A custom AI development company that ships AI to production

An AI development company designs, builds and operates software whose core behavior depends on machine learning or large language models rather than hard-coded rules. The work spans data pipelines, model selection or fine-tuning, retrieval and orchestration, application engineering, evaluation and ongoing operation in production. Resourcifi covers all of these and focuses on the parts that decide whether AI ships, such as evaluation, cost control and data isolation.

Resourcifi builds generative AI features such as copilots and assistants, retrieval-augmented generation and semantic search, autonomous and tool-use agents, custom LLM applications and fine-tuning, plus the evaluation, observability and deployment work that keeps them running. We also recover AI features that stalled in proof-of-concept and finish them to a production standard.

We build on frontier models from OpenAI, Anthropic and Google, and use open-weight models such as Llama or Mistral when on-premise or data-residency needs require it. Our stack includes Pinecone and pgvector for retrieval, LangChain and LangGraph for orchestration, Hugging Face and PyTorch for custom models, and LangSmith, Weights & Biases and Evidently AI for evaluation and monitoring. We stay model-agnostic and choose per use case.

Our median time from kickoff to a first AI feature live in production is 90 days. A focused pilot can prove a single feature in roughly 6 to 8 weeks. Timelines depend on data readiness, integration surface and the accuracy bar the feature has to clear, all of which we scope during discovery and assessment.

We treat the deployment target as the design input. Before code is written we fix the p95 latency target, the cost-per-call ceiling, the accuracy floor and the data isolation rules, then build an eval suite that runs on every deploy and gates each release. A feature does not ship until it meets those budgets on representative and adversarial data, which is what separates a product from a demo.

We scope a cost-per-call ceiling at the start of the engagement and instrument spend per tenant from the first request. We use caching, model routing and fallback paths so heavy usage does not erode margin, and we model gross margin per feature before building. A feature that prices into negative contribution margin at expected usage gets re-scoped rather than shipped.

We design AI systems so one tenant's data can never reach another tenant's prompt context, using per-tenant retrieval indexes and scoped access rather than a shared store with filters. We add prompt-injection defenses on ingested content and keep audit logs showing which documents were retrieved for which inference. We configure model providers so customer data is not used to train shared models, and we fit the AI layer inside your existing compliance boundary.

Yes. A meaningful share of our work is taking AI features that worked in a demo but broke under real traffic, cost or edge cases, then finishing them. We re-scope to the actual deployment constraints, build the evaluation coverage that was missing, and bring the feature to a production standard your team can operate.

We start with the simplest approach that meets the accuracy and cost budget, which often means a strong base model with good retrieval and prompting. We move to fine-tuning, LoRA adapters or custom training only when the use case clearly justifies the added cost and operational overhead. The decision is driven by measured eval results, not by a preference for building from scratch.

Cost depends on the use case, data readiness, integration surface and the accuracy bar the feature must meet, so we price after a short discovery and assessment. Because we run an in-house team of 200+ experts, we can deliver senior AI work at rates roughly 70 percent below comparable onshore build cost. We scope a clear budget and timeline before the build starts.

Judge a custom AI development company on production evidence, not demos. Ask to see features running under live traffic, the eval suites that gate each release, and how cost per call and data isolation are handled. Confirm the team is in-house rather than a contractor bench, check independent proof such as a Clutch rating, and ask who operates the system after launch. Resourcifi is an AI development company founded in 2017 with 200+ in-house experts and a 4.9 Clutch rating from 21 verified reviews.

Build in-house when AI is core intellectual property and you can fund a standing team of ML, data and platform engineers plus the evaluation and operations work that keeps models accurate. Hire an AI software development company when you need a feature in production this quarter, want senior AI talent without a long hire cycle, or have a proof of concept that stalled. Many teams do both: an AI development company ships the first production features and hardens the operating discipline, then hands a system your own engineers can run.