Approach

Measure first. Build second. Hand it over properly.

Five phases. Each one ends with something you can review and a decision you can make, including the decision to stop. Nothing about this method requires you to take delivery on faith.

The method

Assess & baseline

We map the process the way it actually runs, which is rarely the way it's documented. That means sitting with the people doing the work and finding the workarounds, the informal exceptions, and the spreadsheet that holds the whole thing together.

Then we measure it. Volume, cycle time, touch time, error and rework rate, cost per transaction, seasonality. If a number can't be measured directly, we estimate it and label the estimate. That baseline is the reference point for every claim made later, and the single most common thing missing from failed automation projects.

Ends with

Current-state process documentation
Quantified baseline with stated assumptions
Feasibility and data-readiness assessment
Go / no-go recommendation

Design & business case

Target-state design before any code: what the system does, where the humans stay in the loop, what data moves and under what controls, what happens on every failure path, and who owns it in steady state.

The business case is built at the same time, with assumptions visible and arguable. If the honest payback period is four years, you find out here, before spending anything but assessment fees. A design that ignores failure modes, dependencies, and ownership isn't a design; it's a wish.

Ends with

Target-state architecture and process design
Integration, access, and data dependency map
Controls, review points, and risk tiering
Cost/benefit model and payback analysis
Delivery plan with milestones and acceptance criteria

Build & prove

Built in increments against real data and real exceptions, not a curated sample. You see working software early and often, so scope corrections happen while they're still cheap.

Proving means a controlled run beside the existing process, comparing output against how the work is done today. Accuracy, throughput, and exception rate get measured against the baseline from phase one. The go/no-go for full deployment rests on that evidence. Occasionally the evidence says don't deploy, and that's a successful outcome. You spent a build budget instead of a year of operating cost.

Ends with

Working system in a controlled production run
Measured accuracy, throughput, and exception rate
Comparison against the phase-one baseline
Evidence-based deployment decision

Deploy, operate & hand over

Phased rollout with a rollback path. Monitoring and alerting stood up before go-live, not after the first outage. Named owners for the process, the exception queue, and the technology.

Handover is a deliverable with an acceptance test, not a folder of screenshots. You receive source, configuration, prompts, architecture, business logic, dependencies, and runbooks. Enough for another competent engineer to take it over without calling anyone. That standard is deliberate: it means you're keeping AIGenic because the work is good, not because leaving is expensive.

Ends with

Production deployment with monitoring and alerting
Runbooks and exception-handling procedures
Owner and operator training
Complete technical package: source, config, prompts, architecture
Post-deployment measurement against baseline

Scale & govern

One automation is a project. Five is a program, and programs need intake, standards, and someone tracking whether the benefits are real.

This is where the enterprise Center of Excellence discipline earns its place, scaled to your size rather than copied from a company ten times larger. A lightweight intake process so good ideas surface and bad ones get filtered, build standards so the fifth automation looks like the first, and benefit reporting that survives contact with your CFO.

Ends with

Automation intake and prioritization process
Build, naming, and documentation standards
Governance model matched to your risk profile
Benefit tracking and executive reporting
Internal capability development where you want it

Non-negotiables

Five rules we don't bend.

These exist because breaking them is how automation programs fail. They're written here so you can hold us to them.

Measure before you build. No baseline, no project. Without it, every savings claim afterward is a guess wearing a suit.
Humans stay in the loop where a wrong answer is expensive. Review points are set by consequence, not by how confident the model sounds.
Everything is documented and handed over. Source, config, prompts, architecture, logic. You own it outright.
Fix the process before automating it. Automating a broken process produces broken output faster, at higher volume, with less oversight.
We'll tell you when the answer is no. If automation isn't the right call, or the payback doesn't justify it, you hear that, even when it costs us the work.

Practicalities

The questions people ask before they ask anything else.

How long does this take?

An assessment runs two to four weeks. A first automation typically reaches production in six to twelve weeks depending on integration complexity and how much access wrangling is involved. Anything quoted as "a couple of days" is either trivial or being oversold.

Do we need to move our data anywhere?

Usually no. Most builds run against your existing systems and stay inside your environment and accounts. Where a third-party model is involved, exactly what data reaches it is defined in writing, minimized, and agreed before anything is sent.

What if we already have an internal team?

Then the engagement is different: architecture, standards, governance, review, and hard problems, with your team doing the build. Handing capability over is a legitimate goal, and a stated one.

What does it cost?

Assessments are fixed-fee. Builds are milestone-billed against a written scope. Operations are a monthly retainer. You get the number before work starts, and scope changes get repriced in writing rather than absorbed and argued about later.

Start at phase one.

Bring the process that's costing you the most. Thirty minutes is usually enough to tell whether there's a real case here.

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