AI Integration

AI integration is the work of connecting AI to the tools, data, and workflows your business already depends on, so it does real work in the place the work already happens. It is not a rip-and-replace, and it is not a chatbot bolted onto the side. Done well, it is mostly unglamorous plumbing that makes the clever part reliable.

Sixees Labs builds AI integrations for South African businesses that survive contact with real users. The demo is the easy part. Getting a model to behave on your data, inside your systems, under load, without embarrassing you, is the part that takes engineering judgement. If you are not yet sure which problems are even worth solving, that starts as AI consulting and becomes integration once the target is clear.

What AI integration involves in practice

Wiring models into your existing stack, your CRM, your records, your internal tools, rather than asking your business to reshape itself around a new platform.
Grounding the model in your own data so answers are about your business, not the open internet.
The boring foundations that decide whether it holds up: evals, observability, cost gates, and fallback paths for when a model misbehaves.
Choosing the right pattern for the job, a deterministic workflow where you need predictability, an agent only where the autonomy genuinely earns its keep.

Grounding a model in your own records only works if those records agree with each other, which is where AI governance and data foundations come in.

Build versus buy, decided honestly

Not everything should be built, and not everything should be bought. We help you draw the line where it actually belongs: buy the boring, commodity pieces, and build only the part that is genuinely yours. The result is less code to own, lower total cost, and engineering effort spent where it differentiates you. See our take on build vs buy.

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Frequently asked questions

What is AI integration?

AI integration is connecting AI to the tools, data, and workflows your business already runs, so it does real work where the work already happens. That means wiring a model into your existing systems, your CRM, your records, your internal tools, and grounding it in your own data, rather than standing up a separate AI product off to the side that nobody's daily work actually flows through.

How is AI integration different from building an AI product from scratch?

AI integration adds AI to systems you already run, whereas building from scratch creates a new product around the AI. Integration is usually the faster, cheaper, and lower-risk route for an established business, because it meets your people inside the tools they already use instead of asking them to adopt something new. Building from scratch only makes sense when the AI is the product itself, not an improvement to how existing work gets done.

Should we build or buy?

Buy the boring, commodity pieces and build only the part that is genuinely yours. Paying for the plumbing that dozens of vendors already do well leaves your engineering effort for the thing that actually differentiates you, and leaves you with less code to own and a lower total cost. The mistake is building everything for a feeling of control, or buying everything and finding your real advantage is now identical to a competitor's.

How do you keep an AI integration reliable in production?

Reliability comes from the boring foundations, not a better prompt: evals, observability, cost gates, and fallback paths for when a model misbehaves. You test against real cases before and after every change, you can see what the system is doing in production, you cap runaway spend, and you have a defined path for the model to fail safely rather than confidently inventing an answer. That plumbing is the difference between a demo and a system you can put in front of customers.

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If you have a model that works in a demo but not in production, or an integration you have been putting off because it looks fiddly, that is exactly our kind of problem.

Format 60-min call
Output Written summary
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