Technology with Intention

Train. Advise. Build.

We help businesses adopt artificial intelligence by delivering quality training, providing clear advice, and building custom technology.

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Our Services / Training

Quality training

Fundamental content

Learn key concepts, opportunities, and risks

What these models actually do, the four parts of every tool, and the difference between prediction and fact. Plus the prompt formula that reliably lifts output quality: role, context, task, constraints, format.

The tool landscape

Know which tool to reach for

The major assistants have genuinely different strengths, and the right one depends on the task in front of you. We cover how to match tool to task, what your existing licences already cover, and where your data goes in each case.

Custom use cases

Apply takeaways immediately

Sessions are built on your own documents and the tasks that fill your week: written communications, review and analysis, and meeting prep and debrief.

Who it is for

Teams that need working fluency rather than a lecture. Investment teams, operators, boards, and the organizations they fund. We have run this for people who had never opened one of these tools and for people already using them daily, usually in the same room.

Part one, fundamentals

What these models are and are not. A model predicts the most likely response from patterns it learned; it is not looking up a verified fact, which is why your instructions matter and why numbers always get checked. We cover the four parts of every tool, the vocabulary your team will hear all year, and the prompt formula worth memorizing: role, context, task, constraints, format. Then we take a weak prompt and a strong prompt for the same request and show why the output differs.

Part two, the tool landscape

The real differences between the leading assistants, and how to match the tool to the task rather than buying all of them. What your existing licences already include, what needs an add-on, and who in your organization has to switch it on. Where your data actually sits in each case: which tools keep content inside your own tenant, which respect your existing file permissions, and which exclude your conversations from training by default.

Part three, use cases

The three places teams reliably get their first wins. Written communications: board and funder reports, memos, and outreach, drafted from your own templates and style guide so they come back in your voice. Review and analysis: documents, data, and financial models made decision-ready. Meeting prep and debrief: transcripts turned into decisions and action items.

How it runs

Live sessions, single team or full cohort, virtual or in person, from one foundations session to a multi-level program over months. We survey participants beforehand so the material is built on their real work, and sessions include working discussions rather than only presentation.

What you walk away with

Your people using these tools on real work the same week. Session recordings, a prompt library built for your workflows, a written takeaway package for whoever missed it, and a first thirty days laid out week by week so momentum does not depend on us being in the room.

Our Services / Advise

Clear advice

Strategy

Update your strategic plan

A plan built around your workflows, your data, and your people, with a sequence rather than a wish list. We are as specific about what to leave alone as about where to start, because the fastest way to lose a team is to automate the wrong thing first.

Key Decisions

Confidence on the choices that matter

Which tool, on which plan, with which guardrails, and who has to approve it. We often find teams about to buy capability their current licence already covers but nobody enabled.

Who it is for

Leaders who have a decision to make and do not want a vendor’s answer to it.

Strategy

We start from the work, not the tool. Which recurring tasks consume real hours, which of those depend on judgment that should stay with a person, and which are simply steps that could be handled. That distinction sets the sequence: the first thing you automate should be the thing that is safe to get wrong, so the team builds confidence before it builds dependence. You get a plan with a starting point, an order, owners, and the parts of the operation we recommend leaving exactly as they are.

Key decisions

Tool selection, plan and licence tier, and the data questions that decide whether any of it is usable. Does content stay inside your own tenant. Does the tool respect the file permissions you already have, so it cannot surface a document someone was never allowed to open. Are your conversations excluded from model training. Who in your organization actually has to switch a feature on. These are answerable questions, and they are usually the difference between a rollout that clears legal and one that stalls.

The question behind the question

Most teams asking what to buy are closer than they think. Capability is frequently already inside a licence they pay for and nobody has enabled, or switched off by an administrator rather than absent. We check that before recommending spend, which occasionally makes the honest answer to a procurement question no.

How it runs

A scoped diagnostic, a standing monthly retainer, or a seat at the table for one specific decision. We are vendor independent and take no commissions, so the recommendation you get is the one we would act on ourselves. If the right answer is a tool we did not build and cannot bill for, that is the answer you get.

Our Services / Build

Custom technology

Custom Solutions

When 'off-the-shelf' isn't enough

When existing products aren't enough for what you need, our team of engineers build custom, secure technology.

Product Management

From concept to launch

From concept to launch, we help teams prototype new capabilities and build the business case for bespoke applications.

Who it is for

Organizations whose need is specific enough that no product on the market fits it.

What we build

Decision-support engines, assessment and diligence platforms, internal tools that run a workflow end to end, and prototypes that exist to prove or kill an idea before it gets expensive.

How it runs

Prototype first, then the business case, then production. Our team architects the build and stays on the engagement; delivery scales through engineers across the United States, Germany, and Africa.

Our Guiding Vision

REACH for the stars

The models are powerful. The value is in translating them into results your team can actually use. We take a people-centric approach so that your team can accomplish its ultimate mission.

Our Approach

Three Steps to EMPOWERING your team

Every engagement runs the same way, whether it ends in a workshop, a written recommendation, or working software. We start with your people, build around what we actually find, and hand over something your team owns.

What happens

We go through real documents, real workflows, and the tasks that quietly consume the week. We ask what has already been tried, what failed, and what your team is not permitted to change.

What we are listening for

Where judgment is genuinely being applied, and where time is simply disappearing. The first is where AI assists a person. The second is where AI replaces a step. Those call for very different answers, and conflating them is why most AI pilots stall.

What you get

A written read of where AI helps, where it does not, and what each option would take to stand up. We are direct about the places the answer is no, because a tool nobody adopts costs you more than doing nothing.

What happens

Training is built on your files. Advisory addresses your actual constraints, including the ones that are political rather than technical. Builds are scoped to the workflow we watched, not the one described in a brief.

Why it matters

Generic AI training reliably produces enthusiasm and no change in behaviour. People adopt a tool when it solves the problem sitting in front of them that morning, which means the work has to be recognizably theirs.

What you get

A plan with sequence, owners, and a definition of done, priced and agreed before delivery starts. If the honest answer is a smaller engagement than you asked for, that is what we will propose.

What happens

Delivery in whatever form the work takes. Our team stays on the engagement rather than handing off to a delivery team you have never met.

How we know it worked

Your people using it on real work with nobody from Arthur Labs in the room. That is the only test we treat as meaningful.

What you get

Training leaves recordings, a prompt library built for your workflows, and a written takeaway package. Advisory leaves a recommendation you can hand to a board. Builds leave documentation, a handover, and a running system, with support while your team takes it over.

Engagements run from a single session to a multi-month build. The three steps do not change.

Our Work / Selected Engagements

What we have built and delivered

We have delivered AI training to more than 60 organizations.

The engagements below are a selection, chosen to show the range of the work rather than its volume. Client names are held in confidence, so each one describes what the organization needed, what we built or taught, and where it stands today. We are glad to make introductions to references on request.

The situation

A mission investment team overseeing a large portfolio needed to move from AI curiosity to working practice. It also needed a defensible, repeatable way to make a recurring, judgment-heavy investment decision that had always depended on institutional memory.

What we did

We designed and delivered workshop series for the internal team and for the organizations it funds, built around their real documents and real workflows rather than generic examples. Separately, we built a decision-support engine that structures the analysis behind a specialized investment vehicle, including a precedent layer drawn from the institution’s own decision history.

Where it stands

Workshop series running through 2026. The decision engine is in use with the internal working group and moving to a sector pilot.

The situation

A research and investment team wanted working AI fluency across the organization, then wanted its proprietary employment-quality framework turned into something the companies it works with could actually use.

What we did

We delivered a multi-level training program, from foundations through applied use cases built on the team’s own material. We then scoped and began building a diligence platform that scores employers against the framework using public and submitted data.

Where it stands

Training delivered. Platform in build.

The situation

A lean team wanted AI working in its daily operations without adding headcount and without taking on risk it could not see.

What we did

Firm-wide foundations training, then a hands-on implementation. We mapped the recurring workflows, built the tooling to run them, and handed over something the team now operates without us.

Where it stands

Implementation complete.

The situation

Decades of research on management quality sat inside assessments and books rather than inside a product anyone could use.

What we did

We built a working demo that takes a manager assessment and returns a synthesized development report, so the firm could see and test the product before committing to build it.

Where it stands

Demo delivered. Product path in discussion.

The situation

Senior operators who want to use these tools themselves rather than delegate them to someone junior, and who do not have time for a course.

What we did

Personal setup, a prompt library built around how each person actually spends a week, and a short series of working sessions on their live problems.

Where it stands

Ongoing.

Our Clients

Organizations that are essential to the progress of people

We've delivered AI training and built custom tools for foundations, financial institutions, and mission-driven investors.

Current and recent engagements span philanthropic foundations, private-markets investors, an employee-ownership fund, a leadership advisory firm, and individual senior executives. Active conversations include a national small-business program at a global bank, a global alternative asset manager, a multi-billion-dollar credit manager, a real estate brokerage, and several nonprofit intermediaries.

Small Businesses

Industry agnostic

Financial Institutions

CDFIs, Insurance Companies, and Financial Intermediaries

Investors

Funds, Investment Companies, Holding Companies, and Family Offices

Philanthropy

Endowments and Foundations

Institutions & Programs

Universities, accelerators, and the cohort programs that support them

Our Team

Our team has built and advised at NASA, McKinsey & Company, Barclays Capital, Ernst & Young, Two Sigma Impact, and Schmidt Futures, with research and graduate training at Princeton, Oxford, Stanford, and Columbia. We have spent our careers building machine learning and AI systems, and translating technical work into decisions institutions can act on.

Jared is a Senior Advisor at the Ford Foundation and a Founder at Open Athena, an AI-science organization. He has previously held roles at Schmidt Futures as Manager of Science Investments, at Two Sigma Impact as Vice President, at McKinsey & Company, and in the Office of the Chief Technologist at NASA.

Education

  • M.P.A., Princeton School of International and Public Affairs
  • M.Eng., Mechanical and Aerospace Engineering, Princeton University
  • B.A., Astrophysical Sciences, Princeton University

Current

  • Founder, Arthur Labs
  • Senior Advisor, Ford Foundation
  • Founder, Open Athena

Previously

  • Schmidt Futures
  • Two Sigma Impact
  • McKinsey & Company
  • NASA Office of the Chief Technologist

Lanier served in the Financial Services Office at Ernst & Young, LLP, where he led international teams and built automation tools for risk analysis, financial statements, and financial model review. At Arthur Labs he directs operations and delivery across every engagement, from the first workshop to the final build.

Education

  • M.B.A., Columbia Business School
  • B.S., Accounting, Molloy University
  • Certified Public Accountant

Current

  • Operations, Arthur Labs
  • Board of Trustees, Molloy University

Previously

  • Ernst & Young, LLP, Financial Services Office

Jacques has held research positions at Stanford University, the University of Oxford, and the Institute of Cosmology and Gravitation, worked as a quantitative analyst at Barclays Capital, and co-founded Adludio, an adtech AI company he scaled to more than fifty employees across four continents. Jacques is currently Founder and Chief Technology Officer of Arclio, which builds auditable AI agents for regulated work including clinical trial analysis, FDA submission readiness, and regulatory reporting, on Proof of Insight, an open standard for source-linked, replayable, and defensible AI output that he authors and maintains.

Education

  • Ph.D., Mathematics & Applied Mathematics
  • M.Sc., Theoretical Physics

Current

  • Founder and Chief Technology Officer, Arclio
  • Board Member, 10 Academy

Previously

  • Adludio
  • Barclays Capital
  • University of Oxford
  • Stanford University
  • Institute of Cosmology and Gravitation

Engineering capacity

North AmericaEuropeAfrica

Three continents, scaled to the project rather than carried as overhead. A build does not wait on hiring, and the work does not stop when one time zone goes home.

About Arthur Labs

The translation layer

The models are no longer the hard part. The hard part is the distance between a capable model and an organization that has actually changed how it works. Arthur Labs exists to close that distance.

How we work

Your AI General Counsel

We recommend the right tool for you and give guidance on what to adopt, what to ignore, what to build yourself, and what each of those will actually cost you to run.

Our advice is the product.

How we are built

Senior attention, no pyramid

Our team leads every engagement directly and stays on it. Delivery runs through a team of engineers across the United States, Germany, and Africa, scaled to what a project actually needs.

You are not paying for layers of people between you and the person doing the thinking.

What we believe

People first, then technology

Every engagement starts with the people who will use the thing. We begin by meeting your team to surface the work that actually consumes their week, because a tool nobody adopts is a cost, not an asset.

Technology with intention. It is our tagline because it is the constraint we hold ourselves to.

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