Get Custom AI Agents Built for Your Business
Syntora is an AI automation consultancy that develops custom AI agents for businesses. We build multi-agent systems to handle complex, multi-step workflows autonomously.
Syntora is an AI automation consultancy specializing in custom AI agent development for complex, multi-step workflows. We help businesses design and build intelligent systems to automate processes using modern AI and engineering practices. Our approach focuses on transparent architecture and engagement, ensuring clients receive tailored solutions built on a clear understanding of their specific operational needs.
The scope of an AI agent development engagement is determined by workflow complexity and the number of system integrations required. For example, a lead qualification agent connecting a CRM to a data enrichment API is a more straightforward build. An intricate insurance claims processor that reads diverse PDFs, queries multiple internal databases, and necessitates human review for specific cases represents a more involved project.
Syntora brings deep technical expertise in constructing robust data processing pipelines and integrating large language models. We have built document processing pipelines using Claude API for sensitive financial documents, and the same architectural patterns apply to automating complex workflows in other industries. Typical build timelines for multi-agent systems of significant complexity often range from 8-16 weeks, depending on the integration points, data readiness, and validation requirements. Clients would typically provide access to relevant APIs, databases, and domain experts. Deliverables would include a deployed, monitored system, full source code, and comprehensive documentation.
What Problem Does This Solve?
Many businesses first attempt automation with GUI-based platforms. These tools are excellent for simple, linear tasks like posting a form submission to Slack. The trouble starts when a workflow requires memory, complex logic, or error handling. These platforms are typically stateless, meaning they cannot easily pause a task and resume it later with full context if an external API fails.
A common failure scenario involves multi-step data processing. Consider a 15-person logistics company trying to automate freight quoting. The process requires reading an email request, extracting 5-6 key data points, querying three separate carrier APIs, comparing the results using custom business logic, and then drafting a reply. A visual automation tool breaks here. It cannot run the API calls in parallel, its logic module cannot handle the custom comparison rules, and a single API timeout forces the entire 10-step workflow to fail and restart, burning through task credits.
These platforms also lack true observability. When a workflow fails, you might get a generic error message, but you cannot inspect the agent's state, view structured logs, or understand the exact point of failure. This makes debugging business-critical processes nearly impossible and forces teams back to manual work.
How Would Syntora Approach This?
Syntora would begin by thoroughly mapping your entire workflow onto a formal state machine using Python's LangGraph library. This graph would define every possible state and transition, ensuring the process is predictable, auditable, and resilient. All runtime state, inputs, and outputs for every workflow execution would be persisted in a dedicated Supabase Postgres database. This design provides full resumability; if a run encounters an issue on an intermediate step, a human can intervene and restart it from that exact point with full context.
The core of the system would be a set of specialized sub-agents written in Python. For a scenario involving document processing, for instance, one agent might use the Claude 3 Sonnet API to reliably extract structured data from unstructured emails or PDF documents. A second agent, using httpx, would then call relevant external APIs or internal databases concurrently. A supervisor agent would orchestrate this multi-step process, collecting the results from the sub-agents and applying custom business logic to reach a final outcome.
Human-in-the-loop escalation would be built in from the start to handle exceptions and edge cases. If a supervisor agent cannot complete a task after a configurable number of retry attempts, it would trigger a webhook that sends a message to a designated communication channel, such as Slack. The message would include a direct link to the Supabase record for the incomplete run, providing a human operator with all the necessary context to efficiently resolve the issue.
The final system would be packaged into a container and deployed as a FastAPI service on AWS Lambda, triggered by webhooks or scheduled events. This serverless architecture would mean compute costs scale directly with usage, optimizing operational expenses. Syntora would implement structured logging with `structlog` to AWS CloudWatch, with alarms configured to monitor critical system metrics such as error rates and execution times, ensuring proactive identification and resolution of potential issues.
What Are the Key Benefits?
Production System Live in 4 Weeks
We move from discovery to a deployed, production-ready system in 20 business days. No lengthy sales cycles or project management overhead.
Zero Per-Seat or Per-Task Fees
You pay for the initial build and a flat, predictable monthly hosting cost on AWS, typically under $50. No surprise bills that scale with usage.
You Get the Keys and the Blueprints
You receive the full Python source code in your private GitHub repository, plus detailed runbooks. No vendor lock-in, ever.
Alerts You Can Actually Act On
Failures trigger a Slack alert with a direct link to the Supabase execution log. You see exactly what broke and why, no digging through dashboards.
Connects to Any API, Not Just a Pre-Built Library
We write custom Python connectors for your internal databases or obscure third-party APIs. Your workflow isn't limited by a marketplace of apps.
What Does the Process Look Like?
Workflow Discovery (Week 1)
You provide access to current tools and walk us through the workflow. We deliver a detailed state machine diagram and a technical proposal for the build.
Core System Build (Weeks 2-3)
We write the Python code for the agents and orchestration layer. You get access to a staging environment to test key parts of the workflow.
Deployment & Integration (Week 4)
We deploy the system to AWS Lambda and connect the production webhooks. We deliver a runbook with deployment instructions and monitoring setup.
Monitoring & Handoff (Weeks 5-8)
We monitor the system in production for 4 weeks, handling any issues. After this period, you receive the full source code and we offer an optional support plan.
Frequently Asked Questions
- How much does a custom AI agent system cost?
- Pricing is based on the number of integrations and workflow complexity. A simple lead qualification agent connecting two APIs is a much smaller scope than a multi-step document processor with human-in-the-loop escalation. After our one-hour discovery call, we provide a fixed-price proposal with a detailed scope of work. Most projects are a one-time build fee plus minimal cloud hosting costs.
- What happens when an external API like Claude is down?
- The system is designed for graceful failure. Each agent step runs in an isolated context with its own retry logic. If the Claude API is unresponsive, the supervisor agent will retry twice with a 5-second delay. If it still fails, the entire task is paused, its state is saved to Supabase, and a human operator is alerted. No data is lost, and the task can be resumed later.
- How is this different from hiring a freelance developer on Upwork?
- A freelancer might build a script, but Syntora builds a production system. We deliver not just Python code, but a complete solution including state management with Supabase, orchestration with LangGraph, serverless deployment on AWS Lambda, and structured logging for monitoring. You get a maintainable, observable system documented with a runbook, not just a collection of functions. The person on the sales call is the engineer who builds it.
- What kind of businesses are a good fit for Syntora?
- Our clients are typically 5-50 person businesses where a core operational workflow has become a major bottleneck. They've often tried off-the-shelf tools but find they break under real-world complexity. They need a production-grade system but don't have a full-time AI engineer on staff. We work directly with the business owner or head of operations who feels the pain of the broken process.
- Are we locked into using the Claude API?
- No. We default to the Claude 3 family for its strong instruction-following and large context window, but the architecture is model-agnostic. The LLM call is an abstracted function. We can swap in GPT-4, Gemini, or an open-source model hosted on a private endpoint with minimal code changes. We select the best model for the specific task, balancing cost, speed, and capability.
- How is our company's data handled?
- Your data is processed within your own cloud environment or ours, per your preference, and never used for training models. We use Supabase for state persistence, which you can host in your own account. All API keys and credentials are encrypted and stored securely using AWS Secrets Manager. We can sign an NDA before the discovery call and provide a Data Processing Agreement detailing our security practices.
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