01 / Pillar
AI & Machine Learning
From classical ML to generative AI, agentic systems, and LLM platforms — we build production-grade intelligence that drives measurable business outcomes.
GenAILLM AppsAgentsMLOpsComputer VisionNLP
AI-First Engineering Partner
Engineeringintelligenceinto your core.
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.
Engineering across the modern AI stack
AWSGoogle CloudMicrosoft AzureDatabricksSnowflakeOpenAIAnthropic
What we do
Two pillars. One outcome.
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.
02 / Pillar
Data Engineering
The foundation of every AI initiative. We design real-time pipelines, lakehouses, and feature stores that turn fragmented data into decision-ready signal.
LakehouseStreamingETL/ELTData MeshSparkKafka
03 / Foundation
Cloud Solutions
AWS, Google Cloud, and Azure — architected, migrated, modernized. We bring deep expertise across all three hyperscalers, with security and FinOps built in.
AWSGCPAzureKubernetesServerlessFinOps
04 / Delivery
Software Engineering
Full-stack product engineering when you need to build, ship, and scale. Polyglot teams that move with the rigor of a senior engineering org.
React/NextNodePythonGoMobileAPIs
05 / Engagement
Embedded AI Teams
Augment your in-house engineering with senior AI/ML and data specialists who plug in seamlessly — accelerating roadmap delivery without the overhead of hiring.
ML EngineersData EngineersArchitectsSREs
06 / Accelerators
AI Accelerators
Pre-built capabilities and reference architectures that compress time-to-value — RAG platforms, agent frameworks, and domain-tuned starter kits.
RAGAgentsEval HarnessesTemplates
Capabilities we own
Beneath the pillars, deep capability.
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.
Pillar 01 — Data Engineering
Data Engineering
The foundation under every AI initiative. We build the platforms that turn fragmented, messy data into a decision-ready, real-time signal.
01 / 04
Real-Time Pipelines & Streaming
Move data the moment it is created — not in nightly batches.
- Event streaming & change-data-capture from operational systems
- Exactly-once, sub-second pipelines for analytics and ML features
- Backfill + replay strategies that keep history and live data in sync
KafkaFlinkSpark StreamingCDCKinesis
02 / 04
Lakehouse & Data Platforms
One governed platform for BI, data science, and AI — no silos.
- Medallion (bronze/silver/gold) lakehouse architecture
- Open table formats for ACID, time-travel, and schema evolution
- Cost-tuned storage & compute with workload isolation
DatabricksSnowflakeDelta LakeIcebergBigQuery
03 / 04
Transformation & Orchestration
Modeled, tested, and documented data — shipped on a schedule you trust.
- ELT modeling with dimensional and data-vault patterns
- Version-controlled transforms with CI and automated tests
- Dependency-aware orchestration with alerting and SLAs
dbtAirflowDagsterSQLData Mesh
04 / 04
Governance, Quality & Observability
Trustworthy data with lineage, contracts, and proactive monitoring.
- Column-level lineage, cataloging, and discoverability
- Data contracts and quality checks enforced in the pipeline
- PII handling, access controls, and audit for compliance
Great ExpectationsLineageData ContractsGovernance
Pillar 02 — AI & Machine Learning
AI & Machine Learning
From classical ML to generative AI and agents — we build production-grade intelligence that moves real business metrics, not demos.
01 / 04
Generative AI & LLM Applications
RAG, agents, and copilots grounded in your data — built to be evaluated.
- Retrieval-augmented generation over your private knowledge
- Agentic workflows and tool-use for multi-step automation
- Eval harnesses, guardrails, and prompt/version management
RAGAgentsLLM AppsFine-tuningEvals
02 / 04
Predictive & Classical ML
The workhorse models that quietly drive margin and decisions.
- Forecasting, recommendation, and personalization engines
- Fraud, anomaly, and risk detection on streaming data
- Churn, propensity, and uplift modeling on tabular data
ForecastingRecSysAnomaly DetectionXGBoost
03 / 04
MLOps & Production Platforms
Models that stay reliable in production — long after launch.
- Feature stores and reproducible training pipelines
- CI/CD for models with automated promotion and rollback
- Live monitoring for drift, quality, and cost
MLOpsFeature StoreModel RegistryDrift Monitoring
04 / 04
Vision, NLP & Document Intelligence
Turn unstructured documents, images, and speech into structured signal.
- Intelligent document processing and OCR at scale
- Computer vision for inspection, detection, and classification
- NLP and multimodal extraction, search, and summarization
Computer VisionNLPIDP / OCRMultimodal
How we partner
From ambition to production.
A partnership built on engineering rigor — not slideware. Here's how we move from your AI vision to systems running in production.
01 — Discover
Listen, then frame.
We map your data landscape, business outcomes, and AI ambition. We surface where intelligence creates real leverage — and where it's a distraction.
02 — Architect
Design for production.
Reference architectures grounded in security, observability, and FinOps. AI-native from day one — not retrofitted onto legacy systems.
03 — Build
Ship in tight loops.
Senior engineering pods with embedded AI/ML and data specialists. We measure progress in working systems, not Jira tickets.
04 — Scale
Own the long arc.
MLOps, evaluation harnesses, and 24/7 production support. We stay long enough for AI to compound — not a quarter, not a project.
10+
Years of senior engineering experience across the team
3
Hyperscaler clouds
AWS · GCP · Azure
24/7
Production support & on-call coverage
100%
AI-first engagement mindset, every project
In production
Stories from the build floor.
A snapshot of partnerships where AI-native engineering moved from idea to measurable business impact.
Retail · GenAI
Conversational commerce assistant for a multi-brand retailer
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.
3.4×
Conversion uplift
62%
Support deflection
11wk
Idea to production
Global Supply Chain · ML
Demand forecasting & inventory optimization at scale
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.
28%
Forecast error ↓
$14M
Inventory freed
15min
Replan cadence
FinServ · Data Engineering
Real-time risk & fraud signal platform
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.
<200ms
Decision latency
41%
Fraud catch rate ↑
99.99%
Platform uptime
Healthcare · Cloud + AI
Clinical document intelligence on a HIPAA-compliant cloud
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.
7×
Throughput
94%
Extraction accuracy
0
Compliance findings
Why Zeminent
Built for the AI-first decade.
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.
Partnership over project
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.
Senior engineers, not staff aug
Every pod is anchored by engineers with a decade-plus of production experience. No bench rotations, no junior bait-and-switch.
Speed without shortcuts
Pre-built accelerators, reference architectures, and a strong evaluation discipline let us move from prototype to production in weeks, not quarters.
Transparent & agile
Working systems demoed every two weeks. Honest tradeoffs, visible burn-down, and an open conversation about what's worth building — and what isn't.
Production discipline
Evals, observability, cost guardrails, and on-call rotations — every system we build is engineered to live in production, not just impress in a demo.
Leadership
The team behind the partnership.
A senior leadership team with deep roots in engineering, AI/ML, and operating teams that have shipped at scale.
Let's build
Ready to engineer intelligence
into your business?
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.