Get Production AI Agents Built by a Hands-On Engineer
Syntora builds production-grade AI agents for small businesses. An AI agent system automates multi-step workflows without constant human intervention.
Key Takeaways
- Syntora builds production-grade AI agents for small businesses, from lead qualification to customer support triage.
- The founder who takes your call is the engineer who writes every line of production code.
- Systems are built from scratch using Python, Claude API, and custom orchestration for business-critical workflows.
- A typical deployment automates multi-step processes, reducing manual work by over 20 hours per week.
Syntora designs and engineers production-grade AI agent systems for small businesses. These bespoke systems automate complex multi-step workflows, integrating with existing tools and escalating to human oversight as needed. Syntora focuses on delivering custom, reliable solutions tailored to specific business process challenges.
These are not simple chatbots. We specialize in designing and engineering systems that can manage complex, business-critical processes, such as intelligent lead qualification, automated claims processing, or advanced customer support triaging. Such systems are built with custom logic, integrate directly with your existing tools via API, and are designed to escalate to human oversight only when necessary. Our approach focuses on understanding your unique operational challenges to architect a robust, tailored solution. We leverage our experience building similar sophisticated document processing and workflow automation systems in adjacent industries to deliver reliable AI agent capabilities.
Why Do Small Businesses Struggle to Automate Complex Workflows?
Many businesses start with visual workflow builders. They are great for connecting two APIs, like sending a Slack message for a new Stripe sale. But they fail when logic gets complex. For example, a workflow that needs to check inventory, verify customer credit, and then apply a tiered discount requires branching paths that often cannot merge back together. This forces you to create duplicate, hard-to-maintain workflows.
Consider a 12-person recruiting firm processing 400 applicants a month. They try to automate resume parsing. The workflow builder's OpenAI integration can extract text, but it struggles with inconsistent PDF formats. It also charges per 'task', so a 5-step process (trigger, parse, format, log to ATS, notify recruiter) burns 5 tasks per applicant. At 400 applicants, that is 2,000 tasks, hitting usage limits and high costs for a single, unreliable workflow.
The core issue is that these platforms are not designed for state management. An agent that needs to remember the last three interactions with a customer or wait for an external approval cannot be built in a system that treats every run as a stateless transaction. This is why complex, business-critical processes still require manual work; the off-the-shelf tools are architecturally unsuited for the job.
How Syntora Builds Multi-Agent Systems to Handle Multi-Step Workflows
Syntora approaches AI agent development as a custom engineering engagement. The initial phase involves a thorough discovery process where we map your entire workflow into a state machine diagram, often using tools like Miro. This defines every possible state and transition, such as 'awaiting_document' to 'parsing_document' to 'validation_failed'. We use this detailed map to define the agent's core logic and identify all necessary API integrations, connecting the system to your existing tools like Google Drive for new files or your internal CRM via its REST API.
The orchestration layer would be built in Python using frameworks like LangGraph, which allows us to define the state machine as a computational graph. Each node in this graph represents a specialized sub-agent. For example, a lead qualification agent might feature one sub-agent utilizing the Claude API to analyze email intent, another calling an enrichment API for firmographics, and a third checking against existing customer data in a Supabase table. A supervisor agent would then route the lead through these nodes based on the current state and overall workflow. We've built document processing pipelines using Claude API for financial documents, and the same robust pattern applies to analyzing documents for other industry-specific workflows.
The entire agent system would be containerized with Docker and deployed as an AWS Lambda function behind an API Gateway, ensuring scalability and reliability. Workflows are typically triggered by webhooks from your existing tools. The agent's state persistence would be handled by a dedicated Supabase Postgres database, ensuring data integrity and allowing for graceful recovery if a single step fails. The architecture can be designed to maintain cost efficiency, typically resulting in cloud infrastructure costs of under $50 per month for average workloads. Performance targets, such as completing a complex workflow run within a few seconds, would be established during the design phase.
A critical component is the supervisor agent that coordinates the specialized sub-agents and handles error conditions. It can be configured to monitor sub-agent performance, with escalation rules based on factors like consecutive failures or confidence thresholds. For instance, if a sub-agent's confidence score falls below a pre-defined threshold, the supervisor would escalate the task. Typical escalation paths involve a human-in-the-loop workflow, posting a detailed failure report with a link to the specific record into a designated Slack channel. This ensures human intervention for critical issues.
Building such a system typically takes 6-12 weeks, depending on the complexity of the workflow and the number of integrations. Clients would need to provide detailed access to their existing systems and collaborate actively during the discovery and testing phases. Deliverables include the deployed, production-ready AI agent system, comprehensive documentation, and ongoing support options.
| Manual Workflow | Syntora's Automated Agent System |
|---|---|
| Lead qualification takes 10-15 minutes per lead | Lead qualified and routed in under 60 seconds |
| Error rate from data entry reaches 8% | Error rate from automated processing is under 0.5% |
| Support team spends 2 hours daily on ticket triage | Triage is instant, support staff save 10 hours/week |
What Are the Key Benefits?
Production-Ready in 4 Weeks
We move from discovery call to a deployed, production-grade agent system in a single 4-week cycle. No lengthy project timelines or delays.
Fixed Build Cost, Low Operational Spend
You get a single, scoped price for the entire build. Post-launch, serverless hosting on AWS Lambda often costs less than $20 per month.
You Get the Full Source Code
At handoff, you receive the complete Python codebase in your own GitHub repository. You are not locked into a platform and can modify the system yourself.
Failure Alerts Sent Directly to Slack
The system monitors itself. When an agent requires human intervention, it sends a detailed alert to a Slack channel with all context needed to resolve the issue.
Connects to Your Tools via API
Agents integrate directly with your existing software like HubSpot, Google Drive, or custom internal databases using their native REST APIs.
What Does the Process Look Like?
Week 1: Workflow Discovery
You provide access to relevant systems and walk through the current process. The deliverable is a detailed state machine diagram in Miro outlining the full agent logic.
Weeks 2-3: Core Agent Development
We build the agent supervisor, sub-agents, and integrations in a private GitHub repo. You receive daily progress updates and a link to the active development branch.
Week 4: Deployment and Testing
The system is deployed to AWS Lambda. You receive API documentation and a runbook. We test with live data and verify outputs in your production systems.
Post-Launch: Monitoring and Handoff
We monitor the system for 30 days to handle any issues. At the end of the period, we conduct a final handoff call and transition to an optional support plan.
Frequently Asked Questions
- How is the cost and timeline determined for a project?
- Pricing is based on two factors: the number of unique states the agent system needs to manage and the number of external API integrations required. A simple lead qualification agent with 3 states and 2 integrations is a standard 4-week build. A complex document processing system with 10 states and 5 integrations will take longer. We provide a fixed-price quote after the initial discovery call.
- What happens if an external API like Claude is down?
- The agent's state machine is designed for this. If an API call fails, the system uses exponential backoff to retry three times over 5 minutes. If it still fails, the task's state is marked 'pending_api_recovery' in the Supabase database. A background process retries every hour. For critical, time-sensitive workflows, it escalates to the human-in-the-loop Slack channel after the first failure.
- How is this different from hiring a freelance developer on Upwork?
- Syntora provides a production-ready system, not just code. This includes deployment, infrastructure as code via Terraform, structured logging, monitoring, and a runbook. A freelancer might deliver a Python script, but you are responsible for turning it into a reliable, observable service. We build and maintain the entire operational system, which is a fundamentally different scope of work than just writing a script.
- How do you handle sensitive customer data?
- The system is deployed in your own AWS account, so data never leaves your cloud environment. We access external APIs using credentials stored securely in AWS Secrets Manager. Syntora's access is limited to the development and monitoring period via time-bound IAM roles. We never store your customer data on our own systems and can sign any required NDAs or DPAs.
- Can these agents handle higher volumes if my business grows?
- Yes. The architecture is built on AWS Lambda, which scales automatically. It can handle 10 events per day or 10,000 without any changes. The Supabase database is on a standard plan that supports up to 500 concurrent connections, and the cost scales with usage. We've load-tested similar systems up to 100 requests per second without performance degradation.
- What if my workflow changes after the agent is built?
- Because you own the code, you can modify it. The system is built with LangGraph, which makes it straightforward to add, remove, or change the agent's steps by modifying the graph definition. The runbook we provide includes instructions for common modifications. For more significant changes, we offer follow-on work on a project basis.
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