200万ドルの疑問

Stop paying AI to read.
Let it ask SMART TS XL.
Cut your bill.

Your AI already reads your code. With SMART TS XL it reads the answer, not the whole repository.

20%conservative savings
40%平均貯蓄
60%以上高い節約
実際の例
A major bank we met spends $1 billion every six months on AI code analysis.

Even a conservative 20% saves $200 million every six months. Large banks can save hundreds of millions of dollars a year.

On every $1 billion of AI code-analysis spend

保守派(20%)$200M
平均(40%)$400M
高(60%以上)600億ドル以上
Why the bill drops

Same AI. Same questions. Smaller bill.

Every question your AI answers about your code costs money. The difference is how much code it has to read to get there.

WITHOUT SMART TS XL

The AI reads, and reads again

  • Searches raw files and re-reads code for every question
  • Wades through noise: one plain text search for a common COBOL word returned 61,417 matches
  • Rebuilds the same understanding in every session, and pays for it every time
WITH SMART TS XL

The AI asks, and gets the answer

  • Gets the exact lines and relationships, already analyzed
  • 1 SMART TS XL search covered 131,212 documents in 0.31 seconds
  • Works with ChatGPT, Claude, Copilot, Gemini and Grok. Your code stays on your servers
LLM + MCP + SMART TS XL
UCCA現代美術センターがアップロードしたYouTubeで展覧会を見る

See how the savings happen

A 2.5-minute explainer, and a one-minute film about the AI bill.

200万ドルの疑問 (2:29): what happens when the AI calls SMART TS XL through MCP.
The AI Bill (0:50): Linda and Tom find out why the bill is so big.
What would you save?

See your AI bill shrink.

Pick a company size or move the sliders.

試してみてください。
The bigger your code, the more the AI has to read: more automated jobs running, and up to 2x the spend per developer.
Analysis, impact studies and code conversion, about $1,400 per job per day.
Your AI bill per year
Your yearly savings with SMART TS XL
保守派20%
平均40%
ハイ60%を超えます
Over 5 years, at the average level
See what you’d save.Book your live session.
Estimates. Your savings depend on your code, your AI tools and how your teams use them. We’ll measure them on your own code in a live session.
Real questions, real results

私たちのテストでは

Real prompts run on IN-COM’s demo system, with SMART TS XL and without it.

Oct 18 · 4 real prompts

Overnight test

自律的AI SMART TS XL$5.15 · 13 min

All 4 finished.

無し2 of 4 gave up

Finishing: an estimated $295–$538 and about 13 hours.

Oct 17 · complex impact question

One hard question

自律的AI SMART TS XL$0.97 · 3.5 min

Done, with complexity and cross-reference reports.

無し諦めた

Stopped after 4 of 529 files.

Oct 17 · search speed

131,212ドキュメント

自律的AI SMART TS XL0.31 seconds

One search, exact lines returned.

無しFile by file

The AI opens and reads files one at a time.

Oct 17 · noise test

One common COBOL word

自律的AI SMART TS XLExact lines

Only the lines asked for.

無し61,417の一致

From a plain text search, for the AI to read through.

Try your own question

What would you ask your code?

Type a question your teams ask today. We’ll show the closest real example, and run yours on real code in a live session.

Send me my question’s report
Opens an email to IN-COM with your question. Or book your live session and bring it with you.

Stop paying AI to read. Let it ask SMART TS XL. Cut your bill.

See what you’d save, measured on your own code.

Sources for AI spend levels (2026, publicly reported): Typical: Anthropic, Claude Code cost documentation, average about $13 per developer per active day, $150–250 per month (code.claude.com). Heavy: Uber and Microsoft power users at $500–2,000 per engineer per month; Uber cap of $1,500 per engineer, per month, per tool (フォーブス, Unite.AI). Agentic: top 1% of AI-adopting companies at about $7,500 per employee per month, Ramp AI Index (Unite.AI); Meta about $50,000 per employee per year at list prices, SemiAnalysis (暗号ブリーフィング). Frontier: StrongDM guideline of $1,000 per engineer per day; 16 parallel agents at about $20,000 over two weeks, about $1,400 per agent-day, used for automated AI jobs (The Emerging Token Gap). Major bank: an IN-COM conversation with a bank spending about $1 billion every six months on AI code analysis; the bank is not named. Lines-of-code effects (up to 2x per-developer spend; about 2.8 automated AI jobs per million lines, calibrated to the bank example) and savings levels (20% / 40% / over 60%) are IN-COM estimates. Test results are from IN-COM’s demo system. All figures are estimates for illustration only, not a quote or guarantee; actual costs and savings vary by organization, tools, models and usage.