ARCHITECT AI | Strategy Studio
✦ Linear AI Project Planning Studio

Enterprise Business Objective

Select a strategy blueprint below to inspect its live milestone timeline, or enter your custom operational bottleneck.

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● Live Blueprint30-Day Pilot Ready

Customs Brokerage and Compliance Automation

Sub-second automated document parsing, customs compliance validation, and ERP sync for 400 operational staff. Halts $2.5M manual cost bleed and accelerates filing output 10x.
04 4-Quarter Linear Milestone RoadmapClick any phase to inspect deliverables

Deploys sub-100ms classification pre-filter for 50 operational staff. Verifies filing accuracy and locks measurement baseline.

Key Deliverables:
  • Sub-100ms document classification pipeline online
  • Human-in-the-loop validation interface deployed
  • Baseline error rate locked with 0 manual filing backlog
05 Direct Execution Terms & PricingLive in 30 Days
Stage 1 Pilot Fee: $25,000 / 30 days
Stage 2 Monthly Retainer: ₪45,000/mo ($12,200)

01 — What that plan is worth

You just generated the deliverable
enterprises pay $50,000 for.

The role that produces it is called a Forward Deployed Engineer — the hybrid architect Palantir invented and Salesforce, OpenAI, and Anthropic now compete over. Here is what that costs on the open market.

$50,000+

Discovery phase

What an enterprise AI consultancy charges before a line of code is written. Six weeks. The deliverable is a slide deck.

$250–$400+

Per hour

Professional-services billable rate for an FDE-tier resource — the technical people who actually get AI into production.

$300k–$600k

Per year, fully loaded

What a client company pays to keep one Forward Deployed Engineer embedded in their team for 6–12 months.

$0

What you just paid

The plan on this page — executive summary, architecture, attribution formula, four-quarter roadmap. Generated in seconds.

 Traditional FDE engagementThis engine
Time to first plan3 to 6 months of discoverySeconds — then weeks to a live pilot
Cost to see the plan$50k–$500k committed up frontFree. Six plans, no card, no call
Who does the deliverySenior team pitches, juniors buildThe engineer who wrote the engine builds it
What discovery producesA slide deck and a statement of workArchitecture, attribution formula, roadmap
How it scalesLinearly — they must hire more engineersThe planning layer is software; delivery is me

It is an engineering job, not a sales job

70–90% of an FDE's week is shipping production code. They are paid on engineering scales with equity, not on sales quotas and commission.

Total comp runs $200k–$400k

Salesforce publishes base bands of roughly $99k–$186k, rising to $119k–$203k in San Francisco and New York and past $365k for principal tracks. Palantir- and Anthropic-tier AI FDEs land nearer $250k–$450k+.

Clients rent them, they don't hire them

FDEs are procured through professional services or top-tier support contracts. The client pays a fully loaded rate covering overhead and margin on top of the salary.

The premium is for the deployment gap

Internal IT teams can run a demo. Bridging a demo to a working production system inside real data, real latency, and real compliance is the scarce part — and it is what the plan above is scoped around.

Figures are public market benchmarks for the Forward Deployed Engineer role, compiled from Salesforce’s published pay bands, Levels.fyi reporting, and Andreessen Horowitz’s writing on the forward-deployed model. They describe what the role costs in the market — they are not quotes for my work. My pricing is below, in full.

02 — How this works, and how I get paid

I gave away the expensive part
on purpose.

The planning phase is where consultancies make their margin and where buyers lose six weeks. I automated it and made it free. You only pay me once you want the thing actually built — and every stage after that is something you can walk away from.

Stage 00 — The plan

Free

Months of forward-deployed planning, generated instantly.

Three plans without an account, six once you register. No card, no discovery call, no six-week engagement to find out whether the idea is buildable.

  • Executive summary, architecture, and four-quarter roadmap
  • Attribution formula written before any money changes hands
  • Yours to take to your own team — or to another vendor

Stage 01 — The pilot

Fixed fee + upside

One production feature, live in 30 days.

Not a proof of concept in a sandbox. A real capability wired into your real data, your latency budget, and your compliance boundary — the part that kills most enterprise AI.

  • Fixed price quoted by the generator above, agreed before we start
  • Upside is measured against a baseline we lock in writing on day one
  • If it does not clear the baseline, you keep the work and we stop

Stage 02 — The retainer

Sliding cash / equity

The one-year plan, executed.

A monthly retainer on a sliding scale between cash and equity — you choose the mix. Trading equity buys the monthly rate down; the generator above prices both ends of that scale live.

  • Same engineer through the whole rollout — no hand-off to juniors
  • Cash-heavy if you are funded, equity-heavy if you are not
  • Cancellable — the retainer is for building, not for lock-in

Stage 03 — The platform

Managed monthly

When the building stops, the running does not.

Most of what breaks after launch is unglamorous: harness code, model upgrades, compliance evidence, the 3 a.m. page. You can hand all of it to me and keep your own team on your own product.

  • Hosting, monitoring, and on-call for what we shipped
  • Model and dependency upgrades handled before they break you
  • SOC 2 / HIPAA / GDPR evidence gathering and audit support

What you own, and what I keep

You own the deliverables named in our scope, plus a permanent licence to any of my components needed to run them. I keep my pre-existing platform, reusable components, and general know-how. Confidentiality is mutual and narrow. Please do not send anything confidential before an NDA is in place — a general description is plenty at this stage.

Talk about a pilot →

03 — Why it usually fails

Most enterprise AI dies
on the way to production.

The demo trap

Prototypes built on clean sample data break the moment they meet real edge cases, real latency budgets, and the state your database is actually in.

The junior hand-off

Agencies sell you the senior team in the room, then assign the build to whoever is on the bench. The architecture decisions get made by the person with the least context.

The attribution void

Without a baseline agreed before the work starts, nobody can prove the system paid for itself — so it quietly loses its budget at the next planning cycle.

04 — What I build

Four shapes of work
that survive contact with production.

Workflow cost automation

The paperwork, compliance checks, and data re-entry that quietly consume a department. Automated end to end, with a human validation step where being wrong is expensive.

Revenue staff multipliers

Your highest-value people — advisors, analysts, specialists — given a domain-tuned system so they can cover an order of magnitude more accounts without losing the personal touchpoints that win the business.

Customer-one products

Build the AI product for your own operation first, prove the unit economics on your own P&L, and only then decide whether it is worth selling to anyone else.

Multi-tenant platform architecture

Turning an internal capability into a tenant-isolated platform you can license under your own brand — the part that needs to be designed in from the start, not bolted on later.

05 — The receipts

Three engagements, described
the way they actually happened.

No testimonials, no invented percentages, no logos I have not earned. What follows is what shipped, what is still gated, and where I was one engineer on someone else’s team.

Client pilot · 2026

Clinical documentation, structured from a raw session recording

A behavioural-health provider needed ASAM 3.1 assessments written up without clinicians losing an evening to paperwork. The system takes a diarized therapy-session transcript and maps it onto 50 structured fields of the provider's real note format, using speaker-aware transcription and schema-constrained model output rather than free-text summarisation.

Live and gated. Automatic audio capture is deliberately still switched off until clinicians sign off on the field mapping — the failure mode of getting a clinical note wrong is worse than the cost of a manual step.

Shipped
49 commits, first to production in under four weeks
Runtime
Edge worker, access-token gated, PHI safeguards reviewed
Method
Speaker-diarized transcription → schema-constrained extraction

My own product · ongoing

A multi-tenant realtime voice and messaging platform

The infrastructure underneath the client work: seven applications covering API, background workers, marketing, mobile, web, and a WhatsApp adapter and runtime. Realtime voice over telephony, tenant isolation, and the unglamorous harness code that makes model output survive contact with production.

This is my product, not a client engagement. It is listed here because it is why a pilot can move in 30 days — the harness already exists — and because it is the reason I no longer sign category non-competes.

Scale
~2,060 TypeScript files across 7 applications
History
3,305 commits since October 2025
Stack
Realtime speech APIs, Twilio telephony, Postgres

Team engagement · 2023–2024

Health-media platform, backend and frontend, on a distributed team

Nearly two years on a production healthcare content platform, working across backend and frontend alongside four or more other engineers in different timezones. The relevant experience here is not the code — it is knowing how enterprise delivery actually behaves when there are review gates, other teams, and a release calendar you do not control.

Explicitly not a solo build. I was one engineer on a team, and I am naming it that way because the alternative is the kind of proof inflation this page exists to argue against.

Duration
January 2023 – September 2024
Contribution
~101 backend commits among a 5+ engineer team
Context
Distributed team, regulated content domain

Public and verifiable

Developer Advocate at Wix

Wrote the developer ecosystem content for Wix Headless APIs — public, bylined, and still online.

See it →

Published technical writing

Architecture breakdowns, ML lifecycle guides, and code-first walkthroughs under my own name.

See it →

Multi-model production routing

Cascading fast classifiers into frontier models for reasoning, with PII masking between the layers.

Generative media and spatial

Three.js and Gaussian splatting for 3D on the web, synthetic audio, and custom vision workflows.

06 — Inside the engine

Years of senior AI architecture and platform engineering, baked directly into the tool engine.

This strategy generator is not a generic wrapper. It incorporates years of hands-on platform engineering, Developer Advocate expertise at Wix, multi-model agentic routing, and operational workflow automation.

Yitzi Ginzberg — Creator & AI Architect

Yitzi Ginzberg

Creator & Principal AI Architect

What powers the Architect AI evaluation engine?

When you input your enterprise objective, the engine evaluates your request against real-world production architectures, baseline attribution formulas, and governance controls derived from proven deployments.

Wix Developer Advocate Heritage

Built by former Wix Developer Advocate leading developer ecosystem content for Wix Headless APIs. Read guides on the Wix Author Column →

Schema-Constrained LLM Pipelines

An instant local classifier scopes your objective, then gpt-5.6 Sol writes the plan against a strict JSON schema on the Responses API — streamed back section by section, parseable by construction rather than by retry.

Attribution before delivery

Every plan it writes starts by naming the baseline to measure against — because the engagements that lose their budget are the ones that never agreed what “working” meant.

Verified Public Proof

Open-source code, 3D WebGL / Gaussian splatting guides, and ML publications documented on dev.to/yitzi →

Back to the generator→

07 — Writing & publications

Live Engineering Feed.
Articles, guides & publications.

Written by Yitzi G. · Former Wix Developer Advocate

Featured AI Toolkit GuideWix Dev & Dev.to Deep-Dive

Building a WhatsApp Chatbot with Wix Headless API & AI Toolkit

Full technical walkthrough of building an autonomous e-commerce bot using WhatsApp, @wix/headless-ai toolkit, function calling, and real-time cart orchestration.

Read Full AI Guide & Architecture →Updated Guide 2026
Wix Engineering PostWix Engineering Blog

Wix Headless APIs: Unlocking Multi-Platform Headless Architecture

Comprehensive technical guide on leveraging Wix Headless APIs to power custom React, Next.js, and mobile applications with enterprise backend services.

Read Article →Wix Engineering
ML Deep-DiveI On Demand Engineering Blog

The Never-Ending Story of Model Training: A New Chapter for Machine Learning

In-depth technical breakdown of continuous model retraining, automated feedback loops, and production AI model lifecycle management.

Read Article →I On Demand Blog
Author ColumnWix Developer Blog

Yitzi Ginzberg — Official Author Column & Developer Guides at Wix

Collection of technical tutorials, API integration blueprints, and developer ecosystem guides written during my tenure as Developer Advocate at Wix.

Read Article →Wix Author Column
Open Source CodeDev.to Technical Walkthrough

3D Gaussian Splatting & WebGL: Real-Time Spatial Experiences with Three.js

Code-first breakdown converting captured video into interactive 3D web environments using Gaussian splatting, Three.js, and custom shader pipelines.

Read Article →Dev.to / yitzi

08 — Fit

I am a bad fit for plenty of good companies.

Worth reading before you fill in the form — it saves us both a call.

Worth a conversation

  • You have an operational bottleneck that costs real money every week it stays unsolved.
  • You are willing to agree a measurement baseline before the work starts, even if the number is unflattering.
  • You want the person who designs it to also be the person who ships it.
  • Someone with budget authority is actually sponsoring this internally.

Go elsewhere

  • You need a large team billing many seats — that is a job for an agency, and there are good ones.
  • You want strategy decks without anyone writing production code.
  • You are optimising for the cheapest possible day rate.
  • There is no internal sponsor, so nothing can actually be deployed at the end of it.

09 — Get in touch

If the plan looks right,
I’ll build the first piece of it.

Take the generated plan to your own team first if you want — it is yours either way. If you would rather I executed it, tell me what you are trying to fix. Please keep it general: nothing confidential before an NDA is in place.

Apply to work together

Three questions

Keep it general — please don’t include confidential information here. We’ll sign a mutual NDA before anything sensitive changes hands.

✦ Evaluated Enterprise Terms & Grand Slam Proposal

Adjust Offer ↑

Dynamic Pilot → ₪45,000/mo retainer + Performance Savings & Revenue Share · 0% equity stake

Scope Tier: Enterprise Automation

Be specific. “Logistics” is a category; “customs brokerage for EU-bound perishables” is a description. Minimum 10 characters.

The thing that would make a real difference to how the business runs. Rough is fine. Minimum 10 characters.

Internal attempts, vendors, off-the-shelf tools — whatever didn't get there, and why. Minimum 10 characters.

I read these myself · Mutual NDA first