— Established 2024 · Chicago / New York
Meridian partners with leadership teams at small, mid-market, and global organizations to translate artificial intelligence into measurable, durable business outcomes — without the noise.
Index
Engagements span
— A note on our practice
We are not a vendor. We are an extension of your leadership team — assembled to think clearly, decide quickly, and ship the artificial intelligence systems that compound into long-term advantage.
∞
Senior-led
engagements
7
Sectors served
across two coasts
100%
Confidential
by default
4w.
Typical
diagnostic cycle
01 — Practice
Four interlocking capabilities
/ 01
Where AI creates durable advantage for your specific business — and where it does not. Investment thesis, opportunity mapping, and a sequenced 18-month roadmap your board can defend.
/ 02
Custom model engineering, agentic systems, and production deployment. Built by Columbia-trained AI engineers who write the code themselves rather than handing it off.
/ 03
Modern data architecture, evaluation pipelines, governance, and security postures designed for organizations where the cost of getting it wrong is measured in regulators, not retries.
/ 04
Operating model redesign, AI fluency programs for leadership, and the change architecture that determines whether your investment compounds — or quietly evaporates twelve months in.
02 — Approach
Most AI projects fail upstream of any code being written. Our four-phase method is designed to surface those failure modes early — and engineer them out before they reach production.
Phase 01
A focused four-week audit of where AI realistically creates value in your business and where it doesn't. We end with a written investment thesis — not a deck.
Phase 02
Technical and operating-model architecture. Models, data, evaluation, governance, and the team structure required to sustain it without permanent dependence on us.
Phase 03
We build alongside your team — not over them. First production system live within twelve weeks, instrumented end-to-end so its impact is measurable from day one.
Phase 04
Quarterly reviews, model performance monitoring, and an executive cadence designed to keep AI a strategic asset rather than a maintenance liability. We exit when you no longer need us.
03 — Team
Our practice brings together Columbia-trained artificial intelligence engineers, founders who have built and exited companies, and operating executives with decades of experience inside global enterprises.
We are deliberately small and senior. Every engagement is led — not merely staffed — by a partner with skin in the outcome. The work itself is performed by the same people you sit across from on the first day.
Columbia
Engineering & AI
research lineage
Founders
Built, scaled, and
exited operating companies
Operators
Senior leaders from
enterprise environments
Engineers
Production AI systems,
not slide decks
04 — Sectors
Engagements scale from venture-backed companies in their first hire of an AI lead to global organizations rebuilding entire functions on top of language models. The methodology adapts. The standards do not.
i.
Underwriting automation, document intelligence, advisory tooling, and risk & compliance applications.
ii.
Clinical workflow assistants, scientific literature synthesis, payer operations, and regulatory-grade evaluation.
iii.
Predictive maintenance, supply-chain reasoning, and shop-floor knowledge systems built for non-technical operators.
iv.
Personalization, merchandising intelligence, and AI-native commerce experiences that respect brand integrity.
v.
Knowledge work automation for legal, accounting, and advisory firms — designed around fee-earner economics.
vi.
Portfolio-wide AI diligence, value-creation programs, and board-level diagnostics across operating companies.
05 — Insights
Essay · 14 min
A pattern recognition piece on the structural reasons that a working pilot fails to become a working product — and the operating-model interventions that prevent it.
Read the essay →Briefing · 6 min
Twelve questions that separate a defensible AI strategy from an expensive enthusiasm. Designed for directors who do not want to be talked past.
Read the briefing →Field note · 9 min
A practical account of building evaluation pipelines for high-stakes domains — and why public benchmarks are nearly useless for the systems our clients ship.
Read the field note →— Begin a conversation
Initial conversations are confidential and at no cost. We respond within two business days to every serious inquiry.
hello@meridianai.com