I help enterprises move from AI experiments to production platforms — without vendor lock-in, uncontrolled agents, or architecture chaos.

Independent AI Platform Advisor  ·  Architecture → Reference Implementation → Golden Path

AI gateways · agent runtimes · governance · evaluation · developer platforms · deterministic execution · local-first AI

The problem I am called in for

A CTO eighteen months into GenAI has five gateways, three orchestration approaches, inconsistent security, no evaluation standard, and no coherent path for the next team that wants to ship.

Every individual decision was defensible. The aggregate is not a platform, and some of what has already been built is about to become irreversible.

That is an architecture problem, and it has a short, bounded solution. It does not require a three-year systems integrator engagement, and it does not get fixed by adding another vendor.

The Enterprise AI Platform Architecture Sprint

Four to eight weeks. One architect. A defined end.

  • Map the existing system as it actually is, not as the diagrams say.
  • Identify the decisions that are about to become irreversible — and separate them from the ones that can wait.
  • Define the target platform: gateway and routing, agent runtime, evaluation, governance and security boundaries.
  • Establish the interfaces and contracts that let teams move without coordinating on every change.
  • Build a skeletal reference implementation and one production-quality vertical slice — working code, not slideware.
  • Encode the result as a golden path in your developer tooling, so the fiftieth team ships the way the first one did.
  • Transfer it to your engineers. Then leave.

Your team owns the architecture, the decision records, and the running code. There is no dependency on me afterwards, and no three-year backlog to maintain. That is the point of the engagement shape, not a limitation of it.

How to engage

Platform Diagnostic

2–3 weeks

Current state, irreversible decisions, target architecture, prioritised roadmap. Fixed fee. The entry point: it stands alone, and it is how both sides find out whether the larger engagement is worth doing.

Architecture & Reference Implementation

6–10 weeks

The full sprint above. Fixed fee by scope. Ends with architecture, standards, a reference implementation and a golden path your teams own.

Fractional Principal AI Platform Architect

1–2 days / week

Standing design authority. Monthly retainer, three-month minimum. Design review, decision-record sign-off, vendor selection, and mentoring for your staff and principal engineers.

What I do not take

Staff augmentation, production support rotations, long-run backlog ownership, generic cloud migration, and people-management roles. The practice is deliberately narrow: three offers, repeatable, priced as outcomes. I am a hands-on architect, and I write the reference implementation myself.

Why this practice, and not a slide deck

  • Fortune 500 scale, in production. Created a Fortune 500 specialty retailer's enterprise operating model for AI delivery — approved at CTO level — and put a vendor-neutral enterprise AI gateway with policy enforcement into production behind it.
  • Architecture across large delivery organizations. Led architecture across 22 squads and 100+ developers for a sovereign-cloud platform at Nike; guided a 16-person team at DTEK.ai through a computer-vision platform at 99% accuracy and 15 ms latency.
  • Public, runnable proof. An open-source portfolio built independently of any employer and generalised from recurring classes of problem, never from client implementations. Work in progress and released projects both live at github.com/hseshadr.
  • Thirty years, still hands-on. Two U.S. patents. Enterprise architecture across retail, financial services, and consumer platforms.

The point of view you are buying

Deterministic before probabilistic

A model is a component, not an architecture. The parts of a system that must be auditable — authorization, contracts, release gates, money, compliance — should be deterministic and verifiable, with the probabilistic parts bounded inside them. Most enterprise AI risk comes from inverting that.

Platforms before applications

The first AI application is an engineering problem. The fiftieth is a platform problem, and organizations discover this roughly two years and several rewrites late.

Architecture as tooling

A standard nobody can execute is a document. A standard compiled into golden paths, templates, and CI gates is a standard. I judge my own work by whether your engineers can ship correctly without having read anything I wrote.

For intermediaries and partners

GainRatio provides fractional Principal-level AI Platform Architecture: we help enterprises turn fragmented GenAI experiments into governed production platforms, then leave behind the architecture, skeletal implementation and golden path for their teams to own.

GainRatio Inc. contracts as the supplying entity, corp-to-corp. A commission or markup is expected and welcome — an intermediary who originates and manages the account earns it. Engagements are fixed-scope and time-bounded by design; that is what makes them sellable to a CTO with a budget and a board date.

Contact

Harish Seshadri — Founder & Principal Architect