AI Integration
Integrate AI into your product with production-ready systems — not demos. We build the data pipelines, model serving infrastructure, monitoring, and retraining workflows your AI features need to run reliably at scale.
In short: Tech Programmer integrates AI into your product end-to-end — data infrastructure, model integration, monitoring, and retraining pipelines — not just a demo. The same team carries the work through deployment and ongoing maintenance using an Agile process.
What this means for you
- Covers the full lifecycle: data pipeline and vector store setup, LLM/ML model integration, production deployment, and ongoing monitoring/retraining — one team throughout.
- Built for production, not prototypes — includes the data infrastructure and monitoring an AI feature needs to keep working reliably at scale.
- Delivered in Agile sprints with regular demos, so you can validate the AI feature against real use cases as it's built.
- Cost and timeline depend on data readiness and how many systems the AI feature needs to integrate with — scoped during discovery.
- LLM and ML model integration
- Data pipeline and vector store setup
- Monitoring and retraining workflows
- Production deployment and knowledge transfer
How an AI integration engagement runs
The same four-phase process applies, with a data-focused discovery step: Discover (understanding your use case and auditing whether your data is ready to support it), Design (data pipeline and model-integration architecture), Develop (Agile implementation with regular demos), and Deploy & Support (production rollout, monitoring, and retraining workflows). Data readiness is addressed up front, since it's the most common reason AI projects stall after the build has already started.
Frequently asked questions
Do you build custom AI models, or integrate existing ones?
Both, depending on the use case. Most engagements start with integrating an existing model (LLM or ML) via API, since it's the fastest way to prove the use case works. Custom or fine-tuned models come into play when a use case specifically needs them.
Do you handle the data infrastructure, or just the AI feature itself?
We handle both. Production AI features need data pipelines and vector store setup to work reliably, not just the model call — that's part of the same engagement, not a separate project.
How much does AI integration cost, and how long does it take?
It depends on your use case and how ready your data already is — that's the single biggest driver. We scope cost and timeline during discovery once we understand the use case and your existing data, rather than quoting a number up front.
What happens after the AI feature is deployed?
Deployment isn't the end of the engagement — monitoring and retraining workflows are part of the same delivery, so the feature keeps performing as your data and usage evolve rather than degrading silently after launch.