Build smarter. Ship faster. Prove it.
Ninestack ships AI systems that hold up in production. One team takes the engagement from first call to release — strategy, build, deployment, and the number on the other side.
Nine ways we ship AI.
From the first feasibility call to the system humming in production. Each engagement names the work, the timeline, and the result you can check.
AI Consulting
Pick the AI work worth doing. We score use cases by ROI, name the constraints, and hand back a build plan with dates — not a deck.
Read the briefAI App Development
Mobile and web apps with ML in the core path — designed for inference latency, model failure, and the ship date you already committed to.
Read the briefRecommendation Engine
Recommendation systems that move the conversion number — collaborative filtering, content embeddings, real-time signals, and a measured lift on every release.
Read the briefChatbot Development
Chatbots that answer from your actual data — multi-turn context, knowledge-base retrieval, and a clean handoff to a human when the question goes off-script.
Read the briefAI Agents
Agents that plan, call tools, and finish the task — with audit trails on every decision and a human-in-the-loop step where the stakes warrant it.
Read the briefRAG Solutions
RAG that cites the source — hybrid retrieval, learned re-ranking, prompts that admit uncertainty, and answers your team can audit.
Read the briefAI Automation
Automation for the work rules engines couldn't handle — document parsing, judgement decisions, exception routing — measured against the manual baseline.
Read the briefAI Products
From validated problem to live product — strategy, UX, ML, and engineering on one team, with a market-fit signal before we commit to scale.
Read the briefLLM Customization
Adapt a foundation model to your domain — prompt design, RAG, LoRA, full fine-tune. We pick the technique your eval says works, not the one in the headlines.
Read the briefStrategy in the build, not above it.
We do not advise from the sidelines. The same team that scopes the work writes the code, runs the migrations, and ships the model. One engagement, one accountable team, one number to point at when it’s done.
That changes what a recommendation costs us. Every roadmap we hand back is one we have already pressure-tested against your stack, your data, and your release window. No theatre, no slideware.
The result: AI systems with a release date and a measurable lift. Sales up, support time down, error rate cut by a number you can audit.
Where we have shipped.
Eleven sectors, one rule: the sector lead has shipped in it before. We don’t consult our way to domain knowledge.
Pick the work. Set the date. Ship.
Tell us the system you need, the constraint that’s blocking it, and the date you want it live. We’ll come back with a scoped plan inside one business day.