Nuromind partners with research labs, Fortune 500 enterprises, and government agencies to design, build, and deploy production AI — from RAG and agents to bespoke fine-tuned models.
We don't hand you a deck and walk away. Each engagement is staffed by senior practitioners who write the code, run the evals, and own the deployment.
We translate your business objectives into a research agenda. Capability audits, roadmaps, build-vs-buy analysis, and executive briefings.
Production-grade retrieval pipelines over your private corpus. Vector + hybrid search, reranking, evals, and observability.
Multi-agent architectures with tool use, planning, and human-in-the-loop oversight. We design the failure modes before we ship the happy path.
LoRA, QLoRA, RLHF, and full fine-tunes against client-curated datasets. We benchmark, distill, and deploy under your latency budget.
Inference infrastructure that holds. GPU sizing, autoscaling, batching, monitoring, and the on-call runbook.
Generic models rarely survive contact with real data, real regulators, and real users. We build for the constraints that matter in your sector.
Medical imaging, drug discovery acceleration, clinical decision support, and patient-risk prediction.
Real-time fraud detection, risk scoring, regulatory monitoring, and personalized advisory agents.
Predictive maintenance, vision-based QA, demand forecasting, and supply-chain risk modeling.
LTV prediction, dynamic pricing, recommendation engines, and demand forecasting.
Smart-grid load balancing, renewables forecasting, asset performance optimization.
Citizen service automation, resource allocation, policy modeling, and public-safety analytics.
Four phases. Clear deliverables. No mystery work, no surprise invoices, no hand-waving in slide decks.
Two weeks of interviews, data review, and capability mapping. We surface what's tractable, what's not, and what's quietly dangerous.
We commit to a system design with explicit eval criteria. You sign off before a line of production code is written.
Senior researchers and ML engineers ship in two-week increments. Every change is benchmarked against the agreed evals.
Deployment, monitoring, the on-call runbook, and the slow handoff to your team. We exit when you're ready.
Findings, post-mortems, and the occasional opinion. Written by the people doing the work.
Everyone's building RAG systems these days, but most of them disappoint in production. After helping multiple teams get RAG right, here's what I've learned about the architectures, chunking strategies, and retrieval patterns that separate demos from real products.
AI agents are redefining enterprise operations by moving beyond simple automation into territory once reserved for human judgment — planning, reasoning, and acting autonomously across complex workflows.
Imagine a world where you could build sophisticated software that not only follows your instructions but also genuinely learns and adapts over time, all without you having to painstakingly pre-program every single rule and exception. That's not a futuristic fantasy; that's the tangible power of Large Language Models (LLMs), and it's fundamentally changing the landscape of software development — perhaps even rewriting the rules entirely.
Every engagement gets a small senior team — typically a research lead, two ML engineers, and a domain specialist. We don't pyramid; we don't hand off to juniors halfway through.
Discovery is 2 weeks. Most full builds run 12–20 weeks. Operate phase is open-ended and scoped to your team's readiness.
Both. Roughly half our clients have data residency or sovereignty constraints. We've shipped on AWS, GCP, Azure, and customer-owned GPU clusters.
You own the models, weights, datasets, and code. We retain only generic methodology. Everything else is yours.
Discovery is fixed-fee. Builds are typically time-and-materials with a not-to-exceed cap. Most full engagements land between $250k and $1.5M.
Send a brief and we'll respond within two business days with a recommended next step — usually a discovery call, sometimes a polite no.