Service 04

Stand up an AI Factory that ships on schedule.

We design the intake gate, sizing model, standing teams and stage gates that turn your backlog into accepted AI products, then keep delivery honest with independent oversight.

Book a consultation Take the Readiness Check

What you walk away with

A working delivery system, not a slide deck

Intake gate with a readiness checklist and one work order per product
Six-measure sizing model with S/M/L delivery bands and time boxes
Five-stage lifecycle with named sign-offs at every gate
Standing-team design: roles, shared specialist pool, capacity rules
Reusable asset library structure and contribution rules
Product register and portfolio dashboard specification
Definition of done: runbook, model card, lineage, monitoring
Partner scorecard and statement-of-work templates for your vendors

How it works

From backlog chaos to a steady delivery rhythm

We design the factory around how you already work, run the first products through it with you, and then tune it on real evidence.

Team members arranging sticky notes on a journey map during a working session

Baseline your delivery

Map how ideas become products today, where time is lost, and who decides what.

Design the factory

Intake gate, sizing model, stage gates, team structure and the asset library, fitted to your governance.

Pilot with a first wave

Run 3–5 real products through the gates with your teams or partners, and learn fast.

Tune and scale

Adjust bands, gates and capacity rules based on what the first wave showed.

Independent oversight (optional)

We review sizing, gate decisions and the portfolio each month, so delivery stays honest.

The blueprint

Backlog in. Accepted products out.

Work only starts when it's ready. Standing teams build on shared assets, and nothing reaches production without passing its gates.

Intake gate

The delivery clock starts here, and only when the product is ready.

Sized on six objective measures
One work order per product
Data, owner and environments confirmed

Standing delivery teams

Stable, cross-functional teams that add capacity as demand grows.

Team 1

Team 2

Team n

Shared specialist poolSolution architects · MLOps · AI governance · integration · business analysis

Accepted products

Live in production, with everything needed to run and audit them.

Runbook and model card
Data lineage and drift alerts
Reviews at 30 and 90 days

Reusable asset library, owned by you Data connectorsFeature catalogModel & agent templatesTest harnessesCI/CD pipelines

Delivery bands

Every product gets a size, and the size sets the clock

Six objective measures (systems integrated, data readiness, model class, inference mode, agent autonomy and approval span) put each product in a band with a committed time box.

Small

One system, curated data, a standard model and a single business owner.

Commit: up to 8 weeks

Medium

A few systems, new pipelines, near-real-time or multi-feature models.

Commit: up to 16 weeks

Large

Many systems, real-time latency, agents acting within limits, several approvers.

Commit: up to 24 weeks to first release

Want to try the sizing model on one of your own ideas? The AI Factory page has the full framework and an interactive sizing calculator.

Illustrative example

A mid-size insurer with too many pilots and no pipeline

Situation: A dozen AI pilots across claims, underwriting and service, spread over four vendors. Nobody could say which were in production, what they cost or who approved them.
Approach: We baselined delivery, designed an intake gate and sizing model, and set up two standing teams with a shared specialist pool. The first wave of four products ran through the new gates while we tuned the bands.
What you get: One register of every AI product, a predictable path from idea to production, vendor statements of work tied to bands and gates, and a monthly oversight review led by people who don't do the build.

FAQ

Common questions

Do you build the AI products?

No. We design the factory and oversee it. Your internal teams or partners you choose do the build. That separation keeps our sizing and gate reviews independent.

Does this work if we already have delivery partners?

Yes, and that's often where it helps most. We fold existing partners into one intake gate, one set of bands and one register, and give you templates to hold them to it.

What does the oversight retainer include?

A monthly portfolio review, independent checks on sizing and gate decisions, a quarterly look at the band mix, and on-call advice when a product gets stuck.

How is this different from setting up an AI Center of Excellence?

A CoE defines who does what. The AI Factory defines how work flows from idea to production. Most clients need both; see Operating Model & AI CoE.