
Make large language models useful inside real workflows. Built for accuracy, security, and scale.
See How We Build for Complex BusinessesMany USA businesses are experimenting with large language models, but few move beyond standalone chat interfaces. The real opportunity lies in embedding LLMs into business workflows using structured data, domain knowledge, and secure architectures. This solution focuses on production-grade LLM integration and RAG systems that deliver accurate, context-aware AI responses grounded in your business data.
We usually work best with teams who know building software is more than just shipping code.
USA businesses embedding AI into internal or customer workflows
SaaS companies building AI-powered features
Enterprises leveraging proprietary documents and knowledge bases
Product teams moving from AI proof-of-concept to production
Teams seeking basic chatbot templates
Businesses without structured or relevant data sources
Projects expecting AI accuracy without validation layers
Companies unwilling to manage AI governance and ownership
Businesses often connect an LLM API directly to their app without designing retrieval pipelines, data governance, or evaluation frameworks. The result is hallucinations, inconsistent answers, security concerns, and unpredictable costs. What works in a demo breaks under real user load and business risk.
Call LLM APIs directly from the application layer
Skip retrieval and rely only on prompt engineering
Ignore monitoring and evaluation frameworks
Scale usage without cost and latency planning
Hallucinated or inconsistent outputs
Exposure of sensitive business data
Uncontrolled API costs
Low trust in AI-generated responses
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Design retrieval pipelines that ground LLM responses in trusted business data.
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Structured document processing, embeddings, and vector storage with access controls.
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Controlled prompts, context windows, and safety mechanisms for reliable output.
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Measure accuracy, drift, latency, and cost with structured evaluation metrics.
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Production-ready architecture optimized for performance, reliability, and cost.
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We design LLM systems as layered architectures. Retrieval, embeddings, prompt orchestration, evaluation, and monitoring are structured together so AI outputs are grounded, auditable, and reliable.
Retrieval-Augmented Generation connects LLMs to your own data sources so responses are grounded in real business knowledge rather than generic model memory.
Yes. We design secure ingestion, role-based access, and isolation strategies to protect sensitive information.
By combining structured retrieval, prompt controls, evaluation frameworks, and continuous monitoring.
Absolutely. LLM and RAG systems are built to integrate with SaaS platforms, internal tools, CRMs, ERPs, and knowledge bases.
Most focused LLM integrations move to production within a few months, depending on scope, data readiness, and complexity.
PySquad works with businesses that have outgrown simple tools. We design and build digital operations systems for marketplace, marina, logistics, aviation, ERP-driven, and regulated environments where clarity, control, and long-term stability matter.
Our focus is simple: make complex operations easier to manage, more reliable to run, and strong enough to scale.
Integrated platforms and engineering capabilities aligned with this business area.
Share your details with us, and our team will get in touch within 24 hours to discuss your project and guide you through the next steps