Your trusted partner in navigating the AI-first future — architecting AI-native solutions that make your business intelligent by design, not just by application. We engineer AI into the core of your digital operating model.
Every engagement is anchored in two deep capabilities — Data Engineering and AI/ML — delivered on the cloud of your choice. We don't sell hours; we partner to scale your AI mission.
From classical ML to generative AI, agentic systems, and LLM platforms — we build production-grade intelligence that drives measurable business outcomes.
The foundation of every AI initiative. We design real-time pipelines, lakehouses, and feature stores that turn fragmented data into decision-ready signal.
AWS, Google Cloud, and Azure — architected, migrated, modernized. Deep expertise across all three hyperscalers, with security and FinOps built in.
Full-stack product engineering when you need to build, ship, and scale. Polyglot teams that move with the rigor of a senior engineering org.
Augment your in-house engineering with senior AI/ML and data specialists who plug in seamlessly — accelerating delivery without the overhead of hiring.
Pre-built capabilities and reference architectures that compress time-to-value — RAG platforms, agent frameworks, and domain-tuned starter kits.
The overview is the headline. Here is the depth — the specific capabilities we champion and run end-to-end across Data Engineering and AI & ML.
The foundation under every AI initiative. We build the platforms that turn fragmented, messy data into a decision-ready, real-time signal.
Move data the moment it is created — not in nightly batches.
One governed platform for BI, data science, and AI — no silos.
Modeled, tested, and documented data — shipped on a schedule you trust.
Trustworthy data with lineage, contracts, and proactive monitoring.
From classical ML to generative AI and agents — we build production-grade intelligence that moves real business metrics, not demos.
RAG, agents, and copilots grounded in your data — built to be evaluated.
The workhorse models that quietly drive margin and decisions.
Models that stay reliable in production — long after launch.
Turn unstructured documents, images, and speech into structured signal.
A partnership built on engineering rigor — not slideware. Here's how we move from your AI vision to systems running in production.
We map your data landscape, business outcomes, and AI ambition. We surface where intelligence creates real leverage — and where it's a distraction.
Reference architectures grounded in security, observability, and FinOps. AI-native from day one — not retrofitted onto legacy systems.
Senior engineering pods with embedded AI/ML and data specialists. We measure progress in working systems, not Jira tickets.
MLOps, evaluation harnesses, and production support. We stay long enough for AI to compound — not a quarter, not a project.
A snapshot of partnerships where AI-native engineering moved from idea to measurable business impact.
Designed and shipped an LLM-powered shopping assistant grounded on the client's catalog, inventory, and order systems. RAG architecture with eval harness, latency budgets under 800ms, and guardrails for brand-safe responses.
Re-engineered the forecasting stack on a lakehouse architecture with feature store and automated retraining. Replaced spreadsheet-driven planning with hierarchical ML across thousands of SKUs and hundreds of nodes.
Built a streaming analytics platform on Kafka and Flink, with a feature store powering both real-time scoring and offline model training. Sub-second decisions on millions of transactions per day.
Modernized a legacy on-prem stack onto Azure with a HIPAA-aligned reference architecture. Layered an LLM-based document extraction pipeline with human-in-the-loop review and full audit trail.
The companies winning with AI aren't the ones with the most pilots — they're the ones treating AI as core engineering. That's the work we do, every day.
We don't ship and disappear. Your AI mission becomes ours — we stay through scale, iteration, and the messy middle where most engagements drop off.
Every pod is anchored by engineers with a decade-plus of production experience. No bench rotations, no junior bait-and-switch.
Pre-built accelerators, reference architectures, and a strong evaluation discipline let us move from prototype to production in weeks, not quarters.
Working systems demoed every two weeks. Honest tradeoffs, visible burn-down, and an open conversation about what's worth building — and what isn't.
Evals, observability, cost guardrails, and on-call rotations — every system we build is engineered to live in production, not just impress in a demo.
A senior leadership team with deep roots in engineering, AI/ML, and operating teams that have shipped at scale.
10+ years in talent leadership, formerly Head of Talent at Zeminent, where he scaled engineering hiring across global teams.
LINKEDIN12+ years in cloud architecture and software engineering. Previously led platform teams at Zeminent; AWS Certified Solutions Architect.
LINKEDINOperations leader specialising in scaling delivery teams and engineering processes. Former Director of Operations at Zeminent.
LINKEDIN10+ years in technical recruitment and team building. Previously led talent acquisition at Zeminent.
LINKEDIN10+ years building high-performing sales teams, increasing lead conversions, and executing data-driven marketing strategies.
LINKEDINGraduates of the Zeminent MERN Stack Bootcamp, on what changed once they started building for real — in their own words.
The bootcamp didn't just teach MERN — it taught me how a real team ships. Code review, Git discipline, deployment, all of it. I stopped feeling like a student halfway through Phase 2.
I came in knowing basic JS and left with a deployed full-stack app I still get compliments on in interviews. The placement guarantee is real — two offers before graduation.
Twelve weeks, three phases, zero fluff. The weekly builds forced me to actually finish things instead of tutorial-hopping forever. That habit alone was worth the fee.
I was a support engineer before joining. Within a month of finishing, I shipped my first production PR at my company. My mentor still answers my Slack messages at 11pm.
A 30-minute conversation is enough to know if there's a fit. No slides, no pitch — just a working session on where AI can actually move your numbers.
