Embedded Files
The main question of consulting:
> **Price must be tied to value, not to hours spent**
If a business saves $10k+ in the first months, and we charge $2k/month — this is not a service, this is a **gift**. And clients sense it, and paradoxically — they value it less.
---
## Our Pricing for This Service
### Model 1: Project + Retainer (the healthiest)
**Phase 1: Turnkey Project** — fixed price
- Audit of current development processes
- AI agent architecture tailored to the project
- Setup of `agents.md`, skills, integrations
- Team training (2–4 sessions)
- Pilot launch on 1–2 tasks
- Documentation
Range: **$8k–$25k** depending on team size and stack complexity.
**Phase 2: Support** — monthly retainer
- Adapting the agent for new tasks
- Resolving issues that arise in production
- New skills on request
- Team consultations
Range: **$2k–$8k/month**.
This is a fair model: the client sees **concrete results for concrete money**, and we get predictable income.
---
### Model 2: Value-based pricing (we know how to calculate ROI)
We calculate together with the client:
**What AI automation saves:**
- Developer time on routine tasks (reviews, documentation, information retrieval)
- Time for onboarding new developers
- Reduced bug count due to standardization
- Faster time-to-market
- Reduced bus factor (if knowledge lives in the agent, not in people's heads)
**Example calculation:**
- 5 developers × 160 hours × $80/hour = $64k/month payroll
- AI agent saves 15% of time = **$9.6k/month in savings**
- Per year = **$115k in savings**
Then a fair price for the turnkey project is **10–20% of annual savings**, i.e. **$12k–$25k** one-time.
And a $3–5k/month retainer for support.
---
### Model 3: Performance-based (risky, but interesting)
We charge **$0 for setup**, but take a **% of proven savings** over 6–12 months.
Example: 20% of documented savings × 12 months.
Pros:
- The client takes no risk
- We are motivated to do well
- Easy entry for the client
Cons:
- Requires a clear mechanism for measuring savings
- The client might "not notice" the savings
- We work for free during the first months
This model only works with **mature clients** who know how to track metrics.
---
## Key Pricing Rules
### 1. We never sell hours
Hours are a commodity. Value is the result.
❌ "$150/hour for AI setup"
✅ "$15k for an AI agent that saves the team 20 hours per week"
### 2. We always show ROI
The client is buying not a "configured agent" but **$100k+ in annual savings**.
### 3. We separate project from support
The project is capital expenditure (CapEx) — easier to get approved.
Support is operating expenditure (OpEx) — comes from a different budget.
---
## Why $2k/month Is a Mistake
We will not work for $2k/month turnkey. And here's why:
1. **It devalues the service**
If you charge too little, the client thinks it's "just prompts" rather than engineering work. Fast doesn't mean simple. High speed and high price — that's the price of accumulated expertise in AI implementation.
2. **It attracts the wrong clients**
For $2k you get people who don't value results and will squeeze the maximum out of you for the minimum.
3. **It doesn't scale**
We physically won't have time for many such clients.
4. **It leaves no room for growth**
No margin for product improvement, research, or team building.
---
## Positioning
We are not "AI consultants." We are:
> **AI-powered development automation engineers**
Or more precisely:
> **AI agent architects for engineering teams**
We deliberately filter out those who want "AI to write code for $50" and attract those who understand the value of an engineering approach.
In reality, our agents run perfectly on a subscription of $20–50 per month. Thanks to optimizations, precise instructions, and an orientation toward using agents as the primary executor — with a human acting as a task progress controller and result evaluator — we manage to avoid burning tokens and hitting limits due to AI loops during task execution.
---
## Client Quality Filter
### Why an innovation cushion is a must-have
A business without a reserve for development is not a business — it's **day-to-day survival**. Such companies:
- have no room for error;
- have no time for experiments;
- any implementation = stress and resistance;
- first difficulty → "turn it off, it doesn't work";
- decisions are made out of fear, not strategy.
Implementing AI automation in such an environment is like doing major renovations in a rented apartment that could be taken away tomorrow.
---
## Price as a Filter
A high price is **not about greed but about qualification** — honest yet fair:
1. **We filter out the deadbeats right away.**
If a client winces at $15k for a project that saves $100k a year — they're not our client. They're looking for a magic button for $200 (the price of a top AI subscription).
2. **We attract serious players.**
Business with a cushion understands: investing in infrastructure is not an expense — it's capitalization.
3. **We protect ourselves from burnout.**
Cheap clients are the most demanding. Expensive ones respect other people's time and expertise.
4. **We set the right expectations.**
For $20k the client expects results. For $2k — they expect miracles and will be dissatisfied with any outcome.
---
## Qualification Questions for the First Meeting
To avoid wasting time on "deadbeats," we ask these questions:
### Financial
- What budget do you allocate for development automation this year?
- Is there a separate line item for innovation and tools?
- How do you measure ROI from process improvements?
### Organizational
- Who makes decisions about adopting new tools?
- How do pilot projects typically go at your company?
- Is the team's time dedicated to learning new things?
### Strategic
- What are the engineering team's goals for the next 12 months?
- What happens if we don't implement automation — what are the losses?
- What is your plan for scaling the team?
If the client answers most questions vaguely or with "we haven't thought about that" — **that's a red flag**.
In this case, a business and business process audit is necessary to find answers to these questions before discussing AI implementation.
---
## Meta-thinking: What the Absence of a Cushion Tells Us
If a business has no money for innovation, it's usually a symptom of deeper problems:
- **Poor unit economics** (the business breaks even or operates at a loss);
- **Weak management** (can't plan and set aside reserves);
- **Market issues** (losing clients, can't raise prices);
- **Cash flow gaps** (living from payment to payment).
Coming into such a business with your AI agent and automation, you become **the one to blame for all their problems**. They'll look for someone to blame when it "doesn't take off," and the blame always falls on whoever proposed the change.
---
## Our Position in One Sentence
> I work with businesses that know how to invest in their future. I don't work with those who are just surviving today and thinking about tomorrow — they don't have automation on their mind.
This is not snobbery. This is **professional hygiene**.
---
## Practical Advice
A clear section **"Who this is NOT for"**:
❌ You have no budget for tooling development
❌ You're looking for a "quick cheap solution"
❌ Your team resists any changes
❌ You're not ready to allocate time for implementation
❌ You have no development efficiency metrics
This **increases conversion** among the right clients and **saves us dozens of hours** on empty negotiations.
---
Page updated
Google Sites
Report abuse