Custom AI Brain with LLM Integration
Imagine having an AI brain custom-built for your business. We embed leading LLMs into your tools, making every process sharper, swifter, and more insightful.
What this work is measured on.
The outcomes engagements in this practice aim at, and how we track them.
We time how long drafting, analysis, or review takes your team today, then track the same tasks once the model assists, so speed is measured, not promised.
Models are tuned and tested against your own documents and terminology, and we report the evaluation results before anything ships.
We embed LLMs into the applications your team already uses, so adoption happens in your existing tools, not a new one.
We meter token spend and inference cost from the first deployment, and right-size models where a smaller one does the job.
WHERE THIS SITS // CWR90
This is Walk work: days 31-60 of the 90. The build phase, where the agent takes shape inside your systems.
What the engagement covers.
From strategy to implementation, every layer of the build is owned.
Model Selection & Optimization
Choose and fine-tune the optimal LLM for your specific use case, from GPT to Claude to open-source alternatives.
Custom Fine-Tuning
Train models on your specific data and domain knowledge for superior performance and accuracy.
API Integration
Integrate LLMs into your existing applications, tools, and workflows through robust APIs.
Prompt Engineering
Craft optimal prompts and templates that maximize model performance for your specific tasks.
Performance Monitoring
Track model performance, usage patterns, and costs with comprehensive monitoring and analytics.
Scalable Infrastructure
Deploy on cloud, on-premises, or hybrid environments with auto-scaling and load balancing.
Where this already runs.
Sector experience that shortens the path from scoping to shipping.
Content & Media
Content generation, editing, translation, and creative writing assistance
Legal Services
Contract analysis, legal research, and document generation
Healthcare
Medical documentation, clinical decision support, and patient communication
Education
Personalized learning content, assessment generation, and tutoring systems
Software Development
Code generation, documentation, testing, and development assistance
Customer Support
Intelligent chatbots, ticket routing, and response generation
PROCESS
How the work runs.
A fixed sequence with sign-off gates, so you always know where the engagement stands.
- 01
Requirements Analysis
Understand your specific use cases, data, and performance requirements
- 02
Model Selection
Choose the optimal LLM architecture and fine-tuning approach
- 03
Integration Development
Build robust APIs and integrations with your existing systems
- 04
Deployment & Optimization
Deploy to production and continuously optimize performance
FAQ // QUESTIONS
Frequently asked questions.
Direct answers about scope, timelines, and how delivery works.
Which LLM models do you work with?
We work with all major LLMs including GPT-4, Claude, Gemini, LLaMA, and open-source alternatives. We help you choose the best fit for your specific requirements and budget.
How do you ensure data privacy with LLM integration?
We implement multiple privacy safeguards including on-premises deployment, data encryption, access controls, and compliance with regulations like GDPR and HIPAA.
Can you fine-tune models on our specific data?
Yes, we offer comprehensive fine-tuning services using your proprietary data to create models that understand your domain-specific language and requirements.
What's the typical integration timeline?
It depends on the systems involved and the depth of integration, so we scope the timeline per engagement and write it down before work starts. A narrow proof of concept comes first, so you can validate the model on your own data early.
How do you handle model updates and maintenance?
We provide ongoing maintenance including model updates, performance monitoring, prompt optimization, and scaling support to ensure continued optimal performance.
What do you need from us to start an LLM integration project?
A concrete workflow the LLM should improve, sample data from that workflow, and access to the systems the model needs to read from or write to. One engaged stakeholder who can adjudicate quality is worth more than a large committee; LLM integration lives or dies on fast feedback about what a good answer looks like.
How do you stop an integrated LLM from making things up?
We constrain the model rather than trust it: retrieval grounds answers in your own data, output checks catch responses that cite nothing, and consequential actions keep a human sign-off. Where the model is unsure, it says so and escalates instead of improvising. No LLM integration removes wrong answers entirely, so we measure the error rate honestly and drive it down.
How do you measure whether LLM integration actually worked?
We agree a baseline before building: the time, cost, or error rate of the workflow as it runs today. After go-live the same number is tracked, and the comparison is the verdict. If the number does not move, we treat that as our problem to fix, not a rounding error to explain away.
Who owns the code and fine-tuned models afterwards?
You do. Integration code lands in your repositories, fine-tuned models and their training data belong to you, and everything runs in your own cloud and API accounts. Walking away from us costs you nothing technically.
How do you price custom LLM integration?
Through a scoped proposal after an engineering call. Build work is usually fixed-scope, ongoing tuning and support run as a retainer, and model API costs go through your own provider account so you see the real usage bill. We do not mark up tokens.
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