Build Internal AI Tools Without Hiring a Full-Time Team
SMBs should outsource AI development when a project has a defined scope and requires specialized engineering. Hiring in-house makes sense for long-term, core-product R&D, not for building a single, specific tool.
Syntora offers specialized AI development services for small to medium businesses, focusing on systems that support existing operations like document summarization. Our approach outlines a clear technical architecture and engagement process to deliver tailored solutions, leveraging expertise in similar document processing challenges.
Outsourcing is best for building systems that support existing business processes, like summarizing documents, analyzing sales data, or routing customer inquiries. These projects have clear inputs and outputs, making them ideal for a focused engagement with a single expert team that handles all the engineering. For document summarization, Syntora's engagements begin with a discovery phase to understand your specific document types, desired output structures, and integration points. This foundational work determines the project's scope and architecture.
What Problem Does This Solve?
The default path for building custom software is to hire a developer. For AI projects, this often fails. A senior AI engineer costs over $200,000 per year fully loaded, takes 3-6 months to find and hire, and may leave after a year, taking all project knowledge with them. For an SMB, a single bad hire can kill a project and burn a significant portion of the annual budget.
A more common failure is the skills mismatch. You hire a data scientist who can build a model in a Jupyter Notebook but has never deployed a production API or managed cloud infrastructure. Alternatively, you hire a generalist full-stack developer who can build an API but has no experience with prompt engineering, LLM APIs, or vector databases. This results in a system that either never gets deployed or is too brittle for business-critical use.
We saw this with a 25-person logistics company that hired a Python developer to build a routing optimizer. Six months later, they had a script that only ran on the developer's laptop. It had no API, no authentication, and crashed on real-world data volumes because it tried to load everything into memory. The project was scrapped after spending six figures in salary with nothing to show for it.
How Would Syntora Approach This?
Syntora would start by auditing your existing document sources, whether they are email attachments, a shared drive with PDFs, or an existing database. For document parsing, the system would typically use Python with the `unstructured` library to convert Word documents, PDFs, and images into clean text. This extracted data would be stored in a Supabase Postgres database, using the `pgvector` extension to enable efficient semantic search.
The architecture would center on a FastAPI service orchestrating the workflow. For an incoming document, the system would construct a targeted prompt using the extracted text and send it to the Claude 3 Sonnet API via an async `httpx` call. The prompt would instruct Claude to return a structured JSON summary with specific fields. We've built document processing pipelines using Claude API for financial documents, and the same pattern applies to summarizing documents in other industries. The goal would be efficient processing, with performance tuned to your specific document volumes.
The FastAPI service would be packaged into a container and deployed on AWS Lambda. This approach helps manage hosting costs, with projections provided based on your anticipated usage volume. Syntora would develop a simple, secure frontend on Vercel with role-based access control, which can be tied to your Google Workspace or Microsoft 365. This dashboard would provide your team with a clear interface to view and manage the AI-generated outputs.
Production systems would be monitored using AWS CloudWatch, with automated alerts configured for API errors (any 5xx status codes) and high latency (over a 10-second response time). All application events would be written to structured logs with `structlog`, facilitating straightforward tracing and debugging of document processing.
What Are the Key Benefits?
Production System Live in 20 Business Days
We deploy a working system your team can use in four weeks. No lengthy R&D phases or prototypes that never ship.
No Six-Figure Salary, No Per-Seat Fees
One scoped project fee, then minimal monthly hosting costs. You are not paying for a full-time employee or an expensive SaaS subscription.
You Get the Keys and the Blueprints
You receive the full source code in your own GitHub repository and a technical runbook. Nothing is proprietary or locked down.
Alerts Fire Before Your Team Notices
Active monitoring with AWS CloudWatch and structured logging means we detect and fix issues before they impact your workflow.
Connects to Your Data, Not Ours
The system is deployed in your own cloud environment and connects directly to your data sources like Google Drive, S3, or SharePoint.
What Does the Process Look Like?
Kickoff and Access (Week 1)
You provide access to data sources and credentials. We hold a discovery session to map the workflow. You receive a system architecture diagram.
Core System Build (Weeks 2-3)
We build the data processing pipeline and the core API. You receive a secure staging link to test the system with sample data.
Frontend and Deployment (Week 4)
We build the user interface, connect it to the backend, and deploy to your production environment. You receive login credentials for your team.
Monitoring and Handoff (Weeks 5-8)
We monitor the live system for performance and accuracy, resolving any issues that arise. You receive the complete source code and system runbook.
Frequently Asked Questions
- How much does a project cost and how long does it take?
- Cost depends entirely on complexity. A simple API that summarizes text from one data source is a standard 4-week project. A system that integrates with three different data sources and requires a complex user interface might take 6-8 weeks. We provide a fixed-price proposal after our initial discovery call, so you know the full cost upfront. Book a call at cal.com/syntora/discover to discuss your specific project.
- What happens if the Claude API goes down or a process fails?
- The system is built for resilience. API calls to Claude use exponential backoff and retries. If a call fails after three retries, the document is flagged for manual review in the UI and a high-priority alert is sent. The system does not halt; it gracefully handles the failure and notifies us immediately. Your team's workflow is never blocked by a transient third-party outage.
- How is this different from hiring a freelance developer on Upwork?
- This is different in two ways: specialization and execution. Syntora only builds AI-powered business systems using a specific, production-proven tech stack. Freelancers are often generalists. Second, the person on your discovery call is the person who writes every line of code. There are no project managers or offshore handoffs, which eliminates communication gaps and ensures deep ownership of the final product.
- Is the system secure? Our data is highly sensitive.
- Yes. The entire system is deployed within your own cloud infrastructure (your AWS account). Your data is processed in your environment and never passes through Syntora's servers. We connect to your data sources using read-only credentials you provide. You maintain full control and ownership of your data at all times, and we sign an NDA before any work begins.
- Who provides support after the initial project is complete?
- After the 8-week launch and monitoring period, we offer an optional monthly support retainer. This provides a guaranteed 4-hour response time for any issues, covers ongoing maintenance, and includes a small bucket of hours for feature enhancements or adjustments. This ensures the system continues to operate smoothly as your business needs change.
- What if we don't know exactly what to build yet?
- That is a common starting point. We offer a 2-week, fixed-scope AI Opportunity Audit. We'll analyze your existing workflows and data to identify and scope the top 1-3 highest-ROI automation opportunities. You get a concrete, actionable roadmap that you can use to build with us or take to another developer. It is a low-risk way to get started with a clear plan.
Ready to Automate Your Technology Operations?
Book a call to discuss how we can implement ai automation for your technology business.
Book a Call