secret Manager, or 1Password Secrets Automation. n8n credentials are encrypted at rest with keys you control. Service-to-service traffic is mTLS. No secret ever sits in environment variables on a long-lived box.
How long does a typical integration project take?
For a focused integration (one system pair + AI in the middle, e.g. "intake form → CRM → AI triage → Slack alert"), 2 to 4 weeks. For a multi-system integration layer (CRM ↔ ERP ↔ warehouse ↔ AI), 6 to 10 weeks. We always demo end-to-end on real data by week 2.

Got a stack to AI-enable?

We wire AI into the systems you already run.

Book a discovery call. We'll map your systems, your events, and the failure modes you want covered, then come back with a scoped build plan.

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