AI-Native IT Department
15 developers. 3 months. 8× cheaper.
A classic team of 15 people delivers a product in 18 months for $5.95M.
An AI-native team of 3 people delivers the same product in 3 months.
Their FTE: $252K instead of $5.95M. Our service: $300K.
Total: ~$560K instead of $5.95M.
Classic development is killing your budget
Do the math. An average large product (CRM, B2B platform, mobile banking) requires:
Verdict: the classic development model is no longer effective. The market demands speed and savings it cannot deliver.
AI-native team: 3 people, 3 months
We're not saying "fire 15 people." We're saying change the approach.
1. Senior AI Architect
Team Lead. Assigns tasks to the agent, designs architecture, controls code security, develops complex modules himself. Not a "manager" but a lead engineer.
2. AI Developer (×2)
Full-stack operator. Manages the coding agent, conducts code review, orchestrates deployment and auto-testing, writes documentation.
Result: the same product, 8× cheaper, 6× faster.
The numbers speak for themselves — $5.39M savings
| Parameter | Traditional Approach | AI-Native Approach |
|---|---|---|
| Team size | 15 people | 3 people |
| Development time | 18 months | 3 months |
| FTE cost (their payroll) | $5,940,000 | $252,000 |
| Our service (deployment + training + mentoring) | — | $300,000 |
| Server / GPU (rental) | — | $8,000 |
| Total | ~$5,950,000 | ~$560,000 |
| Savings | ~$5,390,000 |
Important: $300,000 is our service fee. The client's own team's payroll ($252,000 for 3 months) is paid by the client as before — the difference is that there are 5× fewer people and the product is ready 6× faster.
Even when purchasing your own server for $70,000 — you still save ~$5.3M.
European market (EUR)
| Parameter | Traditional Approach | AI-Native Approach |
|---|---|---|
| Team size | 15 people | 3 people |
| Development time | 18 months | 3 months |
| Their payroll (salaries + taxes) | €2,700,000 | €117,000 |
| Our service (deployment + training + mentoring) | — | €150,000 |
| Server / GPU (rental) | — | €4,500 |
| Total | ~€2,705,000 | ~€271,500 |
| Savings | ~€2,433,500 |
New paradigm — no classic division of labor
In an AI-native team, there is no "architect who doesn't write code" and "developer who doesn't think about architecture." Everyone does both.
1. Senior AI Architect (Team Lead)
Assigns tasks to the agent, designs architecture, controls code security, develops complex modules. Not a "manager" but a lead engineer.
2. AI Developer (Full-stack Operator)
Manages the coding agent, conducts code review, orchestrates deployment and auto-testing, writes documentation.
Key: everyone is interchangeable. No situation of "John quit — frontend is stuck."
Works with any codebase
"We have a 10-year-old Java monolith, 500K lines" — not a problem. TAO·CODER doesn't require rewriting legacy. It works with existing code, learns it through bounded context and pipeline, and integrates into your current stack without downtime.
We're not saying "throw it all away and rewrite." We're saying make your existing team 20× more effective.
Your data never leaves your perimeter
Zero telemetry
TAO·CODER does not collect logs, code, or prompts — everything stays local.
On-premise LLM
DeepSeek V4 deploys on your servers or in a secure cloud.
Compliance-ready
Architecture ready for SOC2, ISO 27001, HIPAA — pass audits without rework.
Open-source models
No vendor lock-in, full technological sovereignty.
Hardware options
4 months to first product
Audit & team selection
Ready plan, 3 people selected
Infrastructure
LLM and TAO·CODER running on your servers
Training & pilot
First product from idea to production
We stand by the results
| Risk | Solution |
|---|---|
| AI hallucinations | Senior architect reviews every PR, validates tests, and approves deployment. AI generates code but never ships to production without human sign-off. All product decisions — only by humans. |
| Data security | Open-source models + closed perimeter. Leakage excluded by architecture — zero telemetry, on-premise inference. |
| Employee resistance | Salary increase to market level for AI specialists. Retraining — voluntary. |
| LLM provider failure | 2–3 providers in hot standby. Switchover — minutes. |
| Not working? | First product in production — or we work for free until it is. |
Transparent pricing — no hidden costs
(taxes included, no VAT) — our service fee
Included in the service:
- Deploy LLM and TAO·CODER on client infrastructure
- Train team on methodology (Action Learning)
- Senior mentor supervision during pilot
- First product in production under our management
Client pays separately:
- Payroll of 3 team members: ~$252,000 for 3 months (with taxes)
- Hardware: server purchase $50–70K or GPU rental $2K–3K/month
| Item | Amount |
|---|---|
| Payroll 3 people × 3 months (paid by client) | $252,000 |
| Our service (deployment + training + mentorship) | $300,000 |
| Hardware (optional, rental) | from $8,000 |
| Total | ~$560,000 |
| Traditional approach (15 people × 18 months) | $5,950,000 |
| Savings | ~$5,390,000 |
European pricing
(with taxes) — our service fee
Total client cost: ~€271,500 (payroll €117,000 + our service €150,000 + hardware from €4,500)
Savings: ~€2,433,500 vs traditional approach at €2,705,000
Competitors are already deploying
AI-native teams
If your CTO hasn't yet proposed this plan — maybe they just haven't had time to study it. Send them this page. Or write to us — we'll send a presentation for your board of directors.
Every month of delay costs $330K in lost savings. Your competitors aren't waiting.