AI advisory & implementation

Know where AI belongs in your business. Build only what earns it.

NLN Group advises business and technology leaders on where AI and automation genuinely belong in their operations — then helps build what’s justified and hands it over, so your team owns the result.

Advise. Build. Venture.

Start with an assessment See the six service lines

James Miller — Founder. 20+ years building and leading engineering for platforms at Amazon, Meta, and General Motors. Past employment, not NLN clients or endorsers.

01 / The offer

AI Adoption & Applicability Assessment.

One question, answered with the reasoning shown: where does AI or automation genuinely belong in this part of your operation — and where does it not. A standalone paid diagnostic, not the first step of a larger programme.

For each candidate area you get the current workflow and cost baseline, an applicability verdict with the reasoning behind it, a method recommendation — deterministic or self-learning, chosen deliberately — the data and access it would need, and a rough effort and value estimate with its assumptions stated.

The engagement can end in “don’t automate this”, “not yet”, or “a cheaper non-AI fix solves it”. A don’t-proceed recommendation is a successful outcome, and you are told that before you buy.

Start with an assessment Read how engagements work

Primary offer · Standalone purchase

What the assessment establishes

Scope and fee are confirmed in writing before work begins. No published rate card.

Current workflow, effort, quality, delays, and cost drivers — the baseline
Applicability verdict per candidate area, with the reasoning shown
Method recommendation: deterministic vs. self-learning, chosen deliberately
Data, system access, and integration requirements
Expected effort and value, with stated assumptions
A clear recommendation to proceed or do not proceed

Deliverable: A written assessment and a 60–90 minute readout with your decision-makers — written to be handed over, not to create dependency.
Who does the work: James Miller, personally. There is no handoff to a junior team.
What it is not: Not a sales document for a larger engagement. If the answer is no, that is what we will write.

Standalone

A complete product on its own — no obligation to continue with us afterward.

Fixed fee

Confirmed in a one-page scope before work begins. No rate card, no hourly billing.

Fast start

Begins within days of a signed scope — small enough to approve without a committee.

Honest verdict

The recommendation can be “don’t proceed” — and that is a successful outcome.

02 / What we do

Six service lines, one standard.

Judgment before build. Evidence before spend. Capability stays with the client. Every line below is an engagement we are qualified to deliver — scope, deliverables, and measures defined in writing before work begins.

Adoption / Applicability assessment

Fixed fee · standalone

What the client gets: A clear, evidence-based answer to where AI and automation genuinely belong in a defined part of your operation — and where they do not. Per candidate area: workflow and cost baseline, applicability verdict with reasoning, method recommendation, effort and value ranges with assumptions, risks, and the next step.
Who it’s for: A senior buyer — COO, VP Ops, CTO, or an operator reporting to the CFO — who must decide where to point AI spend and needs a defensible basis for the decision. Also the right first step for anyone who suspects they are solving the wrong problem.
Engagement shape: Standalone paid diagnostic: discovery sessions, review of your process documentation and data, written assessment, 60–90 minute readout. 2–4 weeks.
How value is measured: Every candidate area receives a verdict a CFO can challenge and we can defend. A “don’t proceed” is a successful outcome of this product.

Start with an assessment →

Roadmap and ROI planning

Fixed fee · optional review retainer

What the client gets: A sequenced AI and automation roadmap tied to economics: which initiatives, in what order, with what expected effect, at what cost, and what each must prove to earn the next phase — plus a measurement plan and a business-case model whose assumptions are explicit.
Who it’s for: The buyer who has to justify AI investment upward: CFO, COO, CEO, or a VP presenting to them. Also teams with a pile of AI ideas and no defensible order of attack.
Engagement shape: Workshop, analysis, and written roadmap. Often follows an assessment but stands alone. 2–6 weeks.
How value is measured: Your finance function can restate the business case without us in the room. Each roadmap item carries the assumption that, if false, kills it.

How engagements work →

Automation — deterministic and self-learning methods

Project fee · milestones

What the client gets: An automation design with the method decision on the record. Deterministic — rules, workflows, integrations: predictable, auditable, cheap to verify. Self-learning — models that generalize: powerful where it fits, with real evaluation and governance costs. The trade-off is stated: why this method, what it costs to run and verify, and what happens when it is wrong.
Who it’s for: Operations and engineering leaders with a specific process they know is automatable — and the ones who suspect it isn’t, or not the way a vendor described it.
Engagement shape: Design engagement: method decision record, solution design, evaluation and verification plan, build plan with milestones. 3–8 weeks for design; implementation sized separately.
How value is measured: Deterministic components carry testable acceptance criteria; self-learning components carry a stated evaluation method and an error bar you accept before build.

How engagements work →

Implementation

Project fee · milestones

What the client gets: Working software in your environment: integrations into existing systems, workflow automation, model or agent-backed components where justified, monitoring, and the tests, documentation, and runbooks your team needs to own it.
Who it’s for: Companies with an approved direction — from an NLN assessment or their own — that need it built by someone who has shipped platform-grade systems, without hiring a full team for it.
Engagement shape: Milestone-based build in your repository and systems. 6–16 weeks typical, scoped per project.
How value is measured: Acceptance criteria agreed before build — your definition of done, not ours. Your team can deploy, monitor, and modify without us.

How engagements work →

Responsible / Safe AI

Fixed-fee review · optional retainer

What the client gets: A practical review of how you use, or plan to use, AI: data exposure and confidentiality, decision accountability, human-review points, failure modes, and what must be documented for internal audit, customers, or regulators. Output is a findings memo, a guardrail set you can adopt as policy, and a remediation sequence.
Who it’s for: Companies under a compliance, customer, or board question they cannot yet answer well — what happens when the model is wrong, whether customer data is reaching tools it shouldn’t, who is accountable when an automated decision harms someone.
Engagement shape: Diagnostic review: discovery interviews, artifact review of current usage, policies, and data flows, findings memo, readout and guardrail workshop. 2–5 weeks depending on surface area.
How value is measured: Every material AI usage path in scope is reviewed and dispositioned; each guardrail is specific enough to be followed and audited. You leave with a written answer to the question that prompted the engagement.

How engagements work →

Employee training / AI adoption

Fixed fee per cohort

What the client gets: A structured programme that moves a defined group of employees from “we have licenses” to confident, safe daily use of the AI tools relevant to their roles: role-tailored curriculum, hands-on exercises on your real work, guardrails for what must never be pasted into a tool, and a short manager guide for sustaining adoption.
Who it’s for: Companies that bought AI tooling top-down and saw usage plateau, or that are rolling it out and want adoption to stick. Typically a VP of Engineering, Head of Ops, or a People/Enablement leader under pressure to show the spend was worth it.
Engagement shape: Fixed-scope cohort programme: kickoff, 3–5 working sessions, exercises between sessions, closing readout to sponsors. 3–6 weeks per cohort; materials handed over so you can run future cohorts internally.
How value is measured: Adoption metrics you already have, a pre/post self-efficacy assessment stated plainly as self-report, and managers reporting the guardrails are followed without policing.

How engagements work →

Each engagement defines its own scope, deliverables, and measures before work begins. NLN is a new practice — these are the engagements we are qualified to deliver, not a list of past clients.

03 / How engagements work

Advise, build, venture — in that order.

Three shapes, not a lifecycle. Every one is bounded, scoped in writing before it starts, and ends with your team able to run the result.

01 / Advise

Decide, with the reasoning shown

Assessment, roadmap, and responsible-AI review. Bounded engagements, fixed fee, written deliverables — a recommendation you can act on, including the case against, and the assumptions behind both.

02 / Build

Built in your systems, handed over

Design and implementation of what an assessment or roadmap justified. Milestones, working in your repository and environment, delivered with documentation, tests, and decision records.

03 / Venture

Optional, separately agreed

Where advisory work reveals an industry-wide opportunity, we explore building the company that solves it with the partners who should own it. Never assumed, never a condition of an engagement.

Capability transfer is part of every engagement. Every engagement ends with your team able to run the result — documentation, decision records, and a handover session. We build to be left behind.

Fixed fee, confirmed in a one-page scope before work begins. No published rate card, no hourly billing, and no outcome-based or success fees — they create incentives we don’t want in an advisory relationship.

Yes. We build inside your systems, your access controls, and your security requirements rather than requiring you to replace them.

Then the assessment says so. A don’t-proceed recommendation is a successful outcome of this product, and you are told that before you buy.

James Miller, personally. There is no handoff to a junior team. Implementation-heavy work may draw on vetted contractors later; if so, that is stated at proposal time and never implied.

No. We hand over; your team owns the result.

A short conversation, then a one-page scope with a fixed fee. Work usually begins within days of a signed scope.

04 / Method and proof

A new practice. Here is what we bring.

NLN Group is a new practice. We don’t publish client results because we don’t have any yet — here is what we do have.

There are no client logos, metrics, or testimonials on this page. Inventing them would destroy the only asset a new practice has: the presumption of honesty that makes a senior buyer willing to be the first client.

What we can stand behind is the method — the assessment’s decision criteria, the deterministic-versus-self-learning selection frame, the guardrail set, and the assumption structure of the ROI model — and twenty years of platform engineering leadership behind it.

A buyer can judge whether the method is sound without any client history. Sound method is itself the proof, and it is how the credible entrants to this category have always started.

How we keep our claims honest

Illustrative examples carry a visible label, every time
No client names, logos, results, metrics, or testimonials — none exist to publish
No published rate card; scope and fee are confirmed per engagement
Amazon, Meta, and General Motors are past employment, not NLN clients or endorsers
Every recommendation includes the case against, not only the case for

Illustrative example — not a client result

How we make the method call

The question: A finance team spends most of a week each month reconciling invoices across three systems by hand.
What we found: The reconciliation rules were stable and fully specified — but the data never arrived clean. A deterministic problem, not a self-learning one.
The method call: Build rules, validation, and integrations, not a model. A model here would add an evaluation burden and a statistical failure mode that nobody needs.
What the client kept: The systems, the rule set, the runbooks, and two people who can change them.
What it did not do: It did not remove the review step. Some judgments stayed with the finance team, as they should.

All figures and scenarios shown are illustrative and labeled as such. We do not publish fabricated operational results or client outcomes.

05 / Who you work with

The person who scopes your engagement is the person who does the work.

James Miller

Founder · NLN Group

James Miller has spent more than twenty years building and leading engineering for platforms at Amazon, Meta, and General Motors — platforms, AI, and distributed systems, where reliability, failure modes, and accountability were the job rather than the theory.

That is the lens NLN Group works from: scope defined honestly, methods chosen deliberately, a recommendation that can end in “don’t”, and systems built to be handed over.

AmazonEngineering leadership

MetaEngineering leadership

General MotorsEngineering leadership

20+ yearsPlatforms, AI, distributed systems

Past employment, not NLN clients or endorsers.

06 / The third leg

When the pattern repeats, we build the company.

Venture work

Advisory and implementation work shows where the same expensive problem appears across an industry. When it repeats often enough, NLN explores building the company that solves it — with the operating partners who should own it. Investment, responsibilities, and commercial interests are agreed separately, and participation is optional.

Venture work is never a condition of an engagement, and it never entitles us to reuse what we learned inside your business.

Providing access to a process does not authorize reuse of proprietary information, and not every engagement leads to a venture.

Start with an assessment

Tell us what you’re deciding.

A short reply from James, a scoping conversation, and a one-page scope with a fixed fee before any work begins. If the answer is that AI does not belong in your process, that is what you will hear.

Prefer email? hello@nln.group