01
Define the useful answer
We identify the user, source data, desired output, acceptable risk, and measurable quality target for the AI feature.
LaunchPad™ AI integration
Add useful generative AI to the software and knowledge your business already has.
LaunchPad™ helps teams integrate grounded AI assistants, semantic search, document workflows, and LLM-powered features into existing products and internal systems with security, citations, evaluation, and maintainability in mind.
Why this matters
A useful generative AI feature needs more than a chat box. It needs the right data, retrieval strategy, access controls, evaluation set, and production monitoring.
A generic chatbot cannot answer from current, permissioned company information
Your team is unsure whether the use case needs RAG, prompt design, or another approach
AI responses are uncited, inconsistent, slow, or too expensive for production
Security, tenant isolation, and data retention requirements are delaying launch
What is included
Practical deliverables for generative AI integration services.
Use-case, data-readiness, and risk assessment
Model and architecture evaluation based on quality, latency, and cost
RAG pipelines, retrieval, reranking, prompts, and citations where appropriate
Integration with your product, knowledge base, or internal workflow
Authentication, authorization, privacy, and logging controls
Automated evaluations, observability, documentation, and handoff
Delivery process
Built with clarity before complexity.
Each LaunchPad™ engagement starts with the business problem, then moves into the technical decisions required to make the build useful, maintainable, and easier to improve over time.
02
Select the integration path
We compare model, retrieval, hosting, privacy, latency, and cost tradeoffs instead of forcing every use case into the same architecture.
03
Build the grounded experience
We connect approved data, implement the user experience and backend, and add citations or source context when the use case benefits from them.
04
Evaluate and operate
We test answer quality, retrieval, safety, speed, and cost, then add monitoring and documentation for production use.
Common use cases
Where this service fits.
Internal knowledge assistantsCustomer support copilotsSemantic document searchDocument extraction and summariesAI features inside SaaS productsGrounded customer self-service
LaunchPad™ proof points
1,431+
Websites built
20+
Expert developers
FAQ
Questions buyers ask before starting.
What is generative AI integration?
Generative AI integration adds model-powered capabilities to an existing product, knowledge base, or workflow. Examples include grounded assistants, semantic search, document processing, drafting support, and application features powered through an LLM API.
What is RAG and when is it useful?
Retrieval-augmented generation, or RAG, retrieves relevant information from approved sources before a model answers. It can help when responses need current business context, source citations, or user-specific access to private knowledge.
Do we need to fine-tune an AI model?
Not always. Many projects should first evaluate prompt design, structured outputs, retrieval, and workflow changes. Fine-tuning is considered when the use case and test results show a clear reason for it.
Can generative AI be added to existing software?
Yes, when the current system has a workable integration path and the required data can be accessed securely. Discovery identifies the best way to add the capability without rebuilding more than necessary.
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Ready to talk through generative ai integration services?
Book a free LaunchPad™ software consultation and we will help clarify the right build path, whether you need a new system, an improvement, or dedicated development support.