Custom AI development services
Custom AI development is the design and engineering of AI systems built for one company's data, workflows, and goals — rather than off-the-shelf tools. IIInigence delivers this end to end: strategy, LLM and RAG engineering, integration with your stack, and production deployment with monitoring.
Most teams don't need another AI subscription — they need AI that understands their data, plugs into the tools they already run, and does real work reliably. That's what we build: production systems, not demos.
We work from strategy through deployment. That means scoping the highest-ROI use case first, engineering the model and retrieval layer, integrating with your existing stack, and shipping something your team can measure and trust.
Capabilities
What we build
LLM applications
Custom copilots, assistants, and internal tools powered by frontier models, grounded in your data and permissions.
RAG & knowledge systems
Retrieval-augmented generation over your documents and databases, with evaluation and guardrails so answers stay accurate.
AI integrations
Connect AI to the systems that run your business — CRM, ERP, support desk, data warehouse — via robust, monitored pipelines.
ML & data engineering
Custom models, classification, forecasting, and the data plumbing that makes them dependable in production.
FAQ
Custom AI Development — frequently asked questions
- How much does custom AI development cost?
- A focused, production-ready first use case typically runs from the low tens of thousands, depending on data readiness, integrations, and compliance needs. We scope a fixed-price discovery so you see the number before committing to a build.
- How long does a custom AI project take?
- A first production system usually ships in 6–12 weeks. We start with a narrow, high-value use case, prove it, then expand — rather than a multi-quarter big-bang project.
- Do you use our data to train public models?
- No. Your data stays yours. We architect for data isolation and can deploy within your cloud or VPC when compliance requires it.
- Should we build custom AI or buy an off-the-shelf tool?
- Buy when a mature product already fits the workflow. Build when the value depends on your proprietary data, your specific process, or deep integration — which is where generic tools plateau.
- Which models and frameworks do you use?
- We're model-agnostic and choose per use case — frontier hosted models where quality matters most, smaller or open models where cost, latency, or privacy dominate. We don't lock you into one vendor.
Related services
Let's scope your custom ai development project
A short discovery call, a fixed-price plan, and a first production result in weeks.