Large language models are easy to demo and hard to run well inside a real business. The difficult part is rarely the model itself; it's connecting it to your data, your software and your processes in a way that is secure, predictable and affordable. Midnight Bytes is an AI and LLM integration consultancy that does exactly that work.
Our approach: start from the system, not the model
We start from the business system the model has to fit into, not the model. We choose models suited to the job, connect them to your existing data and software, keep data handling secure, and build the integration so it keeps working as usage grows and the model landscape changes.
In practice that means understanding the workflow first: who uses it, what data it touches, what a wrong answer would cost, and how success will be measured. Only then do we choose the model, the architecture and the guardrails.
What we build
- LLM features inside existing products — assistants, summarization, drafting, classification and extraction built into the software your team and customers already use.
- Retrieval over your own data — connecting models to documents, databases and knowledge bases so answers are grounded in your information rather than the model's guesses.
- Workflow automation — AI steps inside business processes, with people kept in the loop where judgment matters.
- Media and content pipelines — AI-assisted processing of text, audio and video as part of larger production systems.
- Integration plumbing — the APIs, queues, webhooks and background jobs that make AI features reliable in production.
Security, privacy and cost
AI integration done carelessly can leak data, run up surprise bills, or produce answers nobody can explain. We design for those risks from the start: controlling what data leaves your systems, keeping credentials out of client code, logging what the model was asked and what it returned, and putting limits on usage and spend. Where your industry has compliance requirements, we build the integration to meet them. See also our cloud and DevOps work, which usually hosts these systems.
Built to last as models change
The model landscape changes month to month. We keep integrations loosely coupled to any one provider so you can switch models as prices, capabilities and terms change, and we add evaluation so you can tell whether a change made things better or worse.
How a project runs
- Discovery — map the workflow, the data and the risks; agree what success looks like.
- Prototype — prove the approach on real data before committing to a full build.
- Build and integrate — production-quality integration with your systems, tests and monitoring.
- Support — post-launch support and maintenance, including model updates.
If you need help deciding where AI belongs in the business before building anything, our fractional CTO service covers that strategy work, and our data engineering practice gets your data ready for it.
Related work
Bring AI into your systems
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