Build AI that survives production. See the cost, progress, and proof.

A working, deployed MVP in 14 days through the fixed-scope AI MVP Sprint (from $5,500): scope frozen on days 1-2, working software by day 6, evaluation gates and cost telemetry by day 13, handover on day 14. Larger products run as phased builds with working software from week 2.

The 14-day MVP sprint is fixed-price from $5,500, and phased production builds start from $24,000. Ongoing teams run as senior AI pods with fully transparent per-role pricing: role compensation plus a flat, itemized 20% delivery layer, with the complete math in a public calculator. Full IP assignment in every contract.

Teams spending $20K+/month on LLM APIs or GPUs share their spend range, provider/workload, and stack in a 3-minute intake. NavyaAI replies with a written leak map showing where the bill inflates across agent loops, retries, routing, and idle capacity, and what to change first. No call required; our published case cut one bill from $47K to $28K per month.

Yes. We design, deploy, and operate private LLM and RAG stacks in your data center or your chosen colocation facility, so prompts, documents, and embeddings never leave your jurisdiction. EU data residency holds by architecture rather than contract clauses, and every tier is sized from our published benchmarks, from 8-watt edge boards to H100 clusters.

Every NavyaAI engagement runs with automatic AI progress tracking: an AI agent watches the repositories and task boards and sends you a daily plain-English report on what shipped, what's in flight, what's blocked, and spend against budget. Every task, commit, blocker, scope change, and spend variance stays visible, and calls happen when they're useful rather than as status theater. Combined with itemized invoices and benchmarks published with raw data, nothing about the engagement is hidden.

NavyaAI is a production AI engineering company: we build, optimize, and deploy AI systems (agents, RAG, private LLMs, and the inference infrastructure underneath) with evals, budgets, and observability engineered in. Unlike most AI engineering companies, the pricing, daily progress, and benchmarks are all inspectable.

Most clients are in the US, Canada, UK, and EU. Engagements run with deliberate timezone overlap for standups and demos, communication in your tools, contracts under mutually agreed jurisdiction with full IP assignment, and deployment in your cloud region, GDPR-aware by default for EU data.

Hardware-up engineering and published proof. The team spans transistor-level architecture to cloud-scale inference, and publishes benchmarks with raw data (158 tokens/sec on a $499 edge board, $0.47 per million tokens on an optimized 70B stack), including the configurations that failed. Every build ships with evaluation gates and cost telemetry.