Custom Claude AI Development for Your Business
Syntora builds custom applications on Anthropic's Claude API for small businesses. We engineer production systems that integrate Claude as a core reasoning engine.
Syntora offers Claude AI integration services, specializing in building custom applications that use Anthropic's Claude API as a core reasoning engine for small businesses. Their approach focuses on engineering production-ready systems with structured outputs and robust deployment strategies. They emphasize understanding client workflows to design tailored solutions rather than selling pre-built products.
The scope of such an engagement involves building a complete system around the API, not just a connection. This includes expert system prompt engineering, structured output parsing, and production wrappers for caching and cost tracking. What determines the scope and timeline are factors like the complexity of your business logic, the volume of data to be processed, and the specific output requirements.
We have experience building document processing pipelines using Claude API for financial documents, and the same patterns apply to other industry documents, ensuring robust data handling and structured outputs.
What Problem Does This Solve?
Many businesses first try to connect to the Claude API using general-purpose automation tools. A platform like Zapier can send a prompt to Claude, but it cannot manage complex logic. For instance, a workflow that needs to read a customer email, look up their order history in Shopify, and then draft a reply based on both, hits a wall. The tool cannot maintain conversational context between steps, leading to generic, unhelpful AI responses.
A common failure scenario involves structured data. A regional insurance agency with 6 adjusters tried using an off-the-shelf AI tool to extract data from 200 claims forms per week. The tool failed on 30% of forms because it couldn't handle variations in PDF layouts. Their SaaS tool had no way to implement custom parsing logic or retry failed documents with a different prompt, forcing adjusters back to manual data entry.
These platforms treat the LLM as a simple, one-shot utility. They lack the architecture for tool-use patterns, context window management, and fallback models. This is the difference between a simple connection and a production system. Business-critical workflows cannot tolerate a 30% failure rate or unpredictable costs.
How Would Syntora Approach This?
Syntora's approach begins by thoroughly mapping your existing workflow and defining a rigid output schema using Pydantic. This step ensures that Claude's responses are consistently structured as valid JSON, which prevents downstream parsing errors. For example, a system designed for a sales team might use a schema for a lead qualification report, specifying fields for budget, timeline, and decision-maker status.
Next, we would develop the core application logic in Python, leveraging the Anthropic SDK. A critical component is the carefully constructed system prompt, often extensive, that clearly defines the AI's role, rules, and available tools. We would utilize Claude's tool-use feature to enable the model to call external functions, such as querying a customer record from a Supabase database before generating a response. This strategy keeps the prompt's context window, which can be thousands of tokens per request, focused on the immediate task.
The Python application would be wrapped in a FastAPI service. This service would incorporate production-grade features like Redis caching to store recent results, which can reduce API costs for repetitive requests and improve response times. We would also implement fallback logic to switch from `claude-3-sonnet` to `claude-3-haiku` if the primary model experiences delays or unavailability, aiming for high availability. All system events would be logged with `structlog` for clear debugging and monitoring.
Finally, we would deploy the FastAPI service to AWS Lambda, fronted by an API Gateway. This serverless architecture is designed to be cost-effective, typically incurring modest monthly charges for moderate workloads. We would configure CloudWatch alerts to notify your team, via Slack, if performance metrics like error rate or P99 latency exceed predefined thresholds. From initial discovery to deployment, a system of this complexity typically involves a build timeline of 3-4 weeks.
What Are the Key Benefits?
Live in 4 Weeks, Not 4 Months
We move from initial discovery call to a deployed production system in under 20 business days. No lengthy sales cycles or project management overhead.
A Fixed Build Cost, Not a SaaS Bill
One-time project pricing for the build. Afterwards, you only pay for AWS hosting and Anthropic API usage, which is often less than $100 per month.
You Own the Code and the Infrastructure
We hand over the complete GitHub repository and AWS account. You are never locked into a proprietary platform and can extend the system yourself later.
Monitored Performance, Not a Black Box
We configure CloudWatch dashboards and Slack alerts for latency, errors, and costs. You see exactly how the system performs and get notified if something is wrong.
Integrates With Your Real Systems
We connect directly to your primary data sources, whether that's a Supabase database, a Salesforce CRM, or a proprietary internal API.
What Does the Process Look Like?
Week 1: Scoping and Access
We hold a 2-hour discovery session to map the workflow. You provide API keys and access to relevant systems. The deliverable is a one-page technical design document.
Week 2: Core Application Build
I write the core Python logic, including prompt engineering and output parsing. The deliverable is access to a private GitHub repository with the initial code.
Week 3: Deployment and Integration
The application is deployed to a staging environment on AWS. We connect it to your systems and run end-to-end tests. The deliverable is a functional API endpoint.
Week 4: Monitoring and Handoff
We monitor the live system for one week, tune performance, and document everything. The deliverable is a runbook covering maintenance and troubleshooting.
Frequently Asked Questions
- What does a typical Claude AI integration project cost?
- Pricing depends on complexity. A system that reads emails and generates summaries is simpler than one that uses multiple tools to interact with your CRM and database. We scope every project during a free discovery call and provide a fixed price for the entire build. There are no hourly rates or hidden fees. Book a call at cal.com/syntora/discover to discuss your specific needs.
- What happens if the Claude API is down or returns bad data?
- The production wrapper we build handles this. API calls have automatic retries with exponential backoff. If a response fails Pydantic validation, the system logs the error and can either try again with a different prompt or flag the item for manual review. For total outages, we can configure a fallback to a different Anthropic model or a static response.
- How is this different from hiring a large AI consultancy?
- Syntora is a one-person consultancy. The engineer on your discovery call is the person who writes every line of code. There are no project managers or offshore teams. This direct-to-engineer model is faster and more focused, designed for small businesses that need a hands-on technical partner, not a large-scale enterprise solution.
- Do we need our own developer to maintain the system?
- No. The system is deployed on serverless infrastructure that requires minimal maintenance. The runbook we provide covers common operational tasks. We offer an optional, flat-rate monthly support plan for ongoing monitoring, updates, and prompt tuning after the initial handoff period. Most clients do not require it for the first year.
- What information do you need from us to get started?
- The first step is a discovery call. To prepare, you should be able to describe the business process you want to automate and have an idea of the data sources involved (e.g., Google Drive, HubSpot, a SQL database). We will also need an API key from Anthropic, which you control and are billed for directly.
- Can you improve an existing Claude integration we already built?
- Yes, if it is written in Python. A common engagement is adding a production wrapper to an existing proof-of-concept script. We can add proper logging, error handling, caching, and deploy it to a reliable serverless architecture on AWS Lambda. This turns a fragile script into a dependable business system that you can monitor and trust.
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