AI Product Development Services That Reach Production in 90 Days.

AI product development services cover the strategy, product design, engineering, integration, evaluation, deployment and optimization needed to turn an AI capability into reliable software. CodeCones connects the user experience, models, data, APIs, cloud environment and operating controls so the product can work with real users and real production constraints, rather than stopping at a model experiment or demo.

We build AI-native SaaS products, copilots and assistants, enterprise search and knowledge products, document intelligence, workflow and decision products, and AI features for existing software. These are complete product experiences with integrations, evaluation and deployment controls—not isolated models or prototypes without a path to reliable operation.

Yes. We assess the prototype, data and product assumptions, then add the architecture, integrations, evaluation framework, governance, observability and reliability controls needed for production. We also plan deployment, rollback, documentation and handover so the result is maintainable software your team can operate, not a one-off demonstration.

Our 90-day positioning describes a focused path from a well-scoped opportunity to a production-ready AI product or product layer. Actual timing depends on scope, data readiness, integrations, product surfaces and risk controls. Discovery establishes the acceptance criteria and delivery sequence before we recommend a schedule.

Cost depends on product scope, data readiness, model approach, user-facing surfaces, integrations, risk and governance controls, performance requirements and post-launch support. A focused feature and an end-to-end AI product have different delivery needs. We clarify those drivers during discovery before recommending an engagement path.

Yes. We develop grounded LLM applications with structured outputs, model and provider evaluation, API and tool integration, guardrails and production monitoring. For RAG, we design ingestion, chunking, embeddings, hybrid retrieval, permissions, freshness, citations and real-query evaluation so generated responses are connected to the right business context.

Yes. CodeCones can own an end-to-end AI product build, or embedded AI product engineers can join your roadmap, codebase and sprint cadence. We agree responsibilities, access, technical standards, acceptance criteria and handover expectations during onboarding so the team model fits how your product organization already works.

Clients own project-specific deliverables, including code, model fine-tuning artefacts, documentation, evaluation datasets and infrastructure definitions, subject to the agreed treatment of pre-existing CodeCones materials, open-source components and third-party technology. Ownership is defined in the engagement agreement. Foundation model weights remain subject to their providers’ licensing terms.

Post-launch support can include monitoring, incident response, quality and cost optimization, model or provider changes, and roadmap support. We use the evaluation and observability foundations established during delivery to identify regressions, drift, latency and cost issues, while planned documentation and handover help your team operate the product confidently.