Generative AI development company that ships to production

A generative AI development company designs, builds and operates products powered by large language or multimodal models. The work goes beyond calling a model API: it includes grounding the model in your data with retrieval, adding safety guardrails, building evaluation suites to measure quality, and operating the system in production. Resourcifi has done this work since 2017 with 200+ in-house experts.

We are model-agnostic. We work with frontier models from OpenAI, Anthropic and Google, and with open-weight models such as Llama or Mistral when you need on-premise or private deployment. We select the model per use case based on quality, cost, latency and data-residency needs, and we keep the architecture portable so you are not locked to one provider.

We ground responses in your own content using retrieval-augmented generation, so answers come from your sources and can be traced back with citations. On top of that we run a three-layer evaluation suite of reference, adversarial and regression checks, and apply output guardrails that block or reroute answers that fail accuracy or safety rules before a user sees them.

A scoped pilot typically runs a few weeks and produces a grounded prototype with an evaluation harness and a go or no-go recommendation. Full production builds are larger and milestone-based. Across our work the median time to a first deployment is 90 days, though the exact timeline depends on data readiness, integrations and the accuracy bar you need to clear.

Cost depends on scope. Work usually starts as a fixed-scope pilot, grows into a milestone-billed production build, and continues as a monthly retainer for operation and improvement. We scope exact pricing to your use case after the discovery call. As an offshore-based team, our blended rates run well below typical onshore rates while keeping a named in-house team on your build.

Before launch we agree on five production targets: first-token latency, cost per call, an answer accuracy floor, throughput at peak, and a recovery time objective. We measure each with an evaluation suite and gate the release against them, then track the same metrics in production with observability so quality, cost and latency stay visible after launch.

Yes. When data residency or security requires it, we deploy open-weight models such as Llama or Mistral inside your own cloud or on-premise environment, served efficiently with tooling like vLLM or NVIDIA Triton. We keep the same retrieval, guardrail and evaluation layers so the private deployment behaves like the hosted one.

We constrain outputs with input and output filters, PII redaction, persona limits and brand-voice checks using tools such as Guardrails.ai and NeMo Guardrails. Every interaction is logged for audit, and where the stakes are high we add a human review step. Brand-voice rules are written into the evaluation suite so off-brand responses fail before reaching a user.

We operate the live system: monitoring quality, cost and latency, updating models, tuning costs, and feeding production traces back into prompts, retrieval and fine-tuning. We can also add new use cases over time. This is offered as a monthly retainer, and 95% of our clients return for repeat work.

Traditional machine learning trains a model on your data to predict or classify a specific outcome, such as a fraud score or a demand forecast. Generative AI uses large pre-trained models to produce new content like text, code or images, usually grounded in your data through retrieval rather than trained from scratch. We build both and often combine them, using classic ML for ranking and prediction alongside generative models for content and conversation.