AI Solutions & Integration
RAG & AI-Workflows
- AI Solutions & Integration
- RAG & AI-Workflows
- Empower decisions with retrieval and automated workflows.
What we do?
Codot’s RAG & AI-Workflows service leverages Retrieval-Augmented Generation (RAG) alongside end-to-end AI workflow automation to deliver intelligent, context-aware solutions. We integrate vector databases and retrieval pipelines with LLMs to surface relevant data on demand, then embed those capabilities into automated workflows—such as intelligent document assistants, automated report generation, knowledge retrieval bots, and decision-support pipelines. Our approach covers data ingestion, indexing, prompt engineering, workflow orchestration, deployment, and continuous optimization, ensuring robust, scalable, and secure RAG-driven automation tailored to your business processes.
Outcomes You Can Expect
- Intelligent Retrieval Services: Production-ready RAG endpoints delivering contextually accurate information from your data sources on demand.
- Automated AI Workflows: Seamless pipelines that trigger retrieval, reasoning, and subsequent actions—reducing manual effort and speeding decision cycles.
- Scalable Infrastructure: Robust vector database deployments and orchestration engines that grow with data volume and workflow complexity.
- Improved Decision-Making: Faster, data-grounded insights delivered via automated reports, assistants, or dashboard integrations.
- Enhanced Security & Compliance: Secure handling of sensitive data in retrieval and automation processes, with audit trails and governance controls in place.
- Continuous Evolution: Ongoing monitoring and iterative refinement ensure retrieval accuracy and workflow effectiveness remain high as requirements change.
Why Choose Codot?
- RAG Expertise: Deep experience setting up vector stores, retrieval pipelines, and LLM integration to deliver reliable, context-rich outputs.
- End-to-End Workflow Automation: We design, build, and deploy AI-driven pipelines that combine retrieval, reasoning, and action steps into seamless processes.
- Secure Data Handling: Implement secure indexing, access controls, and encryption between your data sources, vector DBs, and LLM services to protect sensitive information.
- Custom Orchestration: Craft tailored workflow engines that trigger RAG operations based on events, schedules, or user interactions, ensuring timely and relevant automation.
- Monitoring & Optimization: Provide dashboards and logging for retrieval accuracy, latency, and workflow success rates; iterate on embeddings and orchestration logic continuously.
- Flexible Deployment: Support cloud-native, on-prem, or hybrid setups according to your compliance and performance requirements, with infrastructure as code for reproducibility.
Engagement Workflow
- Discovery & Data Assessment: Review data sources, documents, and knowledge repositories; identify high-value retrieval and automation use cases; define success metrics.
- RAG Architecture & Prototype: Design vector database schema, ingestion pipelines, and retrieval workflows; build a proof-of-concept RAG assistant or automation module for validation.
- Workflow Orchestration Design: Map end-to-end processes—how retrieval outputs feed into automated steps (e.g., notifications, report creation, API calls); select orchestration framework or custom engine.
- Implementation & Integration: Set up vector DB (Pinecone/Qdrant/Weaviate), ingestion pipelines, LLM prompts; develop automation scripts or services to consume retrieval results and perform actions; integrate with existing systems via APIs or connectors.
- Testing & Validation: Conduct retrieval accuracy tests, end-to-end workflow simulations, and security assessments; refine embeddings, prompts, and orchestration logic based on feedback.
- Deployment & Monitoring: Deploy components (ingestion, retrieval service, orchestration engine) in production; configure CI/CD pipelines, monitoring dashboards, and alerting for performance, errors, and data drift.
- Iteration & Maintenance: Analyze usage and retrieval metrics, tune vector indexes, update prompts, and extend workflows with new RAG-driven automations according to evolving business needs.