AI Services
GenAI and LLM Integration
We build RAG pipelines, AI copilots, document intelligence, knowledge retrieval systems, fine-tuning workflows, and enterprise LLM integrations.
Why it matters
Put large language models to work inside your business.
LLM integration becomes valuable when it is connected to the right knowledge, workflow, interface, evaluation method, and business control. A chat box alone rarely solves the operational problem.
Solutyics designs GenAI systems around retrieval quality, model selection, prompt structure, privacy, latency, cost, guardrails, and user feedback. The goal is to make AI output useful, measurable, and safe enough for the context.
Primary intent
Integrate language models into business workflows and software products.
Fit
Where this work creates leverage.
Ideal for
- Companies building internal knowledge assistants
- Products adding AI copilots or document intelligence
- Teams automating content, support, research, or analysis workflows
- Organizations that need RAG or LLM integration in existing software
Typical use cases
- Internal knowledge base assistant
- Document extraction and summarization
- Customer support copilot
- AI workflow inside a SaaS product
Scope
What Solutyics delivers.
Deliverables
- LLM integration architecture
- Retrieval and vector-search pipeline
- Prompt, context, and structured-output design
- Evaluation set and quality checks
- Deployment, monitoring, and documentation
Considerations
- Retrieval quality often matters more than model choice
- Hallucination risk must be handled by design
- Data privacy and provider terms need review
- Cost and latency should be measured before scale
Outcomes
What should improve after the work.
Useful AI inside existing workflows
Better retrieval and answer quality
Clearer guardrails and evaluation
A maintainable GenAI integration
Process
How the work moves
Identify the workflow
We define the task, users, documents, expected outputs, risks, and success criteria.
Design retrieval and prompts
We plan data ingestion, chunking, vector search, context rules, structured outputs, and guardrails.
Build the integration
We implement the UI, APIs, model calls, retrieval pipeline, logging, and feedback loops.
Evaluate and tune
We test answer quality, hallucination risk, latency, cost, and operational monitoring.
FAQ
Questions that shape the work.
What is RAG and when do we need it? +
RAG, or retrieval-augmented generation, lets an LLM answer using your documents or data. It is useful when the model needs current, private, or domain-specific context.
Can you integrate with OpenAI, Anthropic, Gemini, or open-source models? +
Yes. Model choice depends on quality, privacy needs, latency, pricing, hosting requirements, and the type of outputs required.
How do you reduce hallucinations? +
We constrain retrieval, cite source context where appropriate, use structured outputs, add refusal behavior, evaluate against test cases, and design human review for sensitive workflows.
Can LLMs work with private company documents? +
Yes, but access permissions, storage, provider terms, retention, and security controls must be designed before private data is connected.
Do we need fine-tuning? +
Often not at first. Retrieval, prompt design, better data preparation, and evaluation usually come before fine-tuning. Fine-tuning is useful for specific output patterns or domain behavior.
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Next
Bring LLMs into the workflow with retrieval, evaluation, and control.
Share the documents, users, and desired outputs. We will help design the integration beyond the demo.