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

Start with a 2–3 week Platform Diagnostic See how the engagement works

The problem I am called in for

Eighteen months into GenAI, most enterprises have five gateways, three orchestration approaches, 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

Six to ten 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.

What done looks like

  • A canonical gateway and agent-runtime strategy, with deliberate exceptions and migration paths off the redundant platforms.
  • A production vertical slice running under the target architecture — not a prototype.
  • A golden path in your own tooling: the opinionated, executable default that makes the correct way the easy way for the fiftieth team.
  • Every irreversible decision documented, dated, and either taken deliberately or explicitly deferred.
  • Your engineers able to extend it without me. That is the acceptance test.

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 — see below.
  • Thirty years, still hands-on. Two U.S. patents. Enterprise architecture across retail, financial services, and consumer platforms.

I build the thing, not a deck about the thing

One thesis runs through all of it: the parts of a system that must be trusted should be deterministic, signed, and verifiable offline — with the probabilistic parts bounded inside them. These are open source, independent of any employer, and they run.

  • AML-Filter

    Screens a name against 31,566 sanctioned entities — OFAC SDN, EU, UN and UK OFSI — entirely inside the browser tab. Signed watchlist bundles; nothing leaves the machine.

    Live demo  ·  Source

  • EdgeReco

    Product search and recommendations that run on the shopper's device. A 720-product catalog ships as a 1.5 MB signed, content-addressed bundle.

    Live demo  ·  Source

  • EdgeProc

    Ships a search index to a device as signed, content-addressed bundles: fail-closed Ed25519 verification and FastCDC chunking, so the next release downloads only the chunks that changed. 309 tests, 98% coverage.

    Source

Also public: Assay / Avow — sign a decision record with Ed25519 over RFC 8785 canonical JSON, then verify it offline years later  ·  privacy-core — redact PII in the browser before a prompt ever leaves the machine, then rehydrate the reply locally. Everything at github.com/hseshadr.

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.

Evaluation is a release gate, not a dashboard

Monitoring tells you what an agent did after it affected a customer. That is observability, not control. The question a platform has to answer is what an agent will do — exercised against real and synthetic workflows, adversarial inputs, and explicit policy boundaries, before release. Evaluation belongs in CI and in the golden path, on the same gate every other change passes through. The deterministic layer is what makes the gate enforceable: you can assert on authorization, contracts and side effects even where you cannot assert on prose.

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.

Gain Ratio, 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.

Before you spend another year scaling the wrong architecture, spend a few weeks establishing the right one.

You will be talking to the architect who does the work. There is no bench, no account team, and no discovery phase that bills for six weeks before anything is designed.

Start with the Platform Diagnostic — fixed fee, 2–3 weeks

Contact

Harish Seshadri — Founder & Principal Architect