SolutionsSystem Integration

Data systems that connect and simplify your business.

We connect the tools you already use, with the right middleware, governance, and monitoring to keep every flow trusted.

Why integration projects fail

Why these projects fail, and why ours don't.

The killers are small facts about how you operate that never survive a weekly status call. We sit inside the business, so they never have to travel.

The typical project
Your business
●●●●●
the 3PL edge case
what "fulfilled" means here
customers resubmit surveys
1 status call a week
The agency
A PM who moves tickets · an engineer who builds to spec
Green calls all the way to a broken go-live.
The Meridian project
Your business
●●●●●
Meridian, in the buildingan ops-experienced lead + a senior engineer
the 3PL edge casewhat "fulfilled" means herecustomers resubmit surveys
No gap. Caught in week two, built before cutover.
The status reports were green. The go-live wasn't.
Their timeline
week 4on track
week 10on track
week 16on track
go-liveorders stuck, emergency calls
Our timeline
!
week 2"what about resubmitted surveys?"
!
week 9"these statuses don't map 1:1"
built & testedalerts, not surprises
go-liveboring, as planned
The stack

Your systems, and the layer that moves data between them.

We are not selling middleware. We pick the lightest option that fits what you already run, and the mappings and logic live in your repo either way.

The established platforms
Boomi, Celigo, Workato, MuleSoft, n8n: we have run them all, and when one fits your stack and your team, it is the right call.
Custom, AI-built integration
When a platform license is overkill, a senior engineer working with AI tooling builds the flow directly, often at a fraction of the license cost, and you own the code outright.
Where custom agents go further
Middleware & orchestration
Boomi
Celigo
Workato
MuleSoft
n8n
Power Automate
dbt
Boomi
Celigo
Workato
MuleSoft
n8n
Power Automate
dbt
Middleware & orchestration
Netsuite
Salesforce
Shopify
Klayvio
Magento
Oracle
SAP
ShipStation
ShipBob
Blue Yonder
BigCommerce
Netsuite
Salesforce
Shopify
Klayvio
Magento
Oracle
SAP
ShipStation
ShipBob
Blue Yonder
BigCommerce
What week two looks like

The spec says the fields match. The data says otherwise.

Before anything goes live, every status and field is mapped against real records from both systems. The flagged rows are the go-live incidents that now never happen.

Status mapping · 3PL → NetSuite → customerChecked against 90 days of real orders
3PL statusNetSuite statusCustomer seesFinding
SHIPPEDFulfilledOn its way, with a dateClean, 1:1
DELIVEREDClosedDeliveredClean, 1:1
PARTIAL_SHIP(no equivalent)"On its way" for items still in the warehouseNeeds split-order logic before go-live
RTS_CARRIERFulfilledDelivered, for an order coming backReturn path unmapped: refund and restock stall
EXCEPTION_HOLDPendingSilenceThe cancellation window: route to exception queue with a reason
Three of five statuses needed logic no spec had asked for. Found in week two against real orders, built before cutover, and the go-live stayed boring.

Illustrative excerpt; the real mapping covers every field that crosses systems.

How this played out

One client, four integrations, growing trust.

01
Quick automations
Orders and surveys flowing into the ops sheet, automatically.
02
The rescue
Took over a partner's broken Salesforce deployment. Fixed it clean, at a fraction of the cost.
03
The replatform
Shopify to Magento, every field mapped first. No issues.
04
The backbone
3PLs and carriers reconciled line by line, so fulfillment status finally matched reality.

Fewer stuck orders, and fewer cancellations between purchase and installation. "The first system they trusted."

Read the full case study
From the work

From Google Sheets to thousands of orders a day.

The backbone integration, end to end: Shopify, NetSuite, the 3PL and Salesforce reconciled into one order flow, and the fulfillment team's day became the exceptions.

~35%
fewer cancellations between purchase and delivery
3x
order volume through the same size team
4→1
four systems reconciled into one order flow
Then ask it anything

Governed AI chat on everything you just connected.

Your definitions and reconciliation rules are encoded into the layer, so questions that used to be tickets get answered correctly, in seconds, by anyone on the team.

COO

Did every order that shipped yesterday get invoiced?

Governed layer · live

2,214 of 2,219 shipped orders are invoiced and matched. The 5 open ones are in the exception queue with reasons: 3 partial shipments awaiting the second carton, 2 refunds crossing the invoice run. Nothing needs you today.

Using: order flow reconciliation · nightly batch plus live deltas · exception reasons from the queue

Illustrative exchange.

How the governed AI layer works

Get in touch.

Tell us what you are trying to figure out. If we can help, we will say how. If we are not the right fit, we will say that too, and point you somewhere better.

01
Free work first
We start by doing a real piece of work on your data, free, so the first thing you judge is output, not a pitch.
02
First build, fixed scope
A trusted core metric layer in weeks, not quarters, at a fixed price. Small enough to be safe, real enough to matter.
03
Scale when it earns it
Month to month from there, from a few thousand a month. You own every model, dashboard and definition.