We build retrieval pipelines using embeddings, chunking, summarization, metadata-based filtering, and hybrid vector search. Integrations include PDFs, Google Drive, Notion, websites, CRMs, SQL databases, and S3 buckets. Pipelines deliver grounded answers backed by citations instead of hallucinations, reducing support load and enabling self-service knowledge across teams and customers.

We build AI agents that not only answer questions but can perform tasks: updating CRMs, querying SQL, placing orders, creating tickets, summarizing dashboards, and triggering automation flows. LangChain tool runners and event-based orchestration allow models to safely call backend functions with sandboxing and approval gates.

We design applications that maintain long-term context, chain multiple LLMs together for accuracy, and combine text, vision, and tabular data. Whether it’s chat history, hierarchical memory, or long document references, your AI behaves consistently across sessions and reduces repeated queries or missing context problems.

Documents, tickets, emails, and reports are auto-ingested into vector stores using watchers, webhooks, and scheduled pipelines. Indexes rebuild incrementally, maintaining freshness without manual oversight. AI systems stay current on new policies, changes, and product releases without human data entry or intervention.

We architect systems so the LLM, embedding model, or vector store can be swapped without breakage. You gain ownership over architecture instead of being locked into OpenAI, Pinecone, or specific vendors. This lowers long-term cost and preserves flexibility as open-source and self-hosted models mature.

We add guardrails such as token redaction, access controls, human-in-the-loop validation, request logging, and model evaluation pipelines. AI actions become traceable. Sensitive data stays private. CIOs and CISOs get observability and compliance while teams continue innovating safely with emerging GenAI systems.