AI integration services for products already in production
My AI integration services are for SaaS companies adding LLM features to a product that already has paying customers. The prototype works in a notebook. What’s missing is the architecture around it: evals, cost control, fallbacks, permissions and privacy, so the feature behaves in production the way it did in the demo.
I embed with your engineering team, build it in your codebase, and leave the team able to run it. We start with a two-week sprint.
The gap between a demo and a feature
A generative AI demo needs a prompt and an API key. A feature inside a live SaaS product needs answers to harder questions. What happens when the provider returns a 429 or times out at peak? Which customer data is allowed into the prompt, and who audited that? How do you know the new prompt is better than the old one? What does this cost per active user per month, and what stops one tenant running up a bill?
That’s the work of LLM integration services done properly. It’s mostly software engineering, and it’s where most AI features stall.
What I build around the model
- A thin provider layer. One internal interface for completions, structured output and embeddings, with the provider behind it. Swapping or adding a model becomes a config change plus an eval run, not a refactor. If you’re wiring a specific API, see ChatGPT and OpenAI API integration.
- Evals before launch, and in CI. A versioned dataset of real inputs with expected properties, graded by code where possible and by a model-graded rubric where not. Every prompt or model change runs against it. More in LLM evaluation.
- Structured output, validated. Ask for JSON against a schema, validate it, retry once, then fall back. Never let free text flow straight into your database.
- Cost control. Per-tenant token metering, budgets and alerts; prompt caching where the provider supports it; the smallest model that passes the evals; and caching of deterministic results. See LLM cost optimization.
- Latency budgets. Streaming for anything a user watches, background jobs for anything they don’t, and timeouts that trip a fallback rather than hang a request.
- Fallbacks. A second provider, a smaller model, a cached answer, or the non-AI path the product had before. The feature degrades; the product doesn’t break.
- Observability. Every call logged with prompt version, model, tokens, latency and outcome, so you can debug a bad answer three weeks later.
Data privacy and security
Sending customer data to a third-party model is a data-processing decision, not just an engineering one. I map which fields reach which provider, check the provider’s data-retention and training terms (and the regions they process in), and update your subprocessor list and DPA where needed. Sensitive fields get redacted or never leave your infrastructure.
Prompt injection is the other half. Any text a user or a third party controls – an email, a support ticket, a web page – can carry instructions. I treat model output as untrusted input, keep tool permissions narrow, and put guardrails where the model can trigger side effects. The LLM guardrails guide goes deeper.
Typical AI integrations
- Extracting structured data from documents, emails or uploads into your existing models.
- Summaries, drafts and classification inside an existing workflow – the user stays in control and edits the output.
- Search and question answering over product content, which is RAG development.
- Multi-step automations that call your APIs, which is AI agent development.
- Making your product usable by customers’ own AI tools through a custom MCP server.
Why work with me rather than an AI integration company
An agency will often build the feature in its own repo, on its own patterns, and hand it over. I work inside your codebase, in your review process, with your engineers – so the integration fits the architecture you already have and someone on your team understands it.
I’ve spent 20+ years shipping production systems, including platforms with millions of users, payment platforms and multi-tenant SaaS. At Pepkor NexTech I worked across engineering, architecture and cloud infrastructure while contributing to their AI journey. I also run Craaft, where AI agents and humans work the same board through MCP and a REST API, so I deal with agent permissions and agent-facing API design in my own product.
When this isn’t the right service
If you don’t yet know which AI feature to build, start with AI consulting or an AI readiness assessment. If the feature is a single API call with no customer data and no side effects, you probably don’t need an AI integration consultant – a solid senior engineer and a weekend will do.
Questions
What are AI integration services?
AI integration services put large language models and other AI capabilities into an existing product or workflow. Beyond the API call, that means evaluation, cost and latency control, fallbacks, permission-aware data access, privacy review and monitoring, so the feature is reliable in production.
How do you stop LLM costs from running away?
Meter tokens per tenant and per feature, set budgets with alerts, use the smallest model that passes your evals, cache repeated work and use provider prompt caching where available. Most cost surprises come from unbounded context or retry loops, so I cap both.
Will our customer data be used to train the model?
That depends on the provider, the API tier and your agreement with them. Most business API terms exclude training by default, but retention periods and regions vary. I check the current terms for your chosen provider and design so sensitive fields don’t leave your systems unless they need to.
What happens when the AI provider goes down?
The feature should degrade, not fail. I add timeouts, retries with backoff for transient errors, and a fallback: another provider or model behind the same interface, a cached result, or the original non-AI path.
Can you work with our existing engineers?
That’s the default. I embed for a fixed number of days a week, pair with your engineers and review their pull requests, so the team owns the integration once it ships.
Got an AI prototype that isn’t production-ready?
Show me what you’ve built. In a two-week sprint we put evals, cost tracking and fallbacks around it and get it in front of real users safely.
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