Companies are spending enormous amounts on AI tools—support bots, logistics copilots, sales assistants—but most can't answer a simple question: "How much money are we actually saving?"

Meanwhile, half the workforce won't touch these expensive AI features because they're worried about getting automated out of a job. And the other half who do use them are fighting with generic AI that doesn't understand how their specific company works.

AI integration is a journey, not a switch you flip. You start with enablement (AI helping humans work better), gather feedback and data, then gradually move toward automation (AI handling tasks autonomously). Most companies get stuck at the enablement stage because they can't measure what's working, can't get workers engaged, and don't know how to tune their AI for their specific business.

Without the right feedback loops and optimization, that expensive AI copilot stays mediocre forever—and full automation remains a fantasy.

WithAI plugs into whatever AI tools you're already using (no-code builders, or populate agent builder frameworks including OpenAI Agent Kit, Vercel AI SDK, Mastra, Langchain, etc.) and tell you three things nobody else can:

1. Actual Dollar Savings - Not "95% accuracy" or "2000 tokens/min." Real numbers like "$43K saved this month vs. human-only baseline"

2. Who's Using It (And Who Isn't) - See which teams are best working with AI and which teams need help. Get specific recommendations: "Sales team in Austin has 60% lower adoption—here's why"

3. How to Make It Better - Automatic suggestions based on what's working: "Your support agent picks non-preferred responses 23% of the time—add this to your prompt"

Plus we show you how you stack up against competitors. Think Microsoft Copilot Benchmarks, but for outcomes instead of just usage.

AI integration is a spectrum:

Enablement (Left Side): AI assists humans

• Customer support agent suggests responses, human reviews and sends

• Logistics copilot recommends carriers, human approves bookings

• Sales assistant drafts emails, human edits before sending

Automation (Right Side): AI handles tasks autonomously

• Support agent resolves common tickets automatically

• Logistics agent books freight without human approval

• Sales agent sends personalized outreach at scale

The Reality: You can't jump straight to automation. Here's the path:

1. Deploy in enablement mode - Low risk, workers stay in control

2. Collect feedback & data - Workers flag mistakes, suggest improvements

3. Optimize configuration - Tune prompts, add business context, improve accuracy

4. Measure improvement - Track ROI gains as AI gets better

5. Gradually increase autonomy - Let AI handle more without human review

6. Reach automation - AI runs reliably enough to operate independently

Where companies get stuck: Between steps 2 and 3. They deploy AI in enablement mode, but:

• Workers don't provide feedback (no incentive)

• Nobody's systematically collecting improvement data

• No clear metrics showing whether AI is getting better

• Can't justify moving toward automation without proof

How WithAI helps throughout the lifecycle:

• Audit - Predict how much additional ROI there is for an organization. Top of funnel feature and sets a goal

• Early Enablement - ROI tracking shows initial value. Proves AI is worth the investment even in copilot mode

• Feedback Collection - Rewards for worker input. Workers actively help improve AI instead of avoiding it

• Optimization - Automated recommendations based on patterns . "Your agent uses wrong tone 30% of the time—here's the fix"

The key insight: Good enablement becomes great automation. But you need the feedback loops, measurement, and optimization to get there. WithAI provides all three.

Here's a potential twist: we can turn AI adoption into a game where workers actually want to participate.

This type of feature may make sense to the extent that tokenized agent payment economics may in general be a growing trend, where the underlying infrastructure exists so this type of feature could be built pretty easily.

Workers could earn tokens for:

• Using AI features (daily bonuses for consistent use)

• Giving feedback that makes the AI better

• Labeling data that improves agent training

• Hitting productivity milestones with AI assistance

Tokens can be points on a leaderboard (gamification), actual cash bonuses (monetary), or a hybrid. Either way, suddenly workers have skin in the game—they're not worried about being replaced, they're earning rewards while learning valuable AI skills.

This solves two problems at once: low adoption AND the training data shortage that every AI team faces.

Private Equity & VC Roll-Up Funds (our main target)

• You're buying 5-10 HVAC companies, logistics firms, or dental practices

• You want to deploy AI across the portfolio and prove it's working

• You need unified ROI tracking and cross-company benchmarks

• You want to model AI improvement potential before you acquire

This could potentially include an internal business unit that buys legacy businesses to dogfood the product and generate case studies.

Traditional Industries Getting AI-fied

• Logistics & freight

• Field services (HVAC, plumbing, etc.)

• Healthcare services

• Property management

• Manufacturing & supply chain

Basically anywhere with tight margins, lots of operations, and a workforce that needs convincing.

Two ways to plug in:

1. SDK: Drop a few lines into your existing agent code (LangChain, OpenAI SDK, Vercel AI SDK, etc)

2. API Gateway: Change one URL and all your AI traffic routes through us automatically (like Helicone but for ROI tracking)

We're framework-agnostic and modular. New agent builder drops tomorrow? We'll have a connector by next week.

• SaaS subscription: Per company or per portfolio

• Affiliate revenue: When we recommend "you need a better logistics agent," we refer to partners

• White-label and professional services: For B2B integrations and complex enterprise setups

• Internal usage: Use WithAI on top of point solutions for internal roll up unit.

Because we answer the question nobody else can: "How much are we saving with AI, and how do we save more?"

Observability tools show usage. Evals show accuracy. Point solutions show their own metrics. We show the bottom line.

More importantly, we solve the stuck-in-enablement problem. Most companies deploy AI as a copilot and never progress because they lack:

• Proof it's working (ROI tracking)

• Worker engagement (token incentives)

• Systematic improvement (optimization recommendations)

• Confidence to increase autonomy (continuous measurement)

WithAI provides all four, creating a clear path from "AI suggests things" to "AI handles it autonomously." And with token incentives, we align worker motivation with company goals—workers get rewarded for making AI better, companies get better AI and valuable training data.

AI is hitting operations-intensive industries hard. PE firms see the arbitrage: buy traditional businesses cheap, deploy AI, improve margins 30-50%, sell at better multiples.

But they need proof it's working. That's where WithAI comes in - the platform for AI transformation enablement.