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LLM Integration & RAG Development Solutions for USA Businesses
Build secure LLM and RAG systems for business workflows with custom data pipelines, retrieval, evaluation, integrations, and production deployment.
Built around your operation
- USA businesses embedding AI into internal or customer workflows
- SaaS companies building AI-powered features
- Enterprises leveraging proprietary documents and knowledge bases
The business case
Improved productivity and automation
Controlled AI infrastructure costs
Higher user and stakeholder trust in AI systems
Results depend on scope, integrations and adoption.
The operating context
A clearer view of the whole operation.
Businesses are moving beyond standalone AI chat interfaces and looking for ways to connect large language models with internal knowledge, software systems, and operational workflows. Doing this effectively requires more than an API connection. Data ingestion, retrieval, access control, prompt orchestration, evaluation, integrations, and production infrastructure need to work together. PySquad builds LLM and RAG systems around specific business workflows so teams can apply AI to real operational use cases.
Where friction builds
Why LLM Projects Struggle in Production
Connecting an LLM directly to an application can produce impressive demos but often falls short with real business data. Poor retrieval, incomplete context, weak access controls, and inconsistent prompts can lead to inaccurate or irrelevant responses. Without proper evaluation, teams may also struggle to measure whether the system is actually improving the workflow. Production use introduces additional challenges around document updates, user permissions, latency, API usage, monitoring, and integration with existing software. Without a structured architecture, AI features can become difficult to control, expensive to operate, and hard for users to trust.
- 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
Inside the solution
The capabilities behind the operation.
Review the functional scope, then discuss the requirements specific to your team.
Custom RAG Architecture Design
Design retrieval pipelines that ground LLM responses in trusted business data.
Secure Data Ingestion and Indexing
Structured document processing, embeddings, and vector storage with access controls.
Prompt Orchestration and Guardrails
Controlled prompts, context windows, and safety mechanisms for reliable output.
Evaluation and Monitoring Frameworks
Measure accuracy, drift, latency, and cost with structured evaluation metrics.
Scalable Infrastructure Deployment
Production-ready architecture optimized for performance, reliability, and cost.
From requirements to implementation
Grounded in the way your team works.
How we work- 01
Start with a clear business workflow and outcome
- 02
Design retrieval and data layers before prompts
- 03
Validate outputs using structured evaluation
- 04
Scale only after reliability and governance are in place
Our approach
PySquad builds LLM solutions as connected application architectures rather than simple model integrations. We combine data ingestion, retrieval, embeddings, prompt orchestration, APIs, evaluation, monitoring, and access controls based on the workflow and information requirements of the business.
Make an informed decision
Is this the right fit?
The right solution starts with the right operating requirements.
Check the fit with usDesigned for
- 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
May not be suitable for
- 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
Trusted by clients worldwide
See it on your workflows
Discuss your requirementsWalk through the solution with your operation in mind.
Before you decide
Questions worth asking.
Ask something elseWhat is RAG and why is it important?
Retrieval-Augmented Generation connects LLMs to your own data sources so responses are grounded in real business knowledge rather than generic model memory.
Can this work with sensitive or proprietary data?
Yes. We design secure ingestion, role-based access, and isolation strategies to protect sensitive information.
How do you reduce hallucinations in LLM systems?
By combining structured retrieval, prompt controls, evaluation frameworks, and continuous monitoring.
Can this integrate with our existing software stack?
Absolutely. LLM and RAG systems are built to integrate with SaaS platforms, internal tools, CRMs, ERPs, and knowledge bases.
How long does it take to move from proof-of-concept to production?
Most focused LLM integrations move to production within a few months, depending on scope, data readiness, and complexity.
Start with your requirements
Let’s define your next step.
Tell us what needs to work better, the systems you use, and the scope you have in mind.
Discuss your requirementsShare your requirements through our enquiry form.A little closer, wherever you are
Big world.
Close partnership.
Good work travels. We bring product engineering, AI and Odoo ERP to the conversation, and make room for your way of working.
01 / BaseAhmedabadIndia, remote
02 / ApproachOne shared planDiscovery to delivery
03 / ConnectionBuilt around youAgreed meeting rhythm