Build Custom AI Agents That Automate Your Critical Workflows
A custom AI agent system for one core business process typically requires 4 to 8 weeks of focused engineering. This one-time engagement can replace unpredictable monthly SaaS fees with minimal, direct cloud costs.
Syntora develops custom AI agent systems for business process automation, focusing on detailed architectural design and strategic technology choices like FastAPI, Claude API, and AWS Lambda. We approach each engagement as a unique engineering challenge, building tailored solutions to specific operational needs.
The final timeline depends on the number of systems to integrate, the clarity of the business rules, and the complexity of the process logic. An agent that reads an email and creates a CRM record might be a 3-week build. A multi-agent system that processes insurance claims, queries several internal databases, and requires human review for exceptions would take longer due to increased integration points and more intricate logic. Syntora designs these systems to address specific operational challenges, providing a tailored engineering solution.
What Problem Does This Solve?
Many teams first try connector-based automation platforms. These tools are great for simple, linear tasks but break down when real business logic is needed. Their task-based pricing models become expensive fast. A workflow that reads an email, extracts text, asks an AI to summarize, and saves to a database consumes 4 tasks. For 500 emails a day, that is 2,000 tasks daily and a significant monthly bill for one process.
The real failure is in their architecture. These platforms are stateless. They cannot manage workflows that run for hours or days, handle complex error conditions, or allow one step to intelligently inform another. Consider a recruiting firm's applicant-screening workflow. If the AI agent needs to parse a resume, then check the applicant's LinkedIn, and then decide whether to schedule an interview based on both sources, a simple connector tool fails. It cannot maintain the context from the resume while analyzing the LinkedIn profile.
This forces teams to create brittle, multi-part workflows with duplicated steps that are impossible to maintain. When one part fails, the whole process stops silently. There is no central orchestration, no retry logic, and no way to escalate to a human for a decision. They are designed for moving data from point A to point B, not for building autonomous systems that can reason and recover.
How Would Syntora Approach This?
Syntora would start an engagement by thoroughly mapping your entire manual workflow. For a business process like new insurance claim intake, we would identify integration points to source systems, such as using the Gmail API for inbound emails and connecting to your agency's existing management system API. We would use Python with Pydantic to define data validation models, ensuring critical fields are present and correctly formatted before processing proceeds.
The core of the system would be a supervisor agent, built using a custom Python state machine, designed to orchestrate specialized sub-agents. For example, a 'Triage Agent' could use the Claude 3 Sonnet API to classify the claim type from an initial email. A 'Document Agent' would be responsible for extracting policy numbers and incident details from attached PDFs. A 'Validation Agent' would then query a customer database, which we could set up in Supabase, to check policy status, all coordinated by the supervisor.
The multi-agent system's state management and complex routing would be implemented using a framework like LangGraph. This allows for conditional logic; for instance, if a Document Agent extracts a policy number with less than a defined confidence threshold, LangGraph would route the claim to a human-in-the-loop queue. This queue would be a simple web interface, built with FastAPI and hosted on a platform like Vercel, where an adjuster could review and correct information.
The complete system would be packaged into a Docker container and deployed on AWS Lambda, triggered by webhooks from your email provider or other event sources. This serverless, event-driven architecture means cloud costs remain low when the system is idle. We would configure structured logging using structlog and establish alerts in AWS CloudWatch that would trigger if the system's error rate shows a spike, allowing proactive monitoring and issue resolution. This approach focuses on building a resilient and efficient custom solution for your specific business needs.
What Are the Key Benefits?
Your First Agent is Live in 4 Weeks
We complete a focused, single-process agent system in under 20 business days. You see results immediately, not after a quarter-long project.
Pay for The Build, Not Per User
A one-time engineering engagement followed by direct, low-cost cloud hosting. No recurring SaaS license that scales with your headcount or usage.
You Get The Keys and The Blueprints
You receive the full Python source code in your own GitHub repository. We provide a runbook detailing architecture, dependencies, and maintenance.
Alerts Fire Before Your Team Notices
We build monitoring directly into the system using AWS CloudWatch. You get a Slack alert if error rates spike or latency increases, not an angry email.
Connects Directly to Your Core Systems
We work with native APIs for systems like HubSpot, Salesforce, and Gmail. No brittle screen-scraping or relying on third-party connectors.
What Does the Process Look Like?
Week 1: Process Mapping & Access
You provide process documentation and read-only access to relevant systems. We deliver a detailed workflow diagram and a technical plan for your approval.
Weeks 2-3: Agent Development & Review
We build the core multi-agent system in a development environment. You receive access to a staging version to test with sample data and provide feedback.
Week 4: Deployment & Integration
We deploy the system to your production cloud environment and connect it to your live data sources. You receive the first automated outputs for review.
Weeks 5-8: Monitoring & Handoff
We monitor system performance, accuracy, and cost, making adjustments as needed. At the end of the period, we deliver the final code and documentation.
Frequently Asked Questions
- What factors determine the final cost and timeline?
- The primary factors are the number of system integrations, the complexity of the business logic, and the quality of the source data. An agent that reads one API and writes to another is straightforward. A system that needs to orchestrate multiple agents, manage state over several days, and include a human review interface requires more engineering time. We provide a fixed quote after our discovery call. Book a call at cal.com/syntora/discover.
- What happens when an agent fails or an API it depends on is down?
- The orchestration layer has built-in retry logic with exponential backoff for temporary API issues. For a persistent failure, the system automatically places the task in a manual review queue and sends a Slack alert with the specific error log. This ensures no data is ever lost and your team can intervene when automation is not possible. The system is designed to fail gracefully.
- How is this different from hiring a freelance developer on Upwork?
- A generalist developer knows Python, but not the specific design patterns for building reliable agentic systems. We specialize exclusively in this work. We have already built and deployed systems using LangGraph, custom state machines, and human-in-the-loop escalation paths. This experience de-risks your project and results in a production-ready system, not a fragile script.
- Why do you use the Claude API instead of OpenAI?
- For most business automation tasks, the Claude 3 model family, specifically Sonnet, offers the best combination of speed, cost, and reasoning. We have found its rate of structured data extraction errors to be lower than competitors, which is critical for production workflows. We can build with any model, but we start with Claude for its reliability in these specific use cases.
- What does long-term maintenance look like after the initial 8 weeks?
- The system is deployed to your own cloud account, so you have full control and are responsible for the direct cloud costs, typically under $50/month. The runbook we provide covers common maintenance tasks. For teams that want ongoing support, we offer an optional retainer that includes proactive monitoring, dependency updates, and a 4-hour response SLA for any production issues.
- How do you handle our sensitive business data?
- We never store your data on Syntora's systems. The agent code runs entirely within your own secure cloud environment on AWS. We access your APIs using credentials stored in a service like AWS Secrets Manager, which you control. All data processing happens in-memory during the agent's execution or is stored in your own Supabase database instance.
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