Case Study: Our First Enterprise Pilot: 50-Person Consulting Firm, $13 in Month One

A 50-person consulting company became Intrascope's first enterprise pilot. They were not heavy AI users on day one. They were a team spread across tax advisory, financial consulting, business advisory, magazine editors, and directors, mostly working from free ChatGPT accounts, with a few people trying Gemini on the side.

The answers were not good enough for their standard of work. When we implemented Intrascope, it became the firm's first structured encounter with premium AI models. One month later, cautious daily usage was growing, first-month API spend totaled $13, and together we shipped a custom RAG module connected to more than 1GB of internal regulations via n8n.

This is the story of what changed, and why it matters for consulting firms that still rely on free tools.

The company profile

The firm operates across several consulting disciplines. Work depends on accurate interpretation of regulations, fast client communication, editorial quality, and internal alignment between departments.

• Team size: 50 employees
• Departments: tax advisory, financial consulting, business advisory, magazine editors, directors
• Prior AI usage: mostly free ChatGPT; some Gemini; no paid team workspace
• Main pain point: answer quality from free tools was too weak for professional consulting output
• Goal: introduce premium models in a governed workspace without buying personal subscriptions for everyone

Leadership did not need experimental AI for its own sake. They needed consultants, editors, and directors to get reliably better answers from one approved environment.

Where they started: free tools, uneven quality

Before Intrascope, AI inside the firm looked familiar to many consulting offices.

• Most people used free ChatGPT for quick drafts and explanations
• A few employees experimented with Gemini
• Output quality was inconsistent and often not strong enough for client-facing work
• There was no shared context, no project structure, and no central visibility
• Premium models were effectively unavailable without personal paid accounts

Free models helped with small tasks, but they did not deliver the depth required for tax interpretation, financial explanation, or polished editorial work. At the same time, giving every employee a Plus or premium subscription would have been expensive long before usage justified the cost. That trade-off is exactly what we break down in why API access is safer and cheaper than subscriptions.

Our first enterprise pilot: full onboarding from day one

This was not a self-serve rollout. As Intrascope's first enterprise pilot, the firm received hands-on onboarding across the full workspace setup.

• Help registering and connecting API accounts on OpenAI, DeepSeek, and xAI
• Model access limits so only approved providers and tiers were available
• User invites and department-oriented project structure
• Workspace configuration, balance top-ups, and launch readiness checks
• Guidance on how consultants, editors, and directors should use AI in daily work

For teams evaluating a similar path, our get started guide covers the same building blocks: plan selection, API keys, users, limits, and projects.

Once every chosen provider account was topped up, the real test began.

How they chose their model stack

The firm did not try to use one model for everything. After seeing the quality difference from premium access, they defined a practical multi-provider setup inside Intrascope.

Provider	Role in the workspace
OpenAI	Primary models for interpretation, drafting, and higher-value consulting answers
DeepSeek	Simpler everyday tasks such as email drafts, rewrites, and routine prompts
xAI	Image generation and secondary checks on selected answers

That split worked because automatic model orchestration routed each request to the right engine. Consultants did not need to study model benchmarks. The system matched task type to model tier so premium capacity was not wasted on email polish or simple rewrites.

For the broader multi-provider setup, see multi AI workspace for teams.

Adoption: cautious at first, then confident

After launch, employees started carefully. That is normal in consulting environments where accuracy matters.

Over the first weeks, usage expanded naturally as people saw better results:

• Understanding complex client and regulatory questions faster
• Preparing quicker first drafts for email replies
• Running verification passes through search-enabled models
• Using AI for editorial and publication-related work where image generation helped

This team was not trying to extract maximum tokens from every model. That is part of what makes the case interesting. They were normal business users adopting AI in a controlled way, and still getting strong value because orchestration kept quality high and waste low.

For a different team size and workflow, read how an 8-person marketing agency ran campaigns for $27 in one month.

First-month API usage: $13

Because the firm previously relied on free accounts, there was no meaningful baseline subscription cost to compare against. The real number that mattered was actual API usage after premium access went live.

Total first-month vendor spend: $13.

For a 50-person consulting organization with growing daily usage across departments, that is a striking result. It shows what happens when a team pays for usage instead of seats, especially when orchestration keeps lightweight work on efficient models.

The hypothetical subscription comparison

If the company had decided to give every employee at least one Plus or premium personal account, without choice, without governance, and without shared workspace structure, the monthly cost would have approached $1,000 at typical $20-per-seat pricing.

That figure is hypothetical. It assumes universal seat coverage the firm never wanted. But even a smaller slice of the team on personal premium plans would have cost far more than $13 while delivering less control, no shared manifests, and no usage visibility.

This is the core economic difference between consumer subscriptions and a governed API workspace. We explore it further in AI cost optimization for teams and why enterprises need Intrascope.

Built together: custom RAG for internal regulations

Because this was an enterprise pilot, the engagement went beyond standard workspace rollout. Together with the firm, we developed a dedicated RAG module for internal use.

• Connected through n8n automation workflows
• Vectorized the firm's existing regulations knowledge base in text format
• Source corpus size: more than 1GB of regulatory content
• Combined defined workspace context with an external knowledge source for answers

That changed what consultants could do in practice. Instead of relying only on prompt memory or isolated chats, they could ground answers in the firm's own regulatory material while still using premium models inside a governed workspace.

Shared context layers like manifests already help teams keep tone, rules, and project knowledge consistent. This RAG layer extended that idea into live internal knowledge retrieval, a step up for consulting quality and service delivery. Learn how manifests work in what is an AI Manifest in Intrascope.

Why this pilot matters

Free AI is not free when quality fails

The firm was already using AI, but free tools could not meet consulting standards. Premium access through a workspace closed that gap without forcing 50 separate subscriptions.

Enterprise rollout needs more than software access

API setup, model limits, user onboarding, and project structure matter. This pilot showed that guided enterprise onboarding removes friction that otherwise slows adoption for non-technical teams.

Orchestration multiplies value for normal users

You do not need power users to get strong ROI. When everyday consultants use premium models only where they matter, and lighter models handle email and routine work, savings add up fast.

Consulting firms benefit from governed knowledge

Between manifests, projects, orchestration, and RAG over internal regulations, the firm's consulting output moved to a higher level, with better control than scattered free accounts ever allowed. For governance principles, see AI governance for teams.

Key takeaways

• 50 employees across tax, financial, business, editorial, and leadership roles
• Moved from free ChatGPT and Gemini to a governed Intrascope workspace
• Intrascope's first enterprise pilot with full hands-on onboarding
• OpenAI for core consulting work, DeepSeek for simpler tasks, xAI for images and checks
• Cautious adoption that grew as answer quality improved
• First-month API usage totaled $13 across connected providers
• Hypothetical universal Plus-style seats would have cost around $1,000/month
• Custom RAG module built over 1GB+ of regulations via n8n

More customer stories are coming

This pilot is one of the first detailed success stories we are sharing from the field. More are on the way, including companies with heavier usage, larger teams, and different adoption paths.

If you want to follow what we publish next, keep an eye on the Intrascope blog or talk with us about an enterprise rollout for your team.

Conclusion

This consulting firm did not need AI to replace expertise. They needed premium models, structure, and internal knowledge access without sending 50 people to personal subscriptions or free tools with weak answers.

With Intrascope, they got their first real encounter with premium AI, enterprise-grade onboarding, orchestration that matched models to tasks, and a custom RAG layer over more than a gigabyte of regulations, all for $13 in month-one API usage.

Try Intrascope free for 7 days. No credit card required. Or book a call if you are planning an enterprise pilot for your consulting firm.