Automate Your Business Operations with Custom AI Agents
AI agents automate business operations by executing multi-step workflows without human intervention. They handle tasks like processing documents, qualifying leads, and triaging customer support tickets autonomously.
Syntora designs AI agent systems to automate daily business operations by orchestrating multi-step workflows. These systems use state machines to manage process steps and can integrate with various external APIs. Syntora focuses on delivering technical expertise for building production-grade agent solutions.
These are production systems designed for business-critical processes. The complexity is determined by the number of external systems an agent must coordinate and the logical steps in the workflow. A system that connects three APIs is a standard build; one that parses unstructured PDFs before making decisions requires specialized agents.
Syntora has built document processing pipelines using Claude API for financial documents, and the same architectural patterns apply to various industry documents. When considering an AI agent for your operations, Syntora would start by defining the specific workflow and the systems involved. Typical engagements for a multi-agent system of this complexity involve a 4-8 week build timeline, dependent on the clarity of requirements and the client's ability to provide API access and domain context. The deliverables would include a deployed, monitored system and transfer of operational knowledge.
What Problem Does This Solve?
Many businesses attempt automation with linear, webhook-based platforms. These tools are excellent for simple A-to-B connections, but they fail when a process requires stateful, multi-step coordination. They charge per task, so a single workflow that checks inventory, verifies customer credit, and generates an invoice can burn through 5-10 tasks, making high-volume automation expensive.
A common failure point occurs with out-of-order events. Consider a client onboarding workflow: it needs a signed contract from DocuSign and a paid invoice from Stripe before creating a project in Asana. If the client pays the invoice before signing the contract, a linear workflow breaks. It cannot hold the 'paid' state while waiting for the 'signed' state. Teams are forced to build brittle, branching paths that are impossible to debug and maintain.
Simple AI wrappers face a different problem. Connecting a large language model to an API is easy, but it is not a reliable system. These models hallucinate function calls, fail to handle API errors gracefully, and have no long-term memory of a workflow's state. A prototype might work for a demo, but it cannot be trusted to run a core business operation autonomously.
How Would Syntora Approach This?
Syntora would approach your operational challenge by first conducting a discovery phase to map your business operation as a state machine. Each step, such as 'awaiting_payment' or 'project_created', would become a defined state in a Supabase Postgres database. This persistence layer serves as the agent's memory, allowing a workflow to pause for days and resume exactly where it left off once a trigger, like a webhook from Stripe, is received.
Using this state map, we would design a multi-agent system in Python with LangGraph. A central supervisor agent would coordinate specialized sub-agents. For example, in a client onboarding process, one sub-agent could exclusively handle the DocuSign API, another manage Stripe, and a third provision projects in Asana. Any step requiring language understanding, such as extracting a project name from an email, would be handled by a call to the Claude API with structured JSON output. We have experience building similar structured extraction pipelines.
The core of this system would be packaged as a FastAPI application and deployed on AWS Lambda. This serverless architecture ensures that you only incur compute costs when a workflow is active. When a webhook hits the FastAPI endpoint, it would trigger the Lambda function, which would execute the appropriate agent. This design is chosen for its efficiency and reliability compared to polled triggers.
We would implement structured logging using `structlog` for every state transition and API call. If a sub-agent were to fail to connect to an external service after a set number of automated retries, the supervisor agent would pause the workflow. It would then send a detailed alert to a designated Slack channel with a direct link to the workflow's record in Supabase, enabling human-in-the-loop escalation to resolve the issue and resume the process. This ensures operational resilience and provides transparency into agent actions.
What Are the Key Benefits?
Production-Ready in One Month
From our first call to a live, automated system in just 4 weeks. We skip the lengthy slide decks and start writing Python code on day one.
Pay for Compute, Not Per-Seat Licenses
Your cost is based on AWS Lambda execution time, not how many users you have. A system processing 1,000 workflows a month can cost less than $50 to run.
You Get the Keys and the Blueprints
You receive the complete Python source code in your private GitHub repository. No vendor lock-in, just production-grade code with full documentation.
Alerts Before Your Team Sees a Problem
We build in health checks and detailed logging. If an API connection fails, you get a Slack notification with the exact error, not a vague support ticket.
Connects Directly to Any API
We build direct integrations to any system with an API, from internal databases to industry-specific platforms like Procore or Clio. No reliance on third-party connectors.
What Does the Process Look Like?
Workflow Mapping (Week 1)
You provide system access and walk me through the current process. I deliver a state machine diagram and a technical specification for the agent system.
Core Agent Development (Weeks 2-3)
I build the supervisor and sub-agents in Python. You receive daily progress updates and access to a staging environment to review functionality.
Deployment and Integration (Week 4)
I deploy the system on AWS Lambda and connect the live webhooks from your applications. You receive a runbook detailing the architecture and monitoring setup.
Post-Launch Monitoring (Weeks 5-8)
I actively monitor the system for four weeks to handle edge cases and tune performance. We have weekly check-ins to review logs and success rates.
Frequently Asked Questions
- How much does a custom AI agent system cost?
- Cost depends on the number of integrations and workflow complexity. A system connecting three APIs with ten decision steps is a standard 4-week project. A system with five-plus integrations or complex document parsing may take longer. After a 30-minute discovery call where we map the workflow, I provide a fixed-price proposal.
- What happens when an external API like Google Drive is down?
- The agent responsible for that task will retry the API call with exponential backoff, attempting 3 times over 15 minutes. If it still fails, the entire workflow's state is paused in our Supabase database. A human is alerted via Slack with a direct link to intervene. Once the external API is back online, the process can be resumed from the exact point of failure without losing data.
- How is this different from hiring a freelance developer on Upwork?
- Freelancers build to a spec. I build a production system and the process to maintain it. You are not just getting Python scripts; you get architecture design, infrastructure-as-code deployment, structured logging, health-check monitoring, and a runbook. The person on the discovery call is the engineer who writes the code, eliminating communication overhead and project management failures.
- Can these agents handle tasks that involve understanding documents?
- Yes. For document processing, we use the Claude API. An agent can extract specific fields from invoices, summarize legal contracts, or categorize customer feedback from PDFs. We fine-tune prompts to ensure consistent JSON output, which is then passed to other agents for subsequent actions, like creating a record in your CRM. This is ideal for tasks like accounts payable automation.
- What kind of access and credentials do you need?
- I need API keys or OAuth access for the required services. All credentials are stored securely using AWS Secrets Manager, not in the code repository. For internal systems, I can work within a VPN or use a bastion host. I only require the minimum permissions needed for the agents to perform their tasks, following the principle of least privilege.
- Is this a one-time project or an ongoing relationship?
- The build is a one-time, fixed-scope project. After the 4-week post-launch support period, you own the system. Many clients retain me on a small monthly retainer for ongoing monitoring, maintenance, and adding new agent capabilities. This is optional. You have the code and documentation to manage it yourself or bring in another engineer if you prefer.
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