Generative AI and Local LLM Implementation Support Service
Achieve secure in-house AI development with world-class technical capabilities and practical operational know-how.
Risks
By continuously relying on external APIs, three risks remain unaddressed
Confidential information leakage
Business data is sent externally
Uncapped usage-based billing
Cost structure that inflates with usage
Black box nature
Unable to understand its behavior, making accountability impossible
Benefits
Data leakage via API
$58.00
AI processing is completed within your private network. There is no path for business data to leave externally.
Cost Reduction
(3-Year Cumulative)
$58.00
Transition from usage-based billing to a fixed-cost model. Operational costs won't escalate even with increased usage.
Latency Improvement
(Max)
$58.00
Eliminates the need for communication with external APIs, with processing completed entirely within your internal network. This significantly speeds up response times.
Local LLM
Data sovereignty, low latency, and optimized costs
ChatGPT and external APIs
Convenient, but poses challenges in control and cost
Data Sovereignty
Self-contained within your network
Sent to external servers (vendor-managed black box; data may be used for training)
Usage-based billing (OpEx)
Fixed (CapEx)
Usage-based billing (OpEx)
Flexibility
Additional training possible with proprietary data
Limited
Algorithmization of tacit knowledge
QX Engine
Implements the 'art of dialogue that motivates people' from experts such as counselors and physical therapists, based on scientific evidence
General-purpose AI (e.g., ChatGPT, Gemini)
Smart insig Only responses based on general know-how; can only provide superficial responses
Learning from field data
QX Engine
Optimized for real data collected in the 'field' of nursing care and employment support
General-purpose AI (e.g., ChatGPT, Gemini)
Only general knowledge
API integration with existing systems
QX Engine
Can be implemented as an add-on to all touchpoints, including LINE, Slack, and proprietary apps.
General AI (ChatGPT, Gemini, etc.)
General-Purpose API Provision
Use Case
Finance, Insurance
Speed up regulatory response and compliance work by keeping customer information in-house.
Medical, Healthcare
Significantly reduce the administrative burden on healthcare professionals by keeping patient information within the facility.
Manufacturing, Construction
Accelerate on-site decision-making and response by keeping drawings and technical know-how internal.
Legal, Intellectual Property
Reduce man-hours for review tasks by keeping contracts and confidential documents internal.
Public Sector, Retail, Infrastructure
Protect customer data internally while enhancing the responsiveness of frontline staff.
Strengths
Designed to be a lasting company asset
Built on OSS and containers, allowing for long-term operation of developed systems without vendor lock-in.
Security x AI Expertise
Addressing AI-specific risks such as prompt injection, with measures like PII protection.
Hands-on Implementation Support
Total support from system construction to on-site training, establishing operational rules, and internal adoption.
Speaking at Meta US Headquarters' "Global Open Source Innovation Summit"
Quixotiks' Main Development Achievements
NTT East
Coaching App for Agriculture
Lenovo Japan
Employment Support for Persons with Disabilities
Many other successful implementations
Plans
Start in as little as 2 weeks, go live in 3 months
Plan 1
Strategic Consulting
Project Base
Duration
2-4 Weeks
Details
Challenge Assessment, Use Case Selection, ROI Estimation
Preparation
Organize Business Challenges
Outcomes
Visualize ROI
Plan 2
Build and Go Live
One-time
Duration
8-12 weeks
Details
Data preparation, model selection, infrastructure setup, PoC validation
Preparation
Provision of business data, involvement of frontline staff
Outcomes
Visualize ROI
Plan 3
Operation, maintenance, and improvement
Monthly subscription
Duration
Ongoing (monthly)
Details
Production operation, 24/7 monitoring, continuous accuracy improvement
Preparation
Assignment of operations personnel, sharing improvement requests
Outcomes
Continuously improve business fit
FAQ
Q. How much does it cost to implement a local LLM?
It varies depending on the implementation scale, use case, and hardware configuration. Assuming usage of 100 million tokens per month, the break-even point is reached in approximately 14 months compared to external APIs. We will provide an estimate tailored to your company's usage scale during a free individual consultation.
Q. How can we transition from our current ChatGPT usage?
Gradual migration is possible. We recommend starting with tasks where benefits are easily visible, such as internal document search or routine report generation, and gradually expanding the scope of application. The operational know-how accumulated with ChatGPT can also be utilized as an asset during migration.
Q. Which should I choose: on-premise or cloud GPU?
The decision depends on 'data sovereignty requirements,' 'initial investment tolerance,' and 'predictability of usage scale.' If a closed network operation is essential, then on-premise; if you want flexible scaling, then cloud GPU. We will propose the optimal configuration based on your requirements.
Q. Can we just try a PoC (Proof of Concept)?
Yes, it is possible. Our 2-4 week "Quick PoC Proposal" allows you to verify the model's response accuracy and business suitability using your company's data. You can evaluate the cost-effectiveness before full implementation, and it's also possible to conclude with just the PoC.
Q. Can we manage operations in-house after implementation?
Yes, it is possible. We will gradually transfer operational know-how in-house through prompt engineering training and support for establishing usage rules. We will accompany you with an operation and maintenance plan until you can operate independently.
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