Enterprise Data & AI Engineering

Zeraso

Building secure, scalable, intelligent enterprises.

Data engineering, Generative AI, and Agentic AI — engineered for security, guardrails, and production value.

Build AI that is secure. Build data platforms that scale. Build automation that creates business value.

We take organizations from strategy and experimentation to production-ready Data-to-AI systems — with governance, cost, and observability in the architecture.

What we do

A complete Data-to-AI engineering platform.

Three connected capabilities — so intelligence is never built on fragile data, and automation never runs without boundaries.

Enterprise AI

Secure, governed, observable Gen AI — from RAG and copilots to production applications with cost and compliance built in.

Modern Data Engineering

Scalable platforms on Spark, Delta Lake, Kafka, and Airflow — lakehouse foundations that power analytics and AI.

Agentic AI

Tool-using agents that plan, retrieve, and execute multi-step workflows — with human approval where risk demands it.

Selected architectures

Enterprise implementations senior leaders can stand behind.

Reference architectures delivered for regulated and data-intensive organizations — framed for executive outcomes: control, speed, cost, and production readiness.

ARC-01Global Financial Services

Governed Enterprise Knowledge Architecture

Production knowledge assistants across regulated lines of business — with zero ungoverned model access to customer data.

Executive challenge: Pilot Gen AI tools were leaking uncontrolled context into answers and could not pass risk review.

Time-to-answer: Minutes → seconds
Risk posture: Audit-ready by design
Ops model: Central governance, local delivery

Architecture spine 01

Identity & policy gateway
Permissioned retrieval (RAG)
Input / context / output guardrails
Model routing & cost controls
Full audit & observability plane

ARC-02Fortune Retail & E-commerce

Real-Time Lakehouse Decision Platform

A single lakehouse foundation feeding merchandising, pricing, and personalization — batch and streaming on one governed stack.

Executive challenge: Fragmented warehouses and overnight ETL left executives without same-day operational visibility.

Freshness: Overnight → near real-time
Platform cost: Consolidated & attributable
Reuse: One foundation, many products

Architecture spine 02

Kafka event ingestion
Cloud object storage + Delta Lake
Spark transforms & quality gates
Airflow orchestration
BI + AI feature serving layer

ARC-03Enterprise Technology Operations

Human-in-the-Loop Agentic Operations

Agents that investigate incidents and prepare remediations — with mandatory approval before any production action.

Executive challenge: On-call load and tribal knowledge made response quality uneven and hard to scale.

MTTR assist: Faster first diagnosis
Control: No unsupervised production changes
Adoption: Ops teams stay in the loop

Architecture spine 03

Agent orchestration & planning
Tool access to ITSM, logs, runbooks
Action guardrails & least privilege
Human approval checkpoint
Execution + post-action audit trail

ARC-04Multi-National Manufacturing

Legacy ETL to Open Data Platform Modernization

Retirement of proprietary ETL debt in favor of an open, portable lakehouse that finance and operations can trust.

Executive challenge: Vendor lock-in, rising license spend, and brittle batch jobs blocked AI and analytics investment.

Licensing: Reduced proprietary dependency
Delivery: Faster pipeline change cycles
Readiness: AI-ready governed data products

Architecture spine 04

Legacy source mapping & CDC
Spark / Delta modernization path
Data contracts & quality framework
Domain-aligned data products
Migration with parallel run & cutover

Architectures are presented as anonymized reference patterns. Engagement details are shared under NDA.

Security & guardrails

AI that knows its limits.

Enterprise AI is more than wiring an app to an LLM. We design for data security, privacy, governance, prompt injection, reliability, cost, and compliance — from the first architecture decision.

Layer 01

Input

Sensitive-data detection, prompt-injection defense, permission checks, and request validation before anything reaches a model.

Layer 02

Context

Employees retrieve only what they are authorized to see — retrieval scoped by role, tenant, and policy.

Layer 03

Output

PII filtering, business-rule checks, and structured response validation on every generation.

Layer 04

Action

Agents can prepare high-impact work; humans approve execution for finance, infra, and regulated workflows.

Data security

RBAC · PII detection · Masking · Encryption · Tenant isolation

Application security

AuthN/AuthZ · Secrets · API controls · Audit logging

Model security

Input/output validation · Endpoint control · Exposure limits

AI cost engineering

Performance + quality + security + cost.

Inference, tokens, embeddings, GPUs, and agent runs add up fast. We treat cost as an engineering problem — model routing, prompt and context optimization, caching, and usage attribution so simple work never burns advanced models.

Governance & observability

Enterprise AI should be measurable.

Latency and errors, token burn and hallucination signals, cost per workflow and human-intervention rate — visibility that creates a continuous improvement loop.

Modern data engineering

Open-source foundations that scale.

AI needs trustworthy data. We modernize legacy ETL and warehouses into lakehouse and streaming architectures — portable, governed, and ready for analytics and agents.

Apache Spark

Distributed ETL, large-scale transforms, batch workloads

Delta Lake

ACID lakehouse storage, schema evolution, time travel

Apache Kafka

Real-time ingestion, event-driven integration

Apache Airflow

Orchestration, scheduling, operational monitoring

Reference flow

Apps & DBs → Kafka / APIs / Batch → Object Storage → Delta Lake → Spark → Quality & Governance → Analytics / BI / AI

Legacy ETL → modern pipelines

Warehouse → lakehouse

Batch-only → streaming

Proprietary lock-in → open source

Manual ops → automated orchestration

Fragmented data → governed platform

Agentic AI

From answers to action — with control.

Agents that understand, plan, retrieve, reason, call tools, and execute — integrated with CRM, ERP, APIs, and data platforms, under guardrails and human-in-the-loop where risk is high.

Understand→Plan→Retrieve→Reason→Use tools→Validate→Execute

Data engineering agents

SQL assist, pipeline troubleshooting, data-quality investigation, metadata discovery.

Customer & ops agents

Knowledge retrieval, case classification, incident analysis, runbook-guided response.

Finance & BI agents

Invoice flows, reconciliation support, and natural-language answers over enterprise data.

High-risk default: AI recommends → human reviews → human approves → system executes— not unchecked autonomy.

The Zeraso approach

Modernize. Secure. Intelligence. Automate.

01
Modernize
Build a reliable, scalable data foundation.
02
Secure
Protect enterprise data and establish AI governance.
03
Intelligence
Introduce Gen AI and enterprise AI applications.
04
Automate
Deploy agents that turn intelligence into action.

Engineering first

Production implementations — not demos that die after the pilot.

Security by design

Access control, governance, and audit trails from architecture day one.

Cost-conscious AI

Model routing, caching, and observability so spend stays intentional.

Data + AI as one discipline

Reliable data foundations feeding secure intelligence and automation.

Who we serve

Financial ServicesFinTechRetailE-commerceHealthcareManufacturingLogisticsSaaS

Engagements from defined projects and dedicated engineering teams to end-to-end ownership: discovery → architecture → build → deploy → optimize.

Contact

Tell us what you want to build.

Share your company context, the systems involved, and the outcome you need. We'll follow up from contact@zeraso.com with a clear next step.