Forward-Deployed AI Engineering

Bring us the problem. We bring the engineers and the head start.

Most of the expensive problems in an enterprise are not hard questions. They are easy questions that no single system can answer. The renewal that should have repriced. The account nobody realized was two introductions away. The stockout that demand data predicted and procurement never saw. The answer exists — it is just distributed across eleven systems that were never designed to be asked at once.

Object Edge solves those problems by deploying senior engineers into your business, not by selling you a platform and wishing you luck. The engineers are the delivery model. Hive, our company brain, is the reason they arrive with most of the work already done.

How the engagement runs

Diagnostic — two to three weeks, fixed fee. We connect read-only to the systems that hold the answer, quantify what the problem is actually costing, and come back with a number and a path. If we cannot see a path, we say so and you owe nothing further. This is not a discovery phase that bills by the hour.

Delivery pod — eight to twelve weeks, fixed bid. A senior architect and two or three forward-deployed engineers put one workflow into production inside your own cloud. Not a pilot environment, not a proof of concept that needs a second budget cycle to become real.

Managed intelligence — ongoing. The company brain we built the workflow on stays. It gets maintained, extended to the next problem, and monitored as your source systems change. Most clients find the second problem costs a fraction of the first, because the hard part — the connected layer underneath — is already standing.

Why this is eight weeks and not two quarters

A consultancy solving this problem builds the connective layer from scratch, on your budget, every time. That is where the quarters go. We do not, because that layer already exists as a product.

Hive resolves entities across your systems, types the relationships between them, keeps the history queryable, and puts provenance on every claim. Our engineers start from a working company brain and spend their time on your problem instead of on plumbing you have already paid three firms to build. Same engineers you would hire anyway. Substantially less of the work left to do when they arrive.

The commercial consequence matters more than the technical one: it is why we can quote a fixed bid at all.

The problems that fit

The pattern is consistent. The problem spans more than one system, has a real number attached, and has an executive who owns that number. Revenue leakage, lead generation, sales force management, and inventory optimization are the four we have productized for enterprise — but they are examples of the pattern, not the boundary of it.

Warranty claims that pay out against entitlements nobody reconciled. Quote-to-cash cycles where three systems disagree about the price. Entitlement drift eighteen months after an acquisition. Field dispatch that optimizes against yesterday’s inventory. If you can name the problem and roughly what it costs, it fits.

What this is not

This is not staff augmentation, and it is not an AI-transformation program. We do not sell engineering capacity by the head, and we do not sell a multi-year mandate to become an AI-first organization. We take one problem that costs money, put a working system against it inside a quarter, and earn the next one.

Where this connects

Forward-deployed work usually reveals a foundation problem underneath it — which is why this practice sits alongside enterprise data governance, master data management, and data strategy and consulting. We fix those in service of the problem you brought us, not as a prerequisite you have to fund first.

If the problem sits inside commerce, quoting, or subscriptions, commerce implementation and CPQ enablement join the same program.