Your systems don't talk to each other. So your team does it by hand.
Orders re-keyed into the ERP. Purchase orders chased across inboxes. Customers asking where their backordered item is. An embedded engineer, a real person aligned with your business, connects your store, your ERP, and your suppliers — then stays to run it and prove what it produced every month, at a fraction of the cost of a full-time engineer.
Live today for distributors and product businesses running Shopify.
Common questions
The problems we get asked about most
How do you connect Shopify to an ERP without custom development?
You connect Shopify to an ERP without custom development by using an integration layer that already speaks to both systems. Clarissi connects order, inventory, and purchase-order data between Shopify and ERP or OMS systems, then keeps the connection working as either side changes. The work customers usually describe as "custom development" is the part nobody maintains afterward — an embedded engineer stays accountable for it instead of handing over a script.
What moves between the two systems, and the four ways to connect them →
How do you tell customers when a backordered item will ship?
You tell a customer when a backordered item will ship by reading the expected date from the system that actually holds it — usually an ERP purchase order or a supplier feed — and sending it before the customer asks. Shopify knows an item is out of stock; it does not know when more is arriving. Clarissi reads that date from the connected system and sends the update automatically. For one distributor, Yota Xpedition, this addressed 1,493 orders representing $510,323.61 in order value between May and August 2026.
What does it cost to automate supplier purchase orders?
Automating supplier purchase orders costs a fraction of a full-time engineering hire. Clarissi charges a fixed fee for an Assessment that identifies which parts of your order and supplier flow can be automated, then a monthly retainer tied to the outcomes delivered. There is no per-seat licence and no separate integration project — the embedded engineer who builds it is the one who runs it.
What is an embedded engineer?
An embedded engineer is an engineer who works as part of your team, not as an outside vendor. They learn how your operations actually run, build custom solutions on the systems you already use — Shopify, your ERP, your suppliers, your support desk, your marketing and accounting tools — and stay accountable for keeping everything working as your business changes.
Distributors and product businesses use embedded engineers to get the custom solutions they used to patch together from apps, spreadsheets, and manual re-keying — because a full-time engineer was never in the budget. With Clarissi, your embedded engineer builds it, runs it, and proves the results every month. If something isn't working, that's our problem to fix — not yours to live with.
How it works
Three phases. One accountable team.
Assessment
A paid diagnostic — not a sales call. Over 1–2 weeks we analyze your systems and deliver a four-section report: automation exposure, skills match, gap analysis, and recommended path forward.
Learn about the Assessment →Custom Build
We build custom AI fit to your business — your rules, your thresholds, your edge cases. Automations, live dashboards, and analysis on the systems you already run. 2–6 weeks to first outcome.
See how we build →Operate
Your embedded engineer runs the platform, tunes the model, ships new outcomes, and delivers monthly outcome reports. You get verifiable results without the operational overhead of an in-house ops team.
See what your embedded engineer does →Design partner program
A limited number of spots available
We're working with select design partners — teams who get three months of Concierge engagement at no charge in exchange for a case study and testimonial. If your operations are a strong fit, mention it when you book your Assessment.
Apply via Assessment →Customer stories
What this looks like in a real operation
One customer, three things running every day, and the numbers behind them.
Built on the systems you already run
Why now
Two questions decide whether you can let AI run your operations
Can you trust what it does? Can you afford what it costs? We built Clarissi to answer both — so AI can act on your systems, not just advise.
You see what it did — and control what it can do
Every action is recorded the moment it happens: what triggered it, what was evaluated, what ran, and the result. Nothing touches your business that you can't inspect or constrain — so "what did the AI do, and why" always has an answer.
A cost that stays flat as usage grows
AI bills have a way of multiplying quietly. Clarissi is built so doing more doesn't mean paying exponentially more — and every month's report shows what each outcome cost and what it was worth.
Industries
Built for your industry, not a generic platform
Every operation runs the same four systems — a system of record, a way to talk to customers, a process to run, and a way to get paid. Clarissi connects them. What changes between industries is which systems those are, and we know the edge cases in ours.
Your embedded engineer
Embedded engineering — without the in-house engineering team
Running AI in production isn't shipping a model. It's modeling your operations, building custom workflows on top, tuning thresholds, handling edge cases, and monthly proof it worked. Most teams don't have an embedded engineer to own that. We do — at a fraction of an in-house hire.
Run the Assessment
Run a structured analysis of the client's environment over 1–2 weeks and deliver a four-section report covering automation exposure, what's ready to activate, custom candidates, and recommended path forward.
Build custom AI on your semantic model
Build custom AI automation against your business rules using generative AI tools (Claude, Codex). Your operations become a semantic model the AI executes deterministically against — no hallucinations, full audit trail.
Monitor and tune
Review execution logs, adjust conditions as the client's operations evolve, and activate new skills from the catalog as they ship.
Steward your AI spend
Keep token and AI costs flat as usage grows — choosing what runs deterministically vs. what needs a model, pre-computing heavy data instead of re-reading it, and reporting what each outcome cost and what it was worth.
Deliver monthly outcome reports
Measure verified results against the agreed baseline and deliver a clear breakdown of outcomes produced each month.
Manage the engagement
Handle credential setup, integration changes, and tier transitions — so clients never need to maintain an internal automation team.
A fraction of an in-house engineer — with measurable outcomes attached
Without hiring, training, or maintaining an internal AI engineering team. Outcome-aligned: your embedded engineer earns more only when your business produces verifiable results above the baseline we set at the Assessment.
A full-time operations engineer costs $172,000–$185,000 a year fully loaded. Clarissi Growth is $60,000. Want a number for your stack? That's what the Assessment is for.