Workflow value
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.
AI Services
AI Services service track
GenAI and LLM Integration
We build RAG pipelines, AI copilots, document intelligence, knowledge retrieval systems, fine-tuning workflows, and enterprise LLM integrations.
Approach
Put large language models to work inside your business.
The work is structured around explicit decisions and usable outputs rather than a generic delivery template.
Controlled integration
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.
Fit
Where this creates leverage
The strongest engagements have a clear operating constraint, decision, workflow, or delivery risk to improve.
Best fit
Conditions that make the work valuable
- 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
Situations the service can address
- Internal knowledge base assistant
- Document extraction and summarization
- Customer support copilot
- AI workflow inside a SaaS product
Deliverables
What Solutyics actually delivers
Each workstream is labelled for the outcome or artifact it is responsible for, not its position in a template.
LLM architecture
LLM integration architecture
Retrieval pipeline
Retrieval and vector-search pipeline
Prompt and output design
Prompt, context, and structured-output design
Quality evaluation
Evaluation set and quality checks
Operational monitoring
Deployment, monitoring, and documentation
Process
How the work moves
A visible sequence of decisions, working outputs, review points, and handover, rather than a black-box delivery cycle.
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.
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
FAQ
Questions that shape the work
The answers below clarify scope, collaboration, ownership, and the conditions that usually affect delivery.
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 step
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.