Intelligent Agents. Built for Results.
LLM-Integrated Applications
We build LLM-integrated apps that combine natural language reasoning, vector search, and enterprise data to unlock new internal tools, automate decisions, and power smarter workflows — all with production-grade reliability.
CALL US: +1 (773) 759-8300
What We Build
Beyond chat. These are fully functional AI-powered applications.
Our LLM-integrated apps are designed to serve as intelligent internal tools and business-facing systems, connecting large language models to your proprietary data, APIs, and workflows. Built using LangChain, LlamaIndex, LangGraph, and RAG architectures, these apps turn LLMs from general models into domain experts.
What we build:
- Internal-facing copilots for marketing, product, sales, and ops teams
- Customer-facing tools with contextual responses powered by vector search
- Workflow-aware GPT dashboards and RAG-backed applications
- Secure apps with access controls, token limits, and traceability
- Systems that combine retrieval, generation, and tool use — not just one-shot prompts
Whether you need a Notion-integrated team assistant, a secure document explainer, or a fully interactive RAG pipeline, we specialize in building LLM-native applications that work the way your people do — fast, flexible, and focused.
Domain-Tuned, Workflow-Ready
Every app is shaped around your team’s real workflows and tightly integrated with your stack. Here’s what we’ve built for clients across industries:
eCommerce & DTC
RAG-Powered FAQ Tools
Answer complex customer queries using a combination of product manuals, policy docs, and past tickets all without hallucination.
Internal Product Lookup Apps
Let CX teams pull specs, pricing, and shipping info instantly with a search-driven, chat-based interface.
Sales & Promotion Assistants
Enable staff to generate campaign content, summarize promos, and translate across platforms with a single prompt.
Real Estate & Lending
Property Intelligence Dashboards
Combine listing metadata, CRM records, and external APIs to power natural language search across inventory.
Document Summarizers
Parse PDFs, extract key data, and answer questions about appraisals, income proofs, or property history.
LLM-Driven Loan Explainers
Help borrowers understand terms, flag missing inputs, and simulate common “what if” scenarios — in plain English.
Marketing & Agencies
Campaign Brief Assistants
Turn messy stakeholder input into clean creative briefs with prompt-tuned, role-aware generation tools.
Performance Breakdown Bots
Enable account teams to get on-demand explanations of CTR drops, CPA spikes, or conversion changes across clients.
RAG-Powered Research Tools
Search industry whitepapers, past campaign data, and proprietary decks in natural language powered by retrieval.
Internal Ops & SaaS
Product & Docs Copilots
Let team members query release notes, changelogs, and help articles without hunting through Confluence or Notion.
Slack + GDrive Q&A Tools
Ask one question get the answer from the most relevant doc, file, or message across all your internal systems.
RAG Analytics Explainers
Use LLMs to explain trends in dashboards, surface key anomalies, and generate next steps from raw data.
Frequently Asked Questions
What’s the difference between an LLM agent and an LLM-integrated application?
Agents are task-oriented entities designed to take actions. LLM apps are broader — full software systems that embed LLMs for reasoning, answering, or generating inside larger workflows. Many include RAG, APIs, UI, and user access controls.
Can you build both internal tools and customer-facing apps?
Yes. We’ve built LLM-powered internal dashboards for ops and sales teams, as well as external-facing support tools, campaign builders, and explainers.
What is RAG and why is it important in AI apps?
RAG (Retrieval-Augmented Generation) combines external or proprietary data with LLM output. Instead of guessing, the model retrieves relevant information first — ensuring more accurate, grounded, and domain-specific responses.
How do you ensure reliability and safety in these applications?
We include access control, rate limiting, guardrails, fallback flows, and observability. We also support LangSmith, OpenAI logs, and structured evals using DeepEval and Ragas.
What kind of tech stack do you use to build these apps?
We use LangChain, LlamaIndex, LangGraph, CrewAI, FastAPI, Pinecone, pgvector, and Ollama — plus GCP or AWS for cloud hosting. All apps are modular, maintainable, and secure.
Can we start small and scale up later?
Absolutely. We often start with a slim proof-of-concept and grow the feature set over time — turning a chatbot into a workflow copilot, then into a full decision-support app.
Do you support multi-user access or authentication?
Yes. We can integrate with your auth systems (OAuth, SSO, etc.) and create user-based memory, rate limits, and permissions as needed.
Ready to Build Your Own AI Application?
We’ll help you scope, design, and ship a custom LLM-powered app that fits your stack, speaks your data, and drives real productivity.