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Businesses wanting to leverage AI need private, grounded models connected directly to their private documents and databases with strict guardrails.
We build production-grade AI applications integrating modern LLMs (OpenAI, Claude) with vector search (pgvector, Pinecone). Our RAG architectures ground AI responses in your verified company data with zero data leakage.
Clear, verifiable technical milestones handed over with complete source code.
Full application with prompt engineering, guardrails, and validation.
Semantic search and document indexing pipeline.
Accurate information extraction with strict hallucination controls.
Predictable bi-weekly sprint milestones with staging links and live reviews.
Evaluate use cases, data sources, and model selection.
Index documents and configure vector retrieval.
Build interface, integration hooks, and moderation.
Benchmark accuracy, deploy cloud API, and handover.
Explore in-depth articles, case breakdowns, and implementation lessons related to AI Application Development.
A technical evaluation between off-the-shelf AI assistants (ChatGPT Team, Copilot) versus custom agentic RAG architectures integrated with internal company data.
A pragmatic engineering guide on identifying high-ROI operational bottlenecks, selecting between deterministic workflows and LLM reasoning, and avoiding expensive automation pitfalls.
Key questions regarding our AI Application Development engineering engagements.