Coordination overhead drops
Handoffs between systems stop requiring humans. Approvals and genuine exceptions become the only touchpoints left — everything routine executes continuously.
Automation & AI Integration Engineering
Automation that
compounds.
We design, ship, and operate the integration layer for companies with real operational complexity — AI agents, event-driven pipelines, and system-to-system automation, built to production standard.
- Systems integrated
- 120+
- Hours automated / yr
- 40,000+
- Pipeline uptime
- 99.95%
In production at
US freight operator$120M revenue · 9-person ops team
Multi-channel retailer9-figure GMV · 11 systems
PE-backed services group6 entities · 2 ledgers
Series B SaaS40k support tickets / month
Most companies respond to growth by buying more software. The work doesn't shrink — it fragments. We do the opposite: fewer seams, deeper roots. One integration layer, every system speaking, every hour accounted for. The companies that win the next decade won't have more tools. They'll have fewer gaps.
What changes when systems take root
Scale without headcount
Volume doubles; the team doesn't. Throughput becomes a property of your infrastructure instead of a line in your hiring plan.
Audit-ready by default
Every automated decision is logged, replayable, and attributable. Diligence and compliance turn from projects into exports.
Case studies
Production systems,
measured outcomes
Three representative builds. Architectures simplified; numbers real.
1,200 inbound emails a day, handled in under 30 seconds each
The problem. A freight operator's nine-person ops team hand-sorted carrier updates, claims, and quote requests across four shared inboxes. First response averaged 4 hours. Misrouted claims cost real money.
The build. Webhook ingestion from Microsoft Graph into a durable queue. Claude classifies each message against 14 intent classes with structured outputs, extracting shipment IDs and claim references. A confidence gate routes ≥0.92 automatically — ticket created, ERP updated, reply drafted. Everything below the gate lands in a human review queue with the model's reasoning attached. Nightly evals against 2,000 labeled samples catch drift before customers do.
- Claude API
- n8n
- Postgres
- MS Graph
- Zendesk
- NetSuite
- 38hrs
- returned weekly
- 96.3%
- routing precision
- <30s
- p95 intake → routed
One inventory truth across eleven systems
The problem. Storefront, two marketplaces, a 3PL, and an ERP each held their own stock counts. Reconciliation was a nightly CSV ritual. Oversells were a weekly apology.
The build. Every stock movement — sale, return, receiving, adjustment — normalized into a single event stream. A canonical ledger in Postgres holds the truth; idempotent consumers fan updates back out to every channel. Failed deliveries hit a dead-letter queue with one-click replay. Drift monitoring alerts Slack the moment any channel disagrees with the ledger by more than 0.5%.
- Shopify
- Amazon SP-API
- NetSuite
- Redis Streams
- Postgres
- Grafana
- 99.9%
- ledger accuracy
- 4s
- p95 propagation
- −83%
- oversells, first quarter
Quote-to-cash without human hands
The problem. A six-entity services group closed deals in HubSpot, then hand-keyed invoices into two accounting systems. Twelve days of lag, revenue leakage nobody could quantify, and a diligence process that dreaded the question "walk us through billing."
The build. Deal-won events trigger contract validation against rate cards, entity-specific invoice generation, and an approval chain in Slack with full context. Approved invoices post to the right ledger automatically; payment webhooks reconcile against expected cash. Durable workflow execution means a failure mid-chain resumes — never duplicates. Every step lands in an immutable audit log built for the next diligence cycle.
- HubSpot
- Temporal
- Xero
- NetSuite
- Slack
- Postgres
- $48k
- annual leakage recovered
- 0
- manually keyed invoices
- −12
- days sales outstanding
Selected builds
- SupportSupport copilot — RAG over 12k documents, confidence-gated escalation62% auto-resolution · CSAT 4.8
- Revenue OpsExecutive reporting pipeline — nine sources, modeled nightly6h weekly ritual → 4min run
- People OpsEmployee onboarding orchestration — HRIS → IT → payroll → access14 systems, zero-touch day one
- Field OpsField-ops dispatch automation — geo-matching, SLA-aware routing−31% time-to-site
Capabilities
Built like infrastructure,
because it is
Systems Integration
Event-driven pipelines between CRM, ERP, billing, and warehouse. Idempotent consumers, replayable queues, exactly-once effects. The boring guarantees that make automation trustworthy.
AI Agents in Production
LLM systems with structured outputs, eval suites, confidence gating, and human-in-the-loop review. Not demos — agents your auditors and your customers can live with.
Data Infrastructure
Canonical models, change-data capture, and pipelines that make every downstream automation right the first time. One truth, propagated everywhere.
Reliability & Observability
Monitoring, alerting, dead-letter queues, SLAs, runbooks. We operate what we ship — and hand over documentation your own engineers will actually use.
Engagement model
Fixed scope.
Weekly ships.
We take on four build engagements per quarter. Every one starts with an audit.
-
01
Integration Audit
2 weeks · fixed feeWe map your systems, trace the handoffs, and quantify the hours and dollars lost between them. You get an architecture blueprint and a ranked automation backlog with ROI estimates — useful whether or not you build with us.
-
02
Build
6–12 weeks · from $50kFixed scope, weekly demos, production from week one. Real environments, real data, staged rollouts. No big-bang go-live — systems take root gradually, and you see working software every Friday.
-
03
Operate
monthly retainerMonitoring, eval runs, incident response, and continuous improvement under SLA. Living systems are tended, not abandoned — and the next automation is always cheaper than the first.
Questions
Asked before
every engagement
How fast do we see something working?
The first automation reaches production in the first two weeks of a build — not a demo, a workflow running on your real systems with monitoring attached. Every Friday after that, something new ships.
What do you need from our team?
One process owner for about two hours a week, sandbox access to the systems in scope, and a Slack channel. We handle the rest — including the documentation your IT team will ask for.
Who owns the system afterward?
You do. Code, infrastructure, credentials, runbooks, eval suites — everything is yours and documented. The Operate retainer is optional; nothing about the build locks you into us.
How do you handle data security?
Least-privilege service accounts, your cloud or an isolated tenant, secrets in a vault, and no model training on your data — ever. We work inside your security review, not around it.
We already have engineers. Why you?
Even better. Your team keeps building product; we lay the integration rails they don't have time for — and hand over systems they'll actually want to maintain. Most of our best referrals come from in-house CTOs.
What does $50k actually buy?
Typically two to three production workflows, including monitoring, evals, documentation, and handover. The audit that precedes every build defines the exact scope — and the ROI math — before you commit to anything.
Start with the audit
hello@
intuitiv.site
Tell us where the hours go. We'll reply within one business day with an honest read on whether the ROI is there — and walk away if it isn't.