Enterprise technology consulting

The engineering that gets AI, data and software into production.

Most enterprise programmes do not fail on strategy — they stall between a working prototype and a system people trust on a Monday morning. Cloudadorn builds the part in between: the platforms, pipelines, controls and release discipline that make it production. Our engineers do it inside your team, in your repository.

Talk with an engineer See how we build it

What we do

AI agents

Agents that act inside your systems through controlled tools, and stop at a person when the decision costs money.

Cloud & data

Snowflake and Databricks lakehouses, dbt models with tests, and migrations measured against the system they replace.

Apps & platforms

Products, APIs and Kubernetes platforms your own engineers can operate after we leave.

Embedded engineers

Forward Deployed Engineering: seniors in your squads, writing production code and handing it over.

All six practices →

Technology we build on

  • Snowflake
  • Databricks
  • AWS
  • Google Cloud
  • Azure
  • Kubernetes
  • Terraform
  • dbt
  • LangChain
  • Argo CD
  • Kafka
  • Salesforce

Product names are the trademarks of their owners. Listing them describes the platforms we deliver on; it does not imply a partnership or endorsement.

Two ways to start

AI agents

AI that runs inside your operations.

Twelve worked patterns — three-way invoice matching, governed natural-language querying, PII masking, lineage impact analysis — each drawn with the control that keeps it safe. Read them before you talk to us.

Browse the twelve patterns
Embedded engineers

Engineers who join your team.

Forward Deployed Engineering puts senior engineers in your squad with a named delivery charter: they commit to your repository, own the hard paths, and leave documented systems rather than a dependency.

How an FDE pod works
Our capabilities

Six practices, assembled to fit the problem

You do not buy all of these. You tell us what is stuck, and we bring the people who unstick it — usually two or three of the six at once.

PRACTICE 01

AI agents & workflow automation

Production agents perceive an event, decide with rules plus a model, and act through controlled tools. We build the parts that make that survive contact with real data: durable checkpoints, evaluation suites in CI, and a human approval step before anything moves money or touches a customer.

  • Tool calling, structured outputs, and sandboxed actions
  • Orchestration with LangGraph, LlamaIndex workflows, or a custom runtime
  • Human-in-the-loop routing to Slack or Teams, with an audit trail
See twelve worked patterns →
PRACTICE 02

Cloud data & lakehouse modernization

Moving off Oracle, Teradata or SQL Server onto Snowflake, Databricks or BigQuery with a parallel run and reconciliation, so the cutover is a measurement rather than an act of faith.

  • Migration, performance tuning, and cost controls
  • dbt models, tests, documentation, and semantic layers
  • Unity Catalog and Snowflake governance patterns where they apply
Talk to a data architect →
PRACTICE 03

Full-stack apps & microservices

Customer- and employee-facing products that stay fast and secure as usage grows — modern front ends over well-bounded services, with performance budgets set before the first commit rather than discovered in production.

  • React and Next.js portals and product interfaces
  • Services in Node.js, Python and Go; REST and GraphQL APIs
  • Authentication, multi-tenant isolation, and performance budgets
Talk to a full-stack engineer →
PRACTICE 04

DevOps & platform engineering

Making releases boring: infrastructure as code, containers, GitOps promotion between environments, and observability with cost guardrails — so a rollback is a revert, not a runbook.

  • Kubernetes on EKS, GKE or AKS
  • Terraform infrastructure as code; GitOps with Argo CD or Flux
  • CI pipelines, OpenTelemetry-style observability, FinOps basics
Talk to a platform engineer →
PRACTICE 05

Data products & native apps

Packaging data so other teams — or other companies — can consume it safely, inside the lakehouse when that is genuinely the right distribution model and not just the fashionable one.

  • Snowflake Native Apps and marketplace-style packaging
  • Secure sharing and clean-room collaboration patterns
  • Productized metrics, APIs, and access policies
Talk to an application developer →
PRACTICE 06

Forward Deployed Engineering (FDE)

Staff augmentation adds headcount without ownership. An FDE pod is accountable engineering inside your squad: production commits, the migrations nobody wants, and deliberate handover — so the capability stays when the engagement ends.

  • Embedded model with a written delivery charter
  • Architecture review and bottleneck removal
  • Paired work and documented knowledge transfer
How a pod is composed →

Not sure which of these you need? That is a normal place to start — describe the problem and we will tell you which practices it actually touches.

Describe your problem
Industries

The same engineering, adapted to how your sector runs

Controls, systems of record and risk appetite differ by sector. The patterns below are representative of the work, not a guarantee that yours will look the same.

Financial services & insurance

AP exception handling, continuous reconciliation support, and governed analytics for finance operations — with separation of duties designed in, not retrofitted.

Healthcare & life sciences

Document extraction, privacy-aware retrieval, and FHIR-oriented pipelines under least-privilege access and a business associate agreement.

Retail & consumer brands

Unified commerce and inventory data, demand signals, and one agreed definition of a metric across merchandising, finance and marketing.

Supply chain & logistics

Event-driven tracking, milestone exception alerts, and telemetry landed in the lakehouse where operations can actually query it.

Technology & SaaS

Multi-tenant platforms, API scale, and GitOps release paths your own engineers can run on a Tuesday without us.

Manufacturing & energy

Plant and field data ingestion, maintenance signals, and modernization of data estates that predate the cloud.

Solutions & accelerators

Starting points that shorten discovery — not magic timelines

Reusable scaffolding we adapt to your systems. How long any of it takes depends on data quality, access approvals and change control, so we scope after an audit rather than before one.

Finance & AP exception agents

Match invoices to purchase orders and receipts, package low-confidence cases with their evidence for a human approver, and write back only through a controlled ERP adapter — with a checkpoint before every mutation.

Lakehouse migration accelerator

Schema translation helpers, dbt scaffolding, and parallel-run reconciliation, so a Snowflake or Databricks cutover is measured against the source system rather than declared complete.

BI rationalization

Inventory the dashboards, certify the metrics that matter, retire the duplicates, and enforce row-level access — so that “revenue” means one thing in every meeting.

Kubernetes GitOps starter kit

Terraform modules, Argo CD or Flux promotion paths, secrets handling, and baseline observability, so a platform team has a golden path on day one instead of month four.

Each of these begins with an audit of what you already have. We will tell you if the accelerator does not fit.

Book an audit conversation
Forward Deployed Engineering

Embedded seniors who own outcomes

An FDE pod works inside your repository and your standups against a written delivery charter. The measure of the engagement is not hours billed — it is whether your team can run the system after we stop.

Access first — no waived controls

Access requests and tooling are the first workstream, so productive work starts as soon as your process allows — without asking security to skip a step for us.

Production ownership

Features, pipelines, reliability and handover documentation. If we are still indispensable after a year, we did it wrong.

Request an FDE conversation →
A Forward Deployed Engineering pod: two Cloudadorn engineers sit inside the client's squad alongside product, the client's own engineers and the data platform team, under a written delivery charter. The engagement runs in three stages — audit of architecture and bottlenecks, embedded delivery of production commits on hard paths, then handover through paired work and runbooks.
Cloudadorn Academy

Masterclasses, free to read

Long-form write-ups of how we actually build these systems — written so a non-engineer can follow the argument and an engineer still finds the specifics. No email address required to read them.

AI agent architecture 18 min read

Architecting Production AI Agents at Enterprise Scale

Why demos fail in production — and how to design agents with durable memory, tool-safe actions, small specialist agents rather than one super-bot, and exception routing that a person actually acts on.

Cloudadorn AI Engineering Practice
Foundational guide 16 min read

The 3 Pillars of Reliable Autonomous Agents

See → Decide → Do, explained without hype: perception adapters that reject garbage early, hybrid rules-plus-model logic that states its reasons, and sandboxed tools with typed outcomes and human gates.

Cloudadorn Technical Advisory
Production readiness 17 min read

From Prototype to Production: Hardening AI Agents for Real Reliability

The hardening checklist: input validation, checkpoints, spend limits, golden-set evaluations in CI, and staged GitOps release — reliability treated as an engineering discipline rather than a prompt problem.

Cloudadorn Platform & SRE Practice
DataOps & finance 18 min read

Autonomous DataOps & Finance: Shortening the Month-End Close

How a lakehouse, dbt and targeted agents support continuous reconciliation and AP exceptions, so close week becomes confirmation rather than discovery — and what to measure instead of trusting a vendor's benchmark.

Cloudadorn Data Modernization Practice
Certification tracks

Video tracks — in production, not yet published

We are recording guided video tracks mapped to real exam domains, with labs and notes on how each topic shows up in production work. None of them are live yet. One list, one status — tell us which exam you are preparing for and we will tell you where it sits in the queue.

In production Not published. No launch date until a track is ready to watch.

  • Snowflake — SnowPro Core & Advanced Architecture, security, performance and cost patterns as used on real lakehouse programmes — not exam dumps.
  • Databricks — Lakehouse & Data Engineer Spark, Delta Lake, Unity Catalog and pipeline design the way teams actually run them.
  • Cloud — AWS · Azure · GCP foundations Associate-level architecture, weighted toward networking, IAM and data services.
  • Kubernetes — CKA / CKAD-oriented platform track Workloads, networking, GitOps and troubleshooting on EKS, GKE and AKS.
  • dbt — Analytics engineering Models, tests, documentation and semantic layers for exams and delivery work.
Available now

Corporate upskilling

The one offering that is not waiting on a video queue: an FDE pod can teach while it ships, so the learning attaches to your systems rather than to a sample dataset.

Ask about Academy timing →
Careers

Join us — or bring our engineers onto your team

We hire senior engineers who like shipping and are willing to work in someone else's codebase. Through FDE those engineers sit with a client's product squad on applications, data platforms, DevOps and agents — in their tools, against their priorities.

Production work that matters

You own systems people depend on. No bench time, and no engagements that end at a slide deck.

Learn while building

Academy material, current agent and data practice, and colleagues who care about craft.

Open roles on LinkedIn → (opens in a new tab)

We post roles on LinkedIn rather than maintaining a separate listings page, so that page is the single source of truth for what is genuinely open right now. If nothing fits and you would still be a good fit, write to [email protected].

The shape of the team we are building

  • Data / analytics engineersSnowflake, Databricks, dbt, migration and reconciliation work
  • Platform & SRE engineersKubernetes, Terraform, GitOps, observability and cost
  • Full-stack engineersReact and Next.js over Node, Python or Go services
  • AI engineersAgent runtimes, evaluation harnesses, retrieval and tool safety

Every role is a Forward Deployed role: you will spend most of your time inside a client's systems, and you will be expected to leave them better documented than you found them.

Work with us

Tell us what you are trying to fix

Software delivery, a lakehouse migration, AI automation, or embedded engineers — a few sentences is enough to start. A senior consultant reads it and replies on business days. We do not run a mailing list.

  1. You send a short note. Five fields, four of them optional.
  2. A senior engineer replies — usually within one business day — with questions, not a brochure.
  3. A 45-minute call to work out whether this is a problem we are the right people for. Sometimes it is not, and we will say so.
Headquarters
11380 Southbridge Pkwy, Suite # 232
Alpharetta, GA 30022, USA
Phone +1 (678) 208 5818
Email [email protected]
Office hours Monday – Friday, 9:00 AM – 5:00 PM EST
Closed Saturday and Sunday
LinkedIn linkedin.com/company/cloudadorn-com (opens in a new tab)

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