Let us handle it
Custom AI Agent Development
Indext Data Lab provides autonomous, goal-oriented software systems designed to execute complex workflows. We build custom AI agents using RAG, tool-use (function calling), and multi-agent orchestration to automate cognitive tasks, reduce operational overhead, and integrate seamlessly with existing enterprise software ecosystems
100% Job Success
Expert-Vetted
Top-Rated Plus
100% Job Success
Expert-Vetted
Top-Rated Plus
100% Job Success
Expert-Vetted
Top-Rated Plus
100% Job Success
Expert-Vetted
Top-Rated Plus
Do you need a custom AI agent?
Unlike standard chatbots, these agents function as digital team members that perceive environments, make decisions, and perform actions to achieve specific business objectives.
Custom AI agents are autonomous software entities designed to execute complex, multi-step workflows by reasoning through tasks, utilizing external tools, and integrating with enterprise data. The primary value of custom AI agents lies in the transition from
Human-in-the-loop to Human-on-the-loop. This shift allows your specialized talent to focus on strategy while agents handle the tactical execution.
Human-in-the-loop to Human-on-the-loop. This shift allows your specialized talent to focus on strategy while agents handle the tactical execution.
What can it do for your business?
90% Reduction in Latency
Agents process data and trigger actions in milliseconds, operating 24/7
Scalability without Headcount
Increase output volume by deploying additional agent instances rather than hiring and training new staff
Zero-Defect Data Entry
Eliminating human fatigue leads to 100% accuracy in structured data extraction and migration tasks
Most agents remain wrappers
Most AI deployments are thin interfaces masking a lack of structural depth. They offer no moat. They provide no leverage. Architecture is the only defense against noise. These "wrappers" only mimic agency. Indext Data Lab builds modular cognitive architectures.
Architecture over ambiguity
Fragmented AI systems fail during multi-step tasks. Intelligence requires a unified framework to process, remember, and execute. We deploy a recursive stack to ensure consistency.
-
Reasoning Engine
High-parameter models serve as logic processors. Rigid system constraints ensure high signal. This turns raw AI power into a predictable tool. -
Memory Management
This uses vector embeddings (mathematical representations of data) to archive experience. Agents retrieve specific data points from past interactions. This transforms temporary sessions into a persistent knowledge base. -
Tool Augmentation
This utilizes hybrid architecture (the integration of diverse software models) to link reasoning with action. Agents execute code and query databases within secure silos. They perform tasks. -
Orchestration Layer
This employs stateful orchestration (the management of a continuous process state) to handle complex handoffs. Frameworks like LangGraph allow the system to verify its own logic.
"More AI" isn't better
When we rely too much on automation, systems start producing “noise”—extra information and mistakes that pile up over time. These small errors add up and slowly reduce quality. This is what we call hallucination debt.
Our algorithm is simple
Selection
We use the smallest and simplest system that can get the job done. This makes everything faster, cheaper, and more reliable.
Framing
We feed systems only the information they truly need. Less clutter means clearer thinking and better results.
Audit
We regularly review how decisions are made. This helps us catch problems early and keep things on track.
Most companies have one common problem
Tasks that require too much judgment for old software, but are too repetitive for expensive talent. Our workflows solve this.
Instead of a human reading news for 4 hours, agents use time-series analysis to track price shifts over months. They ignore the hype and synthesize the signal into a one-page brief
The agent monitors your supply chain. It identifies a bottleneck (like a late shipment), calculates the impact, and automatically adjusts inventory levels without a human ever touching a keyboard
Using Retrieval-Augmented Generation (RAG), the agent "reads" your technical docs and "executes" diagnostic scripts simultaneously. It doesn't tell you there's an error; it tells you why it happened and how it fixed it
How we deploy
Discovery & Process Mapping
We identify high-friction nodes in your current workflow where cognitive labor is wasted on repetitive tasks
Agent Persona & Tool Definition
Defining exactly what the agent can see (data sources) and what it can do (API permissions)
Prototype & Evaluation (RAGAS)
We test the agent’s reasoning accuracy using rigorous evaluation metrics to prevent "hallucinations" and ensure reliability
Deployment & Monitoring
Scaling the agent into production with comprehensive logging and "human-in-the-loop" checkpoints for high-stakes decisions
Before you ask
How do AI agents differ from standard chatbots?
Chatbots are designed for conversation; AI agents are designed for action. While a chatbot might explain how to book a flight, an agent will search for the flight, compare prices against your budget, and execute the booking via API.
Is my proprietary data safe when used by an AI agent?
Yes. We implement enterprise-grade security protocols, including VPC-isolated environments and PII (Personally Identifiable Information) masking, ensuring that your data is never used to train public foundational models.
Can AI agents integrate with my existing CRM or ERP?
Absolutely. Our agents are built to be "tool-aware." They can be granted secure access to Salesforce, HubSpot, SAP, or custom internal databases to read and write data as needed.
What is the typical development timeline for a custom agent?
A Minimum Viable Agent (MVA) typically takes 4–6 weeks from discovery to initial deployment. Complex multi-agent systems requiring deep integration may take 12 weeks or longer.
How do you prevent AI agents from making mistakes?
We utilize Multi-Agent Supervision (one agent checks the work of another) and strict Output Parsing. For critical business actions, we implement mandatory human approval steps before the agent executes the final command.
We'll make it simple