Build Internal AI Agents to Automate Your Operations
AI agents automate routine tasks by connecting your existing tools with custom code. They use language models to read documents, analyze data, and support decisions.
Syntora designs and builds custom AI agents to automate routine internal tasks for businesses. Leveraging architectures with FastAPI, AWS Lambda, Claude API, and Supabase, Syntora develops robust, scalable systems that streamline operations by automating document processing, data analysis, and decision support workflows. Our engagement model focuses on understanding your unique business problems to deliver tailored, production-grade solutions.
Developing an AI agent is an engineering engagement, not an off-the-shelf product purchase. The scope depends on the number of data sources, the complexity of the logic, and the volume of documents to process. For example, a system that summarizes inbound support tickets involves different architectural considerations than one that analyzes sales data and drafts weekly reports. Syntora focuses on custom solutions to precisely fit your operational needs.
We've successfully designed and implemented document processing pipelines using Claude API for sensitive financial documents in adjacent industries. This experience in robust data extraction and analysis forms the foundation for building similar automated agents to streamline your internal business operations.
What Problem Does This Solve?
Many small businesses start with visual workflow builders. These tools are great for simple triggers, like posting a Slack message when a new lead arrives in HubSpot. But business-critical processes are rarely that simple. These platforms fail when logic gets complex or when reliability is essential.
A common failure point is task-based pricing. A workflow that reads an attachment, summarizes its content, checks for keywords, and saves the output to a database can consume four tasks for a single document. Processing 500 documents a month results in 2,000 tasks and a surprisingly high bill for one automated process.
Consider an operations team that needs to process vendor invoices. The workflow must open a PDF, extract the invoice number and amount, match it to a PO in their accounting software, and flag any discrepancies over 10%. A point-and-click tool's branching logic often requires duplicating steps, doubling task counts. If the accounting software's API is slow, the workflow times out with no automatic retry, forcing a manual check of every invoice.
How Would Syntora Approach This?
Syntora's engagement begins with a comprehensive audit of your manual workflows, breaking them down into discrete steps. We translate these steps into a series of Python functions, opting for custom code over visual editors to ensure maximum control, flexibility, and maintainability. All solutions are designed for deployment on your own cloud infrastructure, providing you with full ownership over logic, error handling, and operational costs.
For internal task automation, Syntora would typically design a resilient system around a FastAPI service. This service would expose a single, secure endpoint to receive inputs, such as newly uploaded documents or triggered events from existing systems. AWS Lambda functions would handle event processing, extracting text from documents, sending it to the Claude API for advanced summarization or data extraction, and then storing the structured output in a Supabase database. This architecture is chosen for its scalability, cost-efficiency, and robust integration capabilities.
Robust error handling is a core component of any system we deliver. Instead of failing an entire workflow, the system would include specific logic to manage malformed documents or API timeouts from external services. Errors would be logged in Supabase and generate targeted alerts, such as Slack notifications, allowing for efficient exception management without constant manual intervention.
The delivered system would expose a user-friendly dashboard, typically deployed on Vercel, to provide a clear overview of processed documents, their status, and any items flagged for review. Role-based access control would be managed through Supabase Auth to ensure secure and appropriate access for your team. A typical engagement for a system of this complexity involves a build timeline of 8-12 weeks, requiring access to historical data for thorough testing and validation. Clients would need to provide detailed workflow documentation, sample data, and access to relevant internal systems for integration during the discovery and development phases.
What Are the Key Benefits?
Your Agent is Live in 4 Weeks
From our first call to a live production system in 20 business days. Your team sees the benefit immediately, not after a long implementation project.
Escape Per-Seat, Per-Task Pricing
We complete a single, scoped project. After launch, you only pay for minimal cloud hosting costs, not a monthly SaaS subscription that scales with usage.
You Get the Keys and the Code
We deliver the full source code to your private GitHub repository. You own the system, the data, and the documentation, with no vendor lock-in.
Alerts Before Your Team Sees a Problem
We configure structured logging with structlog and CloudWatch alerts. You get a Slack message the moment a critical component fails, not after it impacts operations.
Connects Directly to Your Core Systems
We build direct API integrations to your CRM, document storage, and accounting software. No intermediate platform adds a point of failure or latency.
What Does the Process Look Like?
Week 1: Process Mapping & Access
You provide read-only access to the relevant systems and walk through the existing workflow. We deliver a technical specification and a process diagram for your approval.
Weeks 2-3: System Build & Review
We build the core automation logic and data storage. You receive access to a staging environment to test the agent with non-production data and provide feedback.
Week 4: Deployment & Handoff
We deploy the system to your production environment. We deliver a live training session for your team and the complete source code repository.
Post-Launch: Monitoring & Support
We actively monitor the system for 30 days to resolve any issues. You receive a final runbook detailing how to operate and maintain the agent.
Frequently Asked Questions
- How much does a custom internal AI agent cost?
- Pricing is based on project scope, not seats or tasks. Key factors include the number of systems to integrate and the complexity of the business logic. A simple document summarizer is a smaller project than a multi-step financial reconciliation agent. We provide a fixed-price quote after the initial discovery call. Book a call at cal.com/syntora/discover to discuss your specific needs.
- What happens when an API it depends on goes down?
- We build in retry logic with exponential backoff for temporary API failures. If a service is down for an extended period, the task is moved to a queue in Supabase and an alert is sent via Slack. This prevents data loss and ensures the process can be resumed once the external service is restored. No task is ever silently dropped.
- How is this different from hiring a freelance developer on Upwork?
- Syntora is a consultancy focused on building and maintaining production systems. The engineer on the discovery call is the person who writes every line of code. This avoids miscommunication and ensures a deep understanding of the business problem. We deliver documented, monitored systems with a support plan, not just a one-off script that becomes unmaintainable.
- Does our sensitive data leave our control?
- No. The entire system is deployed on your own infrastructure, typically in your own AWS account. Your data is passed to the Claude API for processing, but Anthropic's policy is not to train on API data. All logs and intermediate data are stored in your private Supabase instance. You retain full ownership and control of all your information.
- What if we need to change the process in six months?
- Because you own the code, modifications are straightforward for any Python developer. The FastAPI application is designed for extension. Minor logic changes are covered under an optional monthly support plan. Adding a new major feature, like integrating another software tool, would be scoped as a small, separate project with a clear deliverable.
- What kind of tasks are a bad fit for this approach?
- AI agents are not suited for tasks that require high-level strategic thinking, complex negotiation, or nuanced human judgment. They excel at structured, rule-based, and repetitive work like data extraction, report generation, and first-pass analysis. The goal is to free up your expert team from routine work, not to replace their core decision-making.
Ready to Automate Your Technology Operations?
Book a call to discuss how we can implement ai automation for your technology business.
Book a Call