Guide

How to Use Fulcrum

A discovery instrument you run live, during the call. You score what you hear across three legs; it finds the one weakest leg — the binding constraint — and resolves an honest starting point with a phased roadmap you can leave behind.

What it's for

Companies want to start their AI journey at the exciting use case. The discipline this tool sells is starting at the constraint, not the ambition — the right first move is a function of the weakest of the three legs, which is usually driver clarity or the data foundation, not the fun part.

Run it live, in three legs

Set the scene. Fill in client, attendees, and date at the top. It autosaves as you go, so a call is never lost.
Score Leg 1 — the Driver as they talk: what's really pushing this, whether success is even defined, and whether the stated driver matches the real one. The gap between stated and real is often the first insight worth naming.
Score Leg 2 — the Foundation: data readiness, platform & security posture, and how people reach AI today. Data reality blocks more AI work than model choice ever does.
Score Leg 3 — the Capability: whether the team can evaluate AI output rather than trust it, their technical literacy, and whether they can sustain what you build after you leave.
Capture what they said in the notes field under each leg — verbatim quotes are gold in the readout.
Hit Resolve Starting Point once the driver questions are answered (more legs = a sharper result).

How it decides

Each leg meters 0–100 as you answer. The binding constraint is the lowest leg — that's what the recommendation keys on. It resolves to one of four starting points, in priority order:

Governance & Sanctioned Path — when sprawl is the driver or AI access is ungoverned. Guardrails before projects.

Manufacture Clarity — when the driver is a vague mandate with no measurable outcome. Don't build yet; convert it into a decision.

Foundation First — when the driver is real but the data/platform can't support it. Ship the unglamorous enabler first.

Narrow Pilot — when driver, foundation, and team are all ready. One small, legible, low-risk win that builds the muscle.

Two flags can fire alongside any result: a guardrail flag when AI access is ungoverned (a sanctioned path belongs in Phase 0), and a capability flag when the team is the weak leg (run a guided pilot so the win survives your exit).

The buttons

Resolve Starting Point generates the recommendation, reasoning, phased roadmap, and board-ready success criteria. Print / Save PDF produces a clean client leave-behind (it hides the controls and unselected options). New Assessment clears everything for the next call. Everything autosaves to this browser between sessions.

Reference

Engine & Model Solutions

The use case comes first; the engine and model come second. This is a vendor-neutral map of the major GenAI platforms and access routes — a decision framework to pick the best fit for a customer, not a leaderboard.

This is a framework, not a scoreboard. Specific models, prices, context windows and certifications change monthly — treat named products as a starting point and confirm current specs. Use the Recommend engine & model action on a result to apply these criteria to a specific customer.

Choose in this order

Access route (engine) first. Compliance and data handling decide more than the model does — pick the route that satisfies the customer's regulatory and residency needs before anything else.
Provider / family second. Within an acceptable route, choose the vendor whose strengths and ecosystem fit the workload and the customer's existing stack.
Model tier last. Match capability to the task: a fast/cheap tier for high-volume simple work, a frontier tier only where the task genuinely needs it.

Access routes (the "engine")

Direct provider API — Fastest access to the newest models; you own governance, logging and data handling. Good for custom builds with an internal platform team.

Cloud platform (AWS Bedrock, Azure OpenAI / AI Foundry, Google Vertex AI) — Models run under your cloud contract, region and IAM; supports BAAs, data residency and swapping models behind one seam. The safe default for regulated or enterprise workloads.

Enterprise app (ChatGPT Enterprise, Claude Team/Enterprise, Gemini for Workspace, Microsoft 365 Copilot) — Fastest path to workforce productivity with admin controls and no build; less customization.

Self-hosted open-weight — Maximum control and data sovereignty; you run the infrastructure, scaling and safety. For air-gapped or highly sensitive environments with in-house ML capacity.

OpenAI — GPT / ChatGPT

Broadest ecosystem and mindshare; strong general-purpose and multimodal.

Pros

Largest tooling and integration ecosystem; deep developer familiarity
Strong general, coding and multimodal capability
Enterprise route via Azure OpenAI for in-tenant data, compliance and regional control

Cons

Fast release cadence can mean churn
Governance needs deliberate setup on the direct API
Top capability concentrated in higher-cost tiers

Best fit: Widest ecosystem, off-the-shelf workforce productivity, or Azure-centric enterprises.

Anthropic — Claude

Strong reasoning and long-context; safety-forward.

Pros

Strong at reasoning, long documents and careful instruction-following
Clear API data posture (no training on API data by default)
Available via AWS Bedrock and Google Vertex for enterprise/compliance routes

Cons

Smaller third-party ecosystem than OpenAI
Fewer consumer-facing features
Narrower multimodal breadth

Best fit: Regulated or document-heavy work; teams already on AWS or GCP.

Google — Gemini / Vertex AI

Deep Google Cloud and Workspace integration; strong multimodal.

Pros

Tight integration with Google Cloud, Workspace and BigQuery
Strong multimodal and long-context
Vertex AI adds enterprise controls, residency and multi-model access

Cons

Enterprise/developer experience can be uneven
Pulls you toward the Google ecosystem
Product naming and versions shift often

Best fit: Orgs already on Google Cloud/Workspace, or heavy multimodal and data-warehouse needs.

xAI — Grok

Fast-moving; real-time signal via X; strong on some reasoning/coding.

Pros

Access to real-time / X data
Rapid capability gains
Appealing where less-restrictive outputs or X integration matter

Cons

Newer enterprise, compliance and governance story
Fewer certifications and integrations
Smaller ecosystem and track record

Best fit: Real-time/social signal needs, or teams trading enterprise maturity for speed.

Meta — Llama (open weight)

Open-weight models you can self-host.

Pros

Self-host for data sovereignty, air-gapped or on-prem use
No per-token cost; full control and fine-tuning
Portable across clouds

Cons

You own ops, scaling, security and guardrails
Needs ML / infra talent
Frontier gap vs. top closed models on the hardest tasks

Best fit: Data-sovereignty or cost-at-scale needs with in-house ML capacity.

Mistral

Efficient open + API models; EU-based.

Pros

Strong open-weight options (self-host or API)
EU domicile helps GDPR / data residency
Efficient smaller models for cost and latency

Cons

Smaller ecosystem
Frontier gap on the hardest reasoning tasks
Fewer enterprise integrations than the majors

Best fit: EU data-residency needs, or cost-sensitive / self-host deployments.

The AI Readiness Assessment

How to Use Fulcrum

What it's for

Run it live, in three legs

How it decides

The buttons

Engine & Model Solutions

Choose in this order

Access routes (the "engine")

OpenAI — GPT / ChatGPT

Pros

Cons

Anthropic — Claude

Pros

Cons

Google — Gemini / Vertex AI

Pros

Cons

xAI — Grok

Pros

Cons

Meta — Llama (open weight)

Pros

Cons

Mistral

Pros

Cons