SaaS & Support
Customer support deflection
Deploy RAG assistants over help centers, product docs, tickets, and internal playbooks so customers get fast answers with visible source references.
Yeager helps teams in the US, UK, EU — and every country in between — turn messy knowledge bases into reliable AI assistants, RAG search, and workflow agents with clear source traces, practical guardrails, and deployment paths that respect your data.
Yeager presents AI as a business system, not a gimmick: clear outcomes, implementation discipline, privacy-aware architecture, and a buying experience that feels safe for founders, operators, and technical leaders.
Deploy RAG assistants over help centers, product docs, tickets, and internal playbooks so customers get fast answers with visible source references.
Give teams one trusted interface for SOPs, policies, onboarding docs, sales collateral, and technical manuals without forcing them to search across tools.
Build guarded agents that draft replies, route tickets, update CRM records, prepare reports, and escalate edge cases instead of taking unsafe actions.
Turn a vague AI idea into a scoped pilot with demo data, measurable outcomes, risk notes, and a roadmap your stakeholders can evaluate.
Each service is designed for real adoption: clean data ingestion, measurable answer quality, safer automation, and documentation your technical team can understand after launch.
Custom Retrieval-Augmented Generation architectures that connect your data safely to LLMs — with chunking strategies, hybrid search, and citations your team can inspect.
Context-aware customer service bots that answer from your approved knowledge base, follow your brand voice, and hand off to humans when confidence or permissions are not enough.
Guarded agents that execute multi-step workflows, use tools, and update systems with approval rules, logs, and clear boundaries around what automation is allowed to do.
The architecture is explainable by design. Your team can inspect what was indexed, what was retrieved, why an answer was generated, and when the system should escalate.
We connect sources like Confluence, Notion, Slack, Google Drive, help centers, PDFs, and internal APIs, then structure content so retrieval follows how your documents are actually written.
Documents become searchable embeddings in a vector database such as pgvector, Pinecone, or Qdrant, with keyword matching and metadata filters added when precision matters.
When a user asks a question, the system retrieves relevant chunks, ranks them, and assembles a grounded prompt with the context needed for a verifiable answer.
The model responds, takes an approved action, or escalates. Tool calls, handoffs, and low-confidence cases are designed to be logged rather than hidden.
Your data deserves clear boundaries. Yeager designs isolated, encrypted, auditable systems that can run in managed cloud, your VPC, or an on-premise environment depending on your requirements.
Architectures are planned around encryption in transit and at rest, with key management matched to your cloud or infrastructure policies.
Ship the entire stack to your own VPC or bare-metal infrastructure. Your data, your network, your perimeter.
Logging, access control, deployment records, and change tracking are designed so your team can prepare for formal compliance reviews.
Conversation logs and retrieved context can follow your retention policy, from short-lived debugging windows to longer audit storage.
Granular RBAC down to the document level — agents only retrieve what each user is authorized to see.
Every retrieval, generation, and tool call is logged with timestamps, inputs, and outputs — exportable to your SIEM.
Vinod Kumar leads Yeager with a focus on practical AI engineering: RAG systems, chatbot interfaces, automation workflows, FastAPI backends, and production deployment. The goal is simple: build AI products that are useful, explainable, and reliable enough for real businesses.
International clients need more than a flashy AI demo. They need clear scope, professional communication, secure handling of data, and delivery artifacts their team can review.
Discovery starts with your real data sources, user journeys, permissions, risk profile, and success criteria. The output is a scoped build plan, not vague AI promises.
RAG responses are designed to show source chunks, confidence behavior, and fallbacks. If the answer is not supported, the system should say so or escalate.
Delivery includes deployment notes, environment setup, API behavior, testing notes, and handoff documentation so the system remains understandable after launch.
Everything you need to know before booking a demo.
Share the business problem, data sources, expected users, integration needs, and timeline. Yeager will reply with a practical next step instead of a generic sales pitch.
Choose the category closest to your requirement. Yeager works with teams in every country, so please include your region of operation, compliance concerns, and preferred hosting model in the description.
For teams in any country, the first call focuses on your workflow, data sources, risks, and pilot outcome. If the fit is clear, Yeager can prepare a practical demo path using your sample data.