Service
Agentic AI & Retrieval-Augmented Generation
Build AI systems that retrieve information, reason through problems, and complete multi-step tasks, without waiting to be told what to do next.
Discuss Your Agent Use CaseAgentic AI & RAG Solutions
Most AI tools you've seen so far are glorified search bars. You type something, they respond, and you still have to do the actual work. Agentic AI works differently: it breaks down a goal, pulls in the right data from your systems, calls the tools it needs, and sees the task through without someone shepherding it at every step. At Katalyst AI Lab, we build these systems for production environments, not demos. We connect them directly to your existing APIs, databases, and applications, on whatever foundation model fits your stack.
Explainer
What Is an AI Agent?
An AI agent is software that can perceive its environment, decide what to do with it, reach for tools, APIs, databases, code interpreters, web search, and carry out a sequence of actions to get something done. No one feeds it instructions at every step.
Unlike a standard chatbot that handles one question and waits, an agent can map out a multi-step approach, run it, check whether it worked, and try a different route if it didn't. Think of it as the difference between a tool that responds and a system that operates.
Use agents when your task requires multiple steps, tool use, conditional logic, or coordination across data sources. A simple FAQ bot does not need agent architecture. A system that researches, drafts, and submits a report does.
Explainer
What Is RAG?
RAG (Retrieval-Augmented Generation) connects an LLM to your private documents or databases. Instead of relying on training-time knowledge, the model retrieves relevant content at query time and uses it to answer.
This means your AI can answer questions about internal policies, contracts, or product data, without that data being part of a public model's training & without retraining the model.
RAG is the right choice when your knowledge base is large, dynamic, or confidential. Fine-tuning is better when behaviour consistency is the goal. Many production systems use both.
Services
What We Build
| Service | Description |
|---|---|
| Multi-Agent Orchestration | Systems where multiple specialized agents work in concert. A planner delegates to a researcher, a writer, and a validator, each doing its job, coordinated through LangGraph, CrewAI, or a framework built to your requirements. |
| RAG Knowledge Assistants | Private document Q&A across your internal knowledge base, technical manuals, contracts, research archives. Chunk-level retrieval, semantic search, and source citations with exact page references, so your teams know where answers actually came from. |
| Tool-Use & Function Calling | Agents that reach into your systems: calling external APIs, querying SQL databases, running Python, searching the web, or interacting with your SaaS stack, Salesforce, SAP, Jira, SharePoint, and more. |
| Autonomous Workflow Agents | End-to-end task execution: the agent receives a goal, researches what it needs, drafts the output, reviews it, and delivers, with configurable human approval gates at the decision points that matter. |
| Conversational AI with Memory | Persistent memory across sessions, so agents recall prior conversations, user preferences, and context from past interactions, without you having to re-explain the background every time. |
| RAG System Evaluation & Optimisation | Assessment of existing RAG pipelines using RAGAS metrics: faithfulness, context precision, context recall, and answer relevance. Identifies exactly where retrieval is failing and where generation is going off-track. |
By Industry
Agent Use Cases
Legal
Contract review agent: ingests 300-page contracts, flags clauses that don't match your standard terms, cross-references the relevant regulatory requirements, and produces a structured risk summary your team can act on, in minutes, not days.
BFSI
Regulatory research agent: monitors RBI, FCA, and SEC circulars as they're published, indexes them into a searchable vector database, and surfaces the obligations that apply to a specific product or scenario when your compliance team asks.
Healthcare
Clinical document assistant: reads unstructured patient notes, retrieves matching clinical guidelines and drug-interaction records, suggests the right ICD-10 codes, and flags contradictions before they become a problem downstream.
Enterprise IT
Internal knowledge agent: indexes Confluence, Jira, Slack archives, and internal documentation into a single queryable layer. Your teams ask questions in plain language, instead of searching across five tools and coming up empty.
Technology
Our Stack
Production-grade tools we use to build, deploy, and evaluate agentic AI and RAG systems.
LangGraph
LangChain
LlamaIndex
CrewAI
AutoGen
Custom Python Agent Loops
Pinecone
Weaviate
Chroma
pgvector
Qdrant
Milvus
OpenAI
text-embedding-3-large
text-embedding-3-large
Cohere Embed
E5-large
BGE-M3
GPT-4o
Anthropic Claude 3.5/3 Opus
Meta
LLaMA 3
LLaMA 3
Mistral
7B/8x7B
7B/8x7B
Google Gemini
1.5 Pro
1.5 Pro
RAGAS
Giskard
Arize Phoenix
Custom Eval Harnesses
Select a category to explore our tooling
FAQ
Questions
What is an AI agent, in plain terms?
An AI agent is a system that can reason, use tools, and work through a task from start to finish without someone guiding it at every turn. Give it a goal and the right access like a database, an API, a document store and it figures out the steps, executes them, and delivers the result. It's not a smarter chatbot. It's a different category of software entirely.
What is RAG and do I need it?
RAG (Retrieval-Augmented Generation) connects an LLM to your private documents or databases. Instead of relying on training-time knowledge, the model retrieves relevant information at query time and uses it to answer. You need it whenever you want an AI to answer questions about internal data without that data being part of a public model's training.
Can you integrate agents with Salesforce, SAP, Jira?
That's the core of what we do. We build the connection layer between agents and your existing stack using function calling and custom tool definitions. If a system has an API, an agent can be built to work with it. We've done this with CRM platforms, ERP systems, project management tools, and internal databases, the integration work is a standard part of how we scope every build.
What Would Your Ideal Agent Do?
Tell us the process you want automated, even if it's still rough in your head. We'll scope an agentic architecture for you and show you where the leverage is. No commitment required.
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