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
Who this is for
- You want to add an AI feature to an existing product and need it to work reliably in production, not just in a demo.
- You have a proof-of-concept LLM integration that works fine until real users and real data show up.
- Your data isn't structured or accessible enough yet to support the AI feature you actually want to build.
- You need monitoring and retraining in place so the feature doesn't quietly degrade after launch.
What's included
- Use-case and data-readiness audit during discovery
- Data pipeline and vector store architecture design
- LLM or ML model integration, whether via API or a custom/fine-tuned model
- Production deployment, not just a working prototype
- Monitoring set up before launch, so degraded output gets caught rather than discovered by users
- Retraining workflow so the system keeps working as your data and usage evolve
- Documentation and knowledge transfer for your team
Tech stack
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.
Timelines
AI integration timelines swing more than most projects, mostly for one reason: data readiness. What actually drives it:
- Whether your data is already structured and accessible, or needs pipeline work first
- Whether the use case fits an existing LLM/ML model via API, or needs a custom or fine-tuned model
- How many existing systems the AI feature needs to integrate with
- How much production monitoring and retraining infrastructure already exists
Engagement models
We don't publish a fixed price because cost is driven almost entirely by how ready your data already is, which varies enormously between businesses — we scope it once we understand your use case and your data, not before.
A defined scope, deliverables, and timeline agreed upfront after discovery. Best when requirements are clear enough to lock in before work starts.
Ongoing development or support at a predictable monthly cadence. Best for continuous work — iterative feature development, DevOps support, or a system that needs regular attention.
Our engineers work as an extension of your team, on your timeline and tools. Best when you have in-house capacity but need to scale up quickly.
Case study
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.
How do you handle AI output quality and hallucination risk?
Production monitoring is part of every engagement specifically so degraded or incorrect output gets caught early rather than discovered by users. No integration eliminates this risk entirely — the goal is catching and correcting it fast, not pretending it can't happen.
Can you work with the LLM provider we've already committed to?
Yes — we integrate with the model or provider that fits your use case and existing commitments rather than pushing a specific vendor. Provider choice is discussed during discovery alongside your existing constraints.
Do you sign an NDA before discussing our use case and data?
Yes. We'll sign your NDA before any detailed discussion, or provide our own — data and use-case details are sensitive, and confidentiality is a standard part of scoping this kind of engagement.
Who owns the resulting model, pipeline, and code?
You do. Documentation and knowledge transfer are part of every engagement, so you own the result and aren't dependent on us for every future change.
What if our data isn't ready yet?
That's common, not disqualifying. Data-readiness is assessed during discovery, and if pipeline work is needed before the AI feature itself, that becomes part of the scoped engagement rather than a blocker we discover partway through.
Can you integrate AI into a legacy or non-cloud-native system?
Yes, though it usually means more data-pipeline work upfront to get your data into a state the AI feature can rely on. That scope is assessed during the data-readiness audit in discovery, not assumed to be trivial just because the request sounds simple.
Related reading
- RAG vs. Fine-Tuning: How to Choose the Right Approach for Your AI ProductRAG and fine-tuning get compared as if you have to pick one. In practice they solve different problems — RAG handles knowledge that changes, fine-tuning handles behavior that shouldn't. Here is how to decide which your project actually needs.
- How Much Does AI Integration Cost in 2026? A Realistic Pricing GuideAI integration costs range from about $10,000 for a single API-based feature to $500,000+ for a production agent platform. Here is what actually drives that number, and how to keep your project on the cheap end of its range.