Develop Custom AI Algorithms with a Hands-On Engineer
Syntora develops custom AI algorithms for specific business operations. We build production systems from scratch for forecasting, optimization, and scoring.
Syntora specializes in developing custom AI algorithms for business operations, focusing on solutions for demand forecasting, route optimization, and operational scoring. We engineer bespoke production systems tailored to your unique data and specific business challenges, providing expertise and engagement rather than off-the-shelf products.
The scope of developing custom AI algorithms depends heavily on your data quality and the operational complexity of the problem. For example, a time-series forecast leveraging 24 months of clean, structured sales data represents a more direct engagement. In contrast, a route optimization model that must account for multiple vehicle types, real-time traffic, and complex driver constraints would require a more intricate architectural approach and longer development timeline.
Our expertise lies in understanding these nuances and engineering bespoke solutions tailored to your unique operational challenges.
What Problem Does This Solve?
Many businesses first look for freelancers on Upwork to build a model. They receive a Jupyter Notebook, but this is not a production system. It cannot be called by other applications, has no monitoring, and the freelancer is often unavailable for support a month later. It's a proof-of-concept, not an operational tool.
Then they might evaluate a large consulting firm. These firms are built for enterprise clients, bringing a team of five people to a problem one engineer can solve. A simple lead scoring model becomes a six-month engagement because their process requires a project manager, a business analyst, and multiple developers. The cost reflects this overhead, not the engineering work required.
Off-the-shelf AI platforms seem like a solution, but they are rigid. A logistics company with 15 drivers might try a route planning SaaS. The tool optimizes for distance but cannot handle their specific constraints like mandatory lunch breaks or vehicle capacity. The generated routes are operationally useless, and drivers revert to manual planning.
How Would Syntora Approach This?
Syntora's approach begins with a comprehensive discovery phase, focusing on your existing data infrastructure and operational workflows. We would start by auditing your production data sources, such as PostgreSQL databases on Supabase or external service APIs, to understand data quality and availability. For a route optimization challenge, this initial phase would involve understanding how customer addresses are captured and if geocoding capabilities are required. We'd use Python's pandas library to analyze historical operational data, identifying key patterns and constraints that would inform the algorithm design.
The core algorithms would be developed in Python. For optimization problems like route planning, we would typically leverage libraries such as Google's OR-Tools to model complex constraints, including delivery time windows, vehicle capacities, and driver availability. The design would specify how the algorithm would ingest daily operational manifests and generate optimized sequences tailored to your operational scale.
We would wrap the developed algorithm in a FastAPI service, exposing it through a robust REST API endpoint. This service would be engineered for deployment on serverless platforms like AWS Lambda, optimizing for cost-efficiency and scalability. We would manage the deployment through continuous integration pipelines using tools like Vercel, ensuring future updates and maintenance are streamlined and reliable. The API would be designed with performance considerations in mind, targeting efficient response times suitable for integration into your existing systems.
For operational visibility, all API calls and model outputs would be logged using tools like structlog. We would implement a monitoring and alerting system to detect anomalies or deviations in model performance. For forecasting applications, the engagement would include the design and implementation of an automated retraining pipeline, ensuring the model continuously adapts to new data and evolving business patterns. This iterative approach ensures the solution remains relevant and accurate over time, delivered as an integrated component ready for your team's use.
What Are the Key Benefits?
Production-Ready in 4 Weeks
We move from initial data audit to a deployed production system in a 4-week cycle. No six-month projects or lengthy discovery phases.
No Sales Team, No Project Managers
You pay for engineering, not overhead. The founder on your call is the engineer who writes the code, eliminating communication delays and extra costs.
You Get the Keys and the Code
We deliver the complete source code in your private GitHub repository and a technical runbook. You have full ownership and control.
Alerts Before Your Team Sees a Problem
We use health checks and performance monitoring to detect issues. You get a Slack alert if API response time exceeds 800ms or error rates pass 1%.
Connects to Your Existing Tools
The final system is a REST API. It integrates with any modern software, from your internal CRM to a Google Sheet, using standard webhooks or API calls.
What Does the Process Look Like?
Scoping & Data Access (Week 1)
You provide read-only access to relevant data sources. We conduct a 2-day data audit and deliver a fixed-scope technical proposal outlining the build.
Algorithm Development (Week 2)
We build and test the core logic in a development environment. You receive a weekly progress report with key performance metrics from the model.
API Deployment (Week 3)
We deploy the model as a production-ready API on AWS Lambda. You receive API documentation and a staging URL for your team to test.
Monitoring & Handoff (Week 4+)
We monitor the live system for performance and accuracy. After 30 days, we deliver the final code repository and maintenance runbook.
Frequently Asked Questions
- How much does a custom algorithm cost?
- Cost depends on data complexity and operational constraints. A demand forecast using a single clean data source is straightforward. A route optimization algorithm with multiple vehicle types and real-time traffic data is more complex. We provide a fixed-price quote after a free 2-day data audit so you know the exact cost before committing. To discuss pricing, book a discovery call at cal.com/syntora/discover.
- What happens when the API or model fails?
- The API is deployed across multiple availability zones on AWS for high uptime. The system is designed with fallbacks; for example, a route optimization failure might default to a simpler distance-based route instead of stopping operations. We get an immediate alert on any failure. Our standard support plan guarantees a 4-hour response time for any critical production issues.
- How is this different from hiring a freelance data scientist on Upwork?
- Freelancers often deliver a Jupyter Notebook, which is a research artifact, not a production system. We deliver a production-grade API with monitoring, logging, and automated retraining pipelines. The person on the discovery call is the engineer who builds and supports the system. There is no handoff to a junior developer or a disappearing freelancer after payment.
- Can you use our existing cloud infrastructure?
- Yes. While our default stack uses AWS Lambda and Supabase, we are fluent in Google Cloud and Azure. If your team already has infrastructure on GCP Cloud Functions, for example, we will adapt and deploy there. We can deliver the solution as infrastructure-as-code using Terraform, making it portable and easy to integrate into your existing environment.
- What kind of business problems are not a good fit for Syntora?
- We are not a fit for large-scale data migrations, enterprise-wide digital transformations, or projects requiring teams of developers. We also do not build computer vision or large language models from scratch. We focus on specific, high-impact operational problems for small businesses where a single expert engineer can build and maintain the entire solution.
- How do you incorporate our team's domain knowledge?
- Domain knowledge is critical. The first week includes a deep dive with your subject matter experts. For a lead scoring model, we interview your top sales reps. For demand forecasting, we talk to your operations manager to identify seasonal trends and past promotions that are not in the raw data. This context is what makes a custom model outperform off-the-shelf tools.
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