AI application development services that ship to production

An AI application development company designs, builds and operates software whose core behavior depends on machine learning or large language models. At Resourcifi that spans generative and LLM apps, retrieval-augmented (RAG) tools, copilots, agentic applications and AI features embedded in an existing product, including the data pipelines, evaluations, observability and guardrails needed to run them in production.

Our median time to a first deployed feature is about 90 days. A fixed-scope assessment and roadmap usually takes one to two weeks, a working prototype follows in a few weeks, and the build, hardening and deployment fill out the rest. Timelines vary with data readiness and integration complexity, which we scope during discovery.

We start with a fixed-scope assessment and roadmap, then move to a milestone build or a dedicated team, so cost is tied to defined scope rather than open-ended. A focused proof of concept costs far less than a full production build, and a dedicated team is billed monthly. We give you a clear estimate after discovery, once the use case and integration work are understood.

We are model-agnostic. We use frontier models from OpenAI, Anthropic and Google, plus open-weight models such as Llama, Mistral, DeepSeek and Qwen when on-premise or self-hosting is needed. We pick the model per task based on quality, latency and cost, and design so you are not locked to a single provider.

Yes. A large share of our work is embedding AI features such as copilots, search and generative tools into an existing SaaS or enterprise application. We integrate through your current stack and APIs, and add evaluation and cost controls so the new feature scales without destabilizing what already works.

RAG, or retrieval-augmented generation, has the application retrieve relevant passages from your own documents and pass them to the model so answers are grounded in your data with citations. It reduces hallucination, though it does not eliminate it, which is why we keep evaluations and source citations in the loop. It fits when answers must reflect your specific content.

Traditional apps follow fixed rules, so the same input gives the same output and you test against known cases. AI application development centers on models whose output is probabilistic, so quality is measured with evaluation suites rather than assumed, and the build must handle retrieval, prompt and tool design, guardrails, cost per call and drift over time. The interface can look similar, but the engineering discipline underneath is different.

Every change runs through a three-layer evaluation suite in CI: reference checks against known-good answers, adversarial cases, and regression tests for silent drift. For safety we layer controls around the model, including input validation, least-privilege tools, output filtering and human review on irreversible actions, to address risks like prompt injection.

We agree a recovery time and runbooks before launch, and deploy with production tracing and cost monitoring in place. Because every step is traced, you can see where an answer or action came from and fix the root cause quickly, rather than guessing at a black box.

Yes. About a third of our AI engagements begin as production recovery, finishing a build a previous vendor could not deploy. We typically add the retrieval design, evaluation suite, guardrails and observability the prototype was missing, then get it operating dependably under real traffic.

Yes. You retain full ownership of the application, the source code and the intellectual property created during the engagement. We build on open standards and avoid proprietary lock-in, so you can host, extend or move the application without depending on us.