Sales enablement · For sales and marketing

Alpha

The AI forward deployed engineer for every team.

Who to sell it to, what to say in the first two minutes, and how we win.

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Why care

Every team has ideas. Few get an engineer.

Today · the engineering queue

Idea→Spec→Ticket→Triage→Roadmap queue

Months

With Alpha · an AI engineer in the team

Describe it→Approve the plan→Alpha builds and tests→Live

Same day

The person with the problem builds the fix, with the database, sign-in and hosting included.

The problem

Three things customers tell us.

01

“Our ideas wait months for engineering.”

Specs become tickets, tickets join a queue, and the team works around it in spreadsheets.

With Alpha

Describe it, approve the plan and see it working the same day.

02

“Our AI pilots never reach production.”

Demos made in chat tools have no database, no sign-in, no hosting and no owner.

With Alpha

The same session builds it, tests it, puts it live and runs it on a schedule.

03

“Our AI agents never get past the demo.”

Agents need developer frameworks like LangGraph, testing on real conversations and somewhere to run. Few teams can do all three.

With Alpha

Describe the agent. Alpha builds it in your framework, tests it and puts it live.

Why now

Anyone can build. Few get it live.

88%

of AI proofs of concept never reach wide deployment.

IDC · 2025

Building isn’t the hard part any more. Getting it live is: for applications, and even more for AI agents.

75%

of new code at Google is now written by AI.

Google · Apr 2026

42%

of companies dropped most of their AI projects, up from 17%.

S&P Global · 2025

43%

of data breaches involved AI tools IT never approved.

IBM · 2026

13%

of CIOs say AI has delivered significant value so far.

Gartner · Oct 2026

The shift in one line: AI made writing code easy, so the hard part moved to getting things live: for applications, and even more for AI agents.

88% of AI proofs of concept never reach wide deployment (IDC with Lenovo, March 2025).

Google says about 75% of its new code is now written by AI (April 2026): building is no longer the bottleneck.

S&P Global: 42% of companies abandoned most of their AI initiatives in 2025, up from 17%. IBM 2026: AI tools IT had not approved were involved in 43% of breaches. Gartner, October 2026: only 13% of CIOs report significant value from AI.

The second shift is agents. AI agents can now do real work: use tools and company data, run multi-step workflows, even answer the phone. But building them still means developer frameworks like LangChain, LangGraph or Google ADK, plus testing and hosting, so most stay demos.

Say: customers don’t need more prototypes; they need a path to production. Don’t quote MIT’s “95% of pilots fail”: its method is widely disputed. Click through each source before presenting: these numbers move quickly.

What Alpha is

An AI forward deployed engineer for every team.

Say this first

A forward deployed engineer sits with a business team, learns the real problem and builds the fix on the spot.

It’s how the AI labs get their biggest customers into production. Most teams never get one.

A human FDE

One team at a time

Alpha

Every team, today

	A human FDE	Alpha
Who gets one	The biggest projects	Every team
How soon	Weeks to staff and start	Today, in the browser
Who builds	The FDE, for the team	The team, with Alpha
When it ends	The team can’t change what was built	The team owns the code and keeps going

Who buys

Internal

Sell to the AI leader. Win the builders.

Signs

CIO, CTO or Chief AI Officer

Owns the AI budget and the risk. Wants AI in production, not more pilots.

Champion

Head of AI, or of a business team

The AI or innovation lead, or the head of finance, operations or sales ops. Has the backlog and the ideas.

Builders

Business and technical builders

Analysts and PMs; engineers and AI engineers. They build first and pull others in.

Influences

Security, architecture and engineering leads

Want safety, control and code they can maintain.

Best fit

Large enterprisesFinancial services and insuranceManufacturing and logisticsTech companiesIT services firms · partner channel

Fit signals

An AI budget or a push from the board

A backlog of internal applications

An AI agent use case: support, voice, documents

One team ready to build first

Walk away if

— No AI owner or budget

— They want a ready-made product

— They want an agency to build it

— It’s a one-off need

Internal: hidden in customer view. Who to call and who signs.

The person who signs owns the AI budget and the risk: the CIO, CTO, Chief AI Officer or head of the AI team. In a deal led by one function, it can be that function’s leader.

Your champion is the head of the AI or innovation team, or a business leader with a backlog (finance, operations, sales ops). Builders come from both sides: analysts and product managers in the business, engineers and AI engineers on the technical side.

Bring security, architecture and engineering leads in early; they can slow or stop the deal. Walk away for only four reasons: no AI owner or budget, they want a ready-made product, they want an agency to build it for them, or it’s a one-off need. Everything else is solvable: escalate hosting or identity requirements (on-premise, single sign-on) to product instead of saying no.

IT services firms are a separate channel: their engineers deliver client projects with Alpha, so pitch delivery speed, not internal adoption.

Why they act now

Listen for these moments.

The pain they feel

Ideas stuck in the engineering queue for months

AI pilots that never went live

Staff using AI tools IT hasn’t approved

The cost of waiting

Rivals’ teams ship every week. Every new AI agent becomes a consulting project. Unreviewed AI code keeps piling up.

Triggers to watch for

01A new Chief AI Officer or AI team

02The CEO or board asks for AI results

03A ChatGPT or Copilot rollout just finished

04An AI pilot stalled or was cancelled

05Budget planning season

06A freeze on engineering hiring

Why they would act now, not next year. Listen for the pain on the left and the triggers on the right: triggers turn “interesting” into “this quarter”.

The cost of waiting is concrete: rivals ship weekly while their ideas sit in a queue, every new agent becomes a consulting bill, and AI code nobody reviews keeps growing.

Make the cost of waiting concrete with their own numbers: how many requests are waiting, how long they wait, and what one is worth per month once it’s live. A cost they work out themselves persuades far more than one you quote.

More triggers to listen for: a competitor launches an AI agent, support volume or costs are rising, they’re hiring AI engineers, or a low-code or automation contract is up for renewal.

Two or more triggers in one call? Push for a discovery meeting with whoever owns AI.

The value

Three reasons customers buy.

01

Every team builds

Working software the same day, without waiting for engineering. No code and no setup.

Show it

Alpha asks plain questions, shows a plan, and one sentence becomes a live app.

02

Live, not just code

Database, sign-in, hosting and deployment included. Engineers skip the setup and can take over the same code in GitHub.

Show it

The app goes live behind a sign-in, in one click.

03

AI agents you can trust

Built as real code in the framework you choose, tested on realistic cases, and live with an API.

Show it

An agent’s test results, before it goes live.

This is what you say in the first two minutes of any call. Practise it word for word:

“In most companies, only the biggest projects get an engineer; everything else waits for months. AI can write code now, but code alone isn’t live software: it still needs a database, sign-in, hosting and someone to put it live. And AI agents are harder still: developer frameworks, testing, hosting. Alpha does all of it. You describe the application or the agent you need, Alpha asks a few questions and shows a plan, then builds it, tests it and puts it live, with Claude Code or Codex doing the building. Any team can build what it needs the same day, your engineers skip the setup, and you own the code.”

Then stop and ask which team in their company has the longest queue. Back each pillar with the “show it” moment from the demo, not with a slide.

It keeps working

Work that runs while you’re away.

“Every weekday at 9, send me a summary of the top news in our industry.”

Mon9:00

Tue9:00

Wed9:00

Thu9:00

Fri9:00

Sat—

Sun—

The session is closed. Alpha wakes it for each run.

01Alpha writes it and tests it once

02Then schedules it, in plain English

03It runs even with the session closed

04Every run is logged; failures show in the chat

Live demo

Show one team’s problem, end to end.

The customer

A finance analyst checks cloud bills from AWS, Azure and Google Cloud by hand, every month.

Keep it to

10 minutes. One story, no feature tour.

Show it in this order

01DescribeType the problem in plain English.

02PlanAnswer a few questions. Approve the plan.

03BuildWatch the app appear. Point at the screen to change it.

04ConnectSend each mismatch to the team’s Slack.

05PublishPut it live, behind a sign-in.

06ScheduleRun it every Monday, even with the session closed.

07IT viewShow the code in Git and the spend per person.

One customer story, start to finish. Tell it as the analyst’s day, not as a list of features.

1: type the problem in plain English. 2: Alpha asks a few multiple-choice questions, starting with whether this is a prototype or a production application; approve the plan. 3: the app appears in the live preview; point at part of the screen and ask for a change, like “make this a chart”.

4: connect Slack so each mismatch lands with the team. 5: publish it to a live address behind a sign-in. 6: schedule it to run every Monday. 7: switch to the IT view: the code in Git, ready for review, and usage and spend per person in the admin dashboard.

If time is short, cut steps 4 and 6, never step 7. Rehearse on a prepared session so the build is quick.

Three more minutes? Show an AI agent’s test results from a prepared session. Agents are our strongest point, and this story doesn’t show them yet.

What it builds

One AI engineer. Every kind of work.

Applications

An expense approval app with sign-in and a database.

AI agents

In ten frameworks, tested before they go live. Voice agents too.

Automations

A morning news digest, every weekday.

Spreadsheets

A three-year financial model with charts.

Decks and docs

A ten-slide pitch deck, exported to PowerPoint.

Images and video

A 30-second launch video for a new product.

Diagrams

An architecture map of a delivery app.

Tool connections

Reads Gmail, posts to Slack, updates Notion.

AI agents

Describe the agent. Alpha builds it, tests it and puts it live.

“Build a support agent that answers from our FAQ and hands off to email when it can’t help.”

Built in your framework→Tested on real cases→Live, with an API

The app that uses it is built in the same session.

01Ten frameworks, including Google ADK, LangGraph, CrewAI and OpenAI Agents

02Tested on realistic cases and personas, graded by an AI judge

03Frameworks compared on the same tests

04Voice agents, phone calls included

05Real code your team owns, in your GitHub

Agents are our strongest card. Lead with this slide whenever the customer has a support, voice, document or research use case.

The flow: describe the agent in plain English. Alpha designs it (one agent or a team of agents), builds it as real code in the framework the customer chooses (Google ADK by default; also LangGraph, CrewAI, OpenAI Agents, Pydantic AI, AutoGen, Claude Agent SDK, Vercel AI SDK, Lyzr and GitAgent), checks it and publishes it to the Agent tab, where the team sees its flow and can chat with it.

Evals: a suite of realistic cases and personas, each run several times and graded by code checks and an AI judge, with a report. The same tests compare frameworks to find which does the job best. An agent can’t go live until its tests pass.

It goes live as an API, next to the application that uses it, and both are built in one session. Voice agents run on a real-time speech model; phone calls work through Twilio.

Say: “Other platforms build agents that only run inside their platform. Alpha builds them as real code, in open frameworks you choose, tests them before they go live, and runs them next to the applications that use them.” Never say nobody else builds agents: Copilot Studio, Agentforce and OpenAI all do.

Control

Speed for every team. Control for IT.

Teams get

Plain English, nothing to install

A working version the same day

Their choice of coding agent

Agents in the framework they use

IT keeps

Code in your Git, ready for review

Agents that can’t go live until tests pass

Usage and spend, per person

Ownership of everything Alpha builds

The customer owns the code. Their engineers review it like any other change.

Enterprise

Live today · Roadmap

Ready for the whole company, not one developer.

Secure by design

Every session runs in its own isolated machineLive
No AI keys inside the machineLive
Secrets encrypted and hidden in the chatLive
Code scanning before anything goes liveRoadmap

Admin control

One dashboard of every user, session and appLive
Admins choose which tools teams can useLive
Admins switch features on or off for the companyRoadmap
Roles and audit logsRoadmap

Identity and spend

Sign in with Google or MicrosoftLive
Only your company’s emails get inLive
Usage and spend per person, with capsLive
Company single sign-on and auto provisioningRoadmap

Safe AI agents

Agents in any frameworkLive
Tests before an agent goes liveLive
Claude Code or Codex, per projectLive
Code in your Git, reviewed by your engineersLive

This is our answer to “is it ready for a company like ours?”. It mirrors what buyers now expect from enterprise AI builders (Replit’s enterprise page lists single sign-on, automatic provisioning, roles, private deployments, audit logs and a security scanner).

Lead with what’s live: every session runs in its own isolated machine (Replit’s own page says its sandboxes are containers “moving to microVMs”); AI keys never sit inside the machine; secrets are encrypted and hidden in the chat; one admin dashboard; admins choose which tools teams can use; usage and spend per person, with caps; sign-in limited to the company’s emails; tested agents; Claude Code or Codex.

Items tagged Roadmap disappear in customer view. Never present them as available, and never give a date: use the roadmap answer on the pricing slide.

Competition

Internal

Who they’ll compare us to.

Microsoft Copilot Studio

Agents on Microsoft’s platform, and simple team apps in preview.

AlphaAgents and apps built together, as code you own.

Replit

Quick apps, with enterprise controls already built.

AlphaClaude Code or Codex doing the building, and tested agents in any framework.

Lovable

Good-looking web apps, fast, with enterprise sign-in.

AlphaAlso builds agents, automations and content, with the coding agent you choose.

Emergent

Web and mobile apps, with custom agents.

AlphaAgents in real frameworks that can’t go live until tests pass.

Vercel v0

Full-stack apps, best in Next.js on Vercel.

AlphaAny stack, Python included, and agents in ten frameworks.

Manus

One general agent that researches and builds sites.

AlphaBuilds the agents your teams run, not just one assistant.

Rocket.new

Web and mobile apps, from designs too.

AlphaAgents, automations and content as well. They win on mobile.

ChatGPT, Claude and their coding agents

Answers for everyone; Claude Code and Codex for developers.

AlphaRuns Claude Code and Codex inside, and adds everything around them.

Internal: hidden in customer view. Be honest about where we lose; reps trust that more than a perfect pitch.

Most of these now build and host apps, and many have single sign-on and admin controls (Replit and Lovable both do). Don’t claim they don’t. What’s ours: AI agents built as real code in ten frameworks, tested before they go live; apps and agents built together; Claude Code or Codex doing the building; and apps, agents, automations and content in one place the customer owns.

Where we lose today: native mobile apps (Rocket.new, Emergent), the cheapest quick prototype (Lovable, Replit), enterprise controls Replit and Lovable already sell (single sign-on, roles, audit logs, security scanning), and simple team apps in all-Microsoft companies (Copilot Studio).

The next three slides go deep on the ones you’ll meet most: Microsoft Copilot, Replit and Lovable, then Claude Code and Codex.

v0 now builds full-stack apps (Next.js, with API routes and database integrations); its docs say Next.js gives the most reliable results. ChatGPT’s Codex has Sites (preview, Business and Enterprise plans), which hosts simple internal apps with storage and sign-in. Manus also builds and deploys full apps.

Against Microsoft Copilot

Internal

Microsoft builds the agent and the app apart. Alpha builds them together.

“Keep Copilot for everyday work and simple team apps. Use Alpha for the software and AI agents you want to own.”

01

One application, agent inside

In Microsoft, the agent is built in Copilot Studio and attached to an app built elsewhere. Alpha builds both in one session, as one codebase.

02

Builds in frameworks, not just connects

Copilot Studio connects to agents your engineers already built and host. Alpha builds them for you, in LangGraph, Google ADK, CrewAI and more.

03

Engineers can step in

In Copilot Studio’s apps, everyone changes the app by prompting, with no Git. In Alpha, engineers can also use a terminal, or their own editor through GitHub.

04

Production today

Copilot Studio’s app builder is in preview, “not meant for production use”. Alpha publishes live apps, with rollback, today.

05

Your choice of coding agent

Copilot Studio builds apps with GitHub Copilot. Alpha builds with Claude Code or Codex, chosen per project.

Where they win

All-Microsoft companies that want simple team apps on SharePoint or Dataverse data, inside Teams, paid for with Copilot Credits they already have.

Most enterprises already have Copilot, so don’t sell a switch. Position Alpha alongside it, lead with an agent use case or a production application, and talk to the AI leader and engineering rather than the Power Platform team.

Facts (Microsoft Learn, September 2026): Copilot Studio’s apps are in preview, “not meant for production use”; makers change an app by describing what they want, and the code view is read-only; Microsoft says complex or business-critical apps should go through normal engineering processes. In Power Apps, an agent is built separately in Copilot Studio and then attached to the app.

Copilot Studio agents run on Microsoft’s own low-code platform. It can connect to agents built elsewhere (Microsoft Foundry, the Microsoft 365 Agents SDK, and any agent that supports the A2A protocol) but doesn’t build them in those frameworks. Microsoft’s code framework, the Microsoft Agent Framework, is a separate developer tool written by hand.

Don’t claim that Copilot Studio can’t do voice agents (it can), that it doesn’t work with other frameworks (it connects to them through A2A, so say “builds in” versus “connects to”), or that drawing on the app is unique to Alpha (Microsoft can annotate the canvas). The preview status will change: check it before each deal.

Open question for product: can an Alpha app or agent read and write Outlook, SharePoint and Teams data today? Don’t claim it until confirmed.

Against Replit and Lovable

Internal

They build apps. Alpha builds what the business runs on.

“Great for apps. Alpha builds the apps and the AI agents your teams run.”

01

The best coding agents, your choice

Alpha builds with Claude Code or Codex. Replit runs its own agent; Lovable picks the model for you.

02

Real AI agents, in any framework

Google ADK, LangGraph, CrewAI, the OpenAI Agents SDK and Lyzr. Replit’s agents use one framework; Lovable has none.

03

Tested before they go live

An agent can’t be deployed until its tests pass, so what reaches people has been checked.

04

A whole machine per session

Each session is its own isolated machine, with a database, a shell and jobs that run on a schedule.

05

More than apps

Decks, documents, spreadsheets, images, video and diagrams, in the same place.

Where they win

Both already sell enterprise sign-in, roles and audit logs, and Replit a security scanner. Lovable is the fastest way to a good-looking first version.

Second most common question: “Why not Replit or Lovable?”. Both are strong for apps; position Alpha as what comes next: AI agents and the whole build, with the best coding agents doing the work.

Facts to stand on: Replit’s agents are generated in one framework (Mastra); Lovable’s docs say you can’t pick the model it builds with, and it has no agent framework. Replit lets users pick a model, so lead with Claude Code or Codex as the engine, not with “model choice”.

Be honest about enterprise features: Replit already has single sign-on, provisioning, roles, audit logs and a security scanner, and Lovable’s Enterprise plan has single sign-on (Okta, Entra ID, any SAML or OIDC provider), user provisioning, audit logs and security scans. Ours are on the roadmap; don’t lead with them against either.

Against Claude Code and Codex

Internal

They’re engines. Alpha is the whole workshop.

“Keep them for your engineers. Alpha puts the same coding agents in every team’s hands.”

01

Both coding agents, one place

Alpha runs Claude Code and Codex. Pick per project, and switch when a better model ships.

02

Built for people who don’t code

In the browser, in plain English, with questions and a plan before it builds.

03

It runs what it builds

Every project gets a database, sign-in and hosting, and publishes in one click with rollback.

04

Agents in any framework, tested

Google ADK, LangGraph, CrewAI and more. Agents can’t go live until their tests pass.

05

Company controls

One admin dashboard, spend per person, and admins choose the tools teams can use.

Where they win

An engineer who wants the agent inside their own editor and repository, and needs no hosting, data or company controls.

Landmine questions

Internal

Ask the question. Let them find the gap.

Ask	What it shows	Against
01 If you need an app with an AI agent built into it, how many tools and teams does that take today?	The agent and the app are built apart	Microsoft
02 When an app your business team built needs to become production software, how do your engineers take it over?	Engineers can only prompt, with no Git	Microsoft
03 When you build an AI agent, what framework is its code in, and can your engineers change it and run it elsewhere?	The agent is tied to one platform	Microsoft
04 How do you test an AI agent on realistic conversations before customers see it?	Agent testing is rare	Everyone
05 Which coding agent builds your apps, and can you switch when a better one ships?	One coding agent, chosen for you	Replit, Lovable, Microsoft
06 Once the code is written, who sets up the database, sign-in and hosting, and could someone in finance do it?	Built for developers only	Claude Code, Codex
07 How many engineers would it take to build and run this platform yourselves, and what wouldn’t they build instead?	A year of platform work	In-house

A landmine question points to a real gap in a competitor without naming them. You plant it early; when the competitor pitches, the customer asks them about it, and the gap shows.

Pick two or three per call, based on what the customer mentions. Ask, then stay quiet. Answer only when they ask how Alpha handles it, in one sentence, ideally with a demo.

The rule: only ask about real gaps. If the competitor answers easily, it goes off on you. Every row was checked against the competitor’s own docs in October 2026; check again before a big deal, because these products change fast.

Two more, for the right deal. Against v0: “Does it work as well in Python as in JavaScript?” (v0’s docs say Next.js gives the most reliable results). Against app builders: “Can it give you a real PowerPoint or Excel file?” Use this one only after testing v0 yourself.

Never name or criticise the competitor. The question does the work.

Discovery

Six questions for the first call.

Ask	Good sign	Bad sign
01 What applications or automations are your teams waiting on, and for how long?	A long list and months of waiting	“Engineering keeps up fine”
02 How many AI pilots did you start last year, and how many are live?	Many pilots, very few live	“We haven’t started with AI”
03 Where would an AI agent make the biggest difference?	A named use case: support, voice, documents	“Agents are hype”
04 How much engineering time goes to internal requests and setup?	“Too much. They keep getting pulled in”	Plenty of spare capacity
05 Who owns AI here, and is there budget this year?	A named leader and a budget	Nobody owns it: walk away
06 If teams could build their own apps and agents, who would start, and what comes next?	Several teams and a list of ideas	“Build it for us”, “just this one app”, “anything ready-made?”: walk away

Objections

Eight objections, and what to say.

Incumbent

“We already have Copilot and Copilot Studio.”

Keep them. Alpha is for production software and AI agents you own, built together, in your GitHub.

Incumbent

“Our engineers use Claude Code or Codex.”

Good: Alpha runs both, puts them in every team’s hands, and adds the database, sign-in and hosting.

Incumbent

“Our engineers can build this themselves.”

They could. Should they spend a year on the platform, or on your product?

Risk

“AI-written code is risky.”

It lives in your Git, your engineers can review it, and agents can’t go live until their tests pass.

Risk

“What about security and our data?”

Each project runs in its own isolated machine with no AI keys inside, and only your company’s emails get in.

Risk

“Will we be locked in?”

No. Pick Claude Code or Codex, build agents in open frameworks, and keep the code.

Fit

“Our people aren’t technical.”

That’s who it’s for: plain-English questions and a plan before anything is built.

Fit

“We don’t know what to build.”

Most teams don’t at first. We’ll pick a first project together, team by team.

Agree with something true, reframe it, back it with a live fact, then ask a question. Proof from the demo beats any slide.

Incumbent: for Copilot, use the Microsoft slide. For “we’ll build it ourselves”: isolated machines, hosting, sign-in and agent testing are a year of platform work. For Replit or Lovable: “They’re great for apps. Alpha also builds the AI agents your teams run, tested before they go live.”

Risk: “What if usage costs run away?” Spend per person, with caps they set. “If everyone builds, won’t it be chaos?” It’s already happening in tools IT never approved; Alpha brings it into one place, with an admin view of every user, session and app. “You’re a new product.” Start with one team and agreed success criteria; the code is theirs either way.

Fit: “Our use case is too complex or regulated”: start with something contained next to it, and let engineers step in on the same code. On-premise hosting, single sign-on or audit logs: “Tell me your requirements, and I’ll bring our product team to the next call.” Escalate it as a large account; never say no and never promise a date.

If they ask about price: pricing isn’t set yet. Say: “It depends on how many teams and how you roll it out. Let’s agree on the first team and the problem it solves, and I’ll come back with a proposal.”

Pricing and roadmap

Internal

How to shape the deal.

Start small

One team and one real problem. Prove it in weeks, then grow.

Pricing

[Tiers, typical deal size and discount limits: to confirm]

Big accounts

Large, high-usage logos go to leadership before any number is shared.

When they ask “when is it coming?”

“It’s on our radar, and I won’t promise a date I can’t keep. Tell me what it would unlock for you, and I’ll bring our product team to the next call.”

Never promise a date or a feature

Summary

What to remember.

01 · Why Alpha

Every team has ideas that wait for engineering.

Alpha is an AI forward deployed engineer for every team.

AI writes code now. Getting it live is the hard part.

02 · Who and how

Sell to the AI leader. Win the builders.

Ask the six questions before any demo.

Demo one team’s problem, end to end.

03 · How we win

Tested AI agents, in the framework the customer chooses.

Live software, not just code, and the customer owns it.

Answer every objection with proof.

Next step

Book a discovery call, then scope a pilot with one team.

Get help

Pitch deck, battlecard, and the product team for deal support.