We Build Custom AI Agents Aligned with Your Business Goals

An AI agent is software that automates tasks, makes decisions, and interacts with systems with minimal or no human input. Unlike basic automation, AI agents can analyze data, adapt, and execute actions independently. Businesses use them to handle customer inquiries, schedule tasks, and streamline operations—freeing teams to focus on higher-value work.

AI agents need quality data—structured (databases, spreadsheets) or unstructured (emails, documents). The better the data, the better the agent’s performance. We prioritize data security with self-hosted solutions, ensuring your information stays private and compliant with regulations.

Data control depends on your chosen collaboration model:

AI Agent as a Service (PC-Hosted)

• Hosted and managed on our secure servers.
• We have access to your data for processing, with strong safeguards to maintain privacy.

Self-Hosted AI Agent (Client-Hosted; Private Cloud / On-Prem)

• You maintain full data control within your private infrastructure.
• We only access data temporarily for setup or maintenance.

Pay as You Go AI Agent (SaaS / API-Based)

• Data is processed by external API providers (like OpenAI, Pinecone).
• We configure the AI agent but do not retain long-term access to your data.

Our Managed Service subscription typically costs around $3,000/month (example pricing) and covers:

✔ AI Agent Hosting
We handle server management, scaling, and security.

✔ Monitoring & Maintenance
Regular updates, performance tracking, and issue resolution.

✔ Baseline Development & Support Hours
A set number of hours per month for improvements, integrations, or troubleshooting.

✔ No Hidden Hosting Fees
No additional costs if we host everything (unless specific 3rd-party APIs are required).

Why it’s worth it:

• No hosting hassle: We take care of everything.
• Predictable costs: A fixed monthly fee covers hosting, maintenance, and routine updates.
• Fast deployment: Quick feature rollouts and improvements.
• Expert support: We ensure your AI agent runs smoothly and adapts to your changing needs.

We offer both a monthly subscription and a pay-as-you-go option, and you can mix and match these with any of our deployment models.

The subscription typically costs around $3,000/month and includes 8 hours of development and consulting each week, support, hosting, and more. If you will need more than 8 hours in a given week, additional work is billed on a pay-as-you-go basis.

If you prefer not to subscribe, or only need occasional development, you can choose the purely pay-as-you-go approach and pay only an hourly fee.

• In the Self-Managed AI Agent (PC-hosted) model, you pay either a subscription or pay-as-you-go for development and support, with no extra third-party costs unless you opt into external APIs.
• In a Client-Hosted (Private Cloud/On-Prem) setup, you also have the option of a subscription or pay-as-you-go, and you’d cover any external expenses (e.g., API usage) yourself.
• In the Pay As You Go AI Agent model (Hybrid SaaS/API-Based) model lets you pay only for the development time you use, while any SaaS or API fees are your responsibility.

This flexibility means you can choose predictable, all-inclusive pricing or a fully on-demand approach—whichever aligns best with your needs.

Yes. AI agents connect with your software, databases, and workflows to automate tasks without disrupting your existing setup. For example, integrating an AI agent with your CRM can automate data entry, customer follow-ups, and sales processes.

Pre-built AI agents handle general tasks but may not align with your specific needs. A custom AI agent is built for your business, ensuring seamless integration, compliance with industry regulations, and automation tailored to your workflows.

AI agent integration connects the agent with your existing systems—CRMs, databases, communication platforms—to automate tasks and improve efficiency. This reduces manual work, enhances decision-making, and streamlines operations without requiring a major system overhaul.

Timelines depend on complexity. Simple agents can be built in hours, whereas advanced systems with deep integrations can take weeks or months. AI development is often faster than traditional software methods, allowing quicker implementation and ROI.

AI agents can run on cloud platforms or on-premise servers. Self-hosting ensures full data control by running the agent on your infrastructure—whether on your servers or private cloud—helping businesses meet strict security and compliance requirements.

Alternatively, if you opt for a SaaS-based solution, you do not need to maintain any infrastructure. This option provides flexibility, reduces operational complexity, and ensures seamless integration with your existing workflows.

And yes, we offer AI agent development on our client's infrastructure.

Fine-tuning can improve an AI agent’s performance for specific applications, but it requires careful planning, time, and effort. Before considering fine-tuning, we recommend trying:

✔ Prompt Engineering – Crafting better instructions to improve model responses.
✔ Prompt Chaining – Breaking complex tasks into multiple smaller prompts for better accuracy.
✔ Function Calling & Tool Use – Using external tools and structured API calls to get better results.

When is fine-tuning worth it?

Fine-tuning makes sense when:
🔹 You need a specific style, tone, or format that the base model struggles to follow consistently.
🔹 The model needs to be highly reliable in producing a particular output structure.
🔹 The AI has trouble following complex instructions or handling many edge cases correctly.
🔹 You need it to perform a new skill or task that is difficult to express in a prompt.
🔹 When it’s easier to “show, not tell”—meaning that training it on example outputs is more effective than just tweaking prompts.

When should I enhance the AI with additional data instead of fine-tuning?

If the model lacks knowledge about specific facts, company-specific processes, or recent events, fine-tuning may not be the best solution. Instead, using Retrieval-Augmented Generation (RAG) can be a more efficient way to provide the AI with external knowledge. This involves:

🔹 Feeding structured or unstructured data into a system where the AI can retrieve it when needed.
🔹 Using vector databases (e.g., Pinecone, Qdrant) to store embeddings for efficient search.
🔹 Updating data sources without needing to retrain the model.

What’s the process?

Fine-tuning requires:
🔸 Collecting and structuring high-quality training data.
🔸 Running training jobs, which can take time and resources.
🔸 Iterating and refining based on test results.

Meanwhile, enhancing the AI with external data (RAG) allows for a faster feedback loop, as data updates can be done dynamically without retraining.

If you're unsure whether fine-tuning or external data augmentation is the right approach, contact us, and we’ll help determine the best solution based on your needs!

RAG retrieves real-time information from external sources, making AI responses more accurate and up-to-date. This is crucial for tasks like customer support or research. Keep in mind that RAG requires unstructured data.

We use a comprehensive and versatile tech stack for AI agent development, ensuring adaptability, efficiency, and scalability:

Agent Hosting & Serving

• Assistants API: A robust platform for hosting and managing AI agents, providing seamless deployment and integration capabilities.

Observability

• Langfuse: A tool designed for monitoring the performance and behavior of AI agents, helping identify and resolve issues quickly.
• LangSmith: Provides detailed analytics and debugging features, enabling continuous improvement of AI agents.

Agent Frameworks

• CrewAI: A framework that simplifies the creation and customization of AI agents, focusing on collaborative and scalable solutions.
• AutoGen: Facilitates autonomous decision-making by AI agents, enabling them to perform complex tasks with minimal human input.
• LlamaIndex: Offers an indexing and retrieval framework optimized for large-scale AI applications, enhancing data accessibility and processing.

Storage

• Pinecone: A vector database tailored for managing embeddings and enabling fast similarity searches, crucial for AI agent operations.
• Neon: A serverless PostgreSQL platform optimized for speed and scalability, ideal for structured data storage.
• Algolia: A search and discovery platform for fast, intuitive access to data, enhancing user interaction capabilities.
• Supabase: An open-source backend-as-a-service solution that integrates seamlessly with databases, providing storage and real-time features.
• Qdrant: A high-performance vector database designed for storing, searching, and retrieving embeddings used by AI models.

Model Serving

• Ollama: A model serving platform that allows efficient deployment and scaling of AI models in production.
• Gemini: An advanced model serving tool for delivering high-performance inference and real-time processing.
• OpenAI: Provides state-of-the-art language models for natural language understanding, generation, and decision-making.
• Anthropic: Specializes in safe and reliable AI models, focusing on robust decision-making.
• Groq: A hardware-accelerated platform for rapid and efficient AI model inference.
• DeepSeek: A specialized model for analyzing and retrieving information from unstructured data.

Workflow Automation

• N8N: A no-code workflow automation tool that integrates with multiple systems to streamline repetitive processes.
• Zapier: Simplifies workflow automation by connecting apps and services, enabling AI agents to interact seamlessly with external tools.
• Make: A powerful automation tool that allows customization of workflows and processes, enhancing AI agent functionality.

This stack enables us to develop AI agents tailored to your needs, integrating cutting-edge technologies for optimized performance and efficiency.

There’s only one way to find out! Contact us, share your vision, and we’ll explore the possibilities together. If it’s feasible, we’ll develop a Proof of Concept (PoC) to test its potential and demonstrate how it could work.