Mental BoundOverview
AI systems should solve real problems, not sit in demos. We build custom AI agents, retrieval-augmented generation (RAG) pipelines, and workflow automation that integrate into your existing stack and deliver measurable results. Whether you need document intelligence that understands your contracts, autonomous lead generation that qualifies prospects, or fine-tuned models trained on your domain, we focus on shipping solutions that scale.
Capabilities in Detail
Custom AI agents handle multi-step tasks—research, summarization, routing—with clear guardrails and human oversight. RAG systems connect your knowledge base to large language models so answers are grounded in your data, not generic training. Document intelligence extracts structured data from PDFs, contracts, and forms. Autonomous lead generation qualifies inbound leads, enriches data, and routes to sales—without manual triage. Workflow automation orchestrates tools, APIs, and human steps into repeatable processes.
Our Approach
We start with the problem, not the model. We map your workflows, identify where AI adds value, and design systems that fail gracefully. We prefer composable architectures: modular agents, pluggable retrievers, and clear separation between logic and data. We test with real inputs, measure latency and accuracy, and iterate until the system behaves reliably in production.
FAQs
How long does a typical RAG system take to build?
Most RAG implementations ship in 4–8 weeks, depending on data volume, chunking strategy, and integration depth. We prioritize a working prototype in the first 2 weeks.
Do you fine-tune models or use prompt engineering?
Both. For most use cases, prompt engineering and RAG deliver strong results without fine-tuning. We recommend fine-tuning when you have large, high-quality datasets and need consistent output formatting or domain-specific terminology.
How do you handle hallucinations?
We ground responses in retrieved context, add citation requirements, and use structured outputs where possible. We also design fallback paths when confidence is low.