DATANOMIQ — DATA · AI · ENGINEERING
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PRACTICE AREA

Software & Agentic Engineering

We deliver production software your teams can own, and we help them adopt AI-assisted coding — Claude Code, Cursor, cloud or on-premises LLMs — without trading speed for trust. The toolchain we teach is the one we use when we build for you.

AI-ASSISTED DELIVERY
~/project/feature-branch
claude review --diff
✓ 14 files reviewed, 2 suggestions
agent: drafting tests…
claude commit --push
✓ pushed to origin/feature-branch

AI-assisted engineering with guardrails — reviews, tests, and observability stay in the loop.

TYPICAL TIMELINE

6–12 wk

TEAM SIZE

2–5 ppl

STARTING POINT

Backlog review

DELIVERS

Shipped product

INSIDE THIS PRACTICE

What’s included.

01 / 02

Software Engineering

We design and build scalable, production-ready software around your constraints — products and integration layers your teams can own and extend.

  • Full-stack application development
  • API and backend systems
  • Cloud-native architectures on AWS, GCP, and Azure
  • Integration with existing systems
02 / 02

Agentic Engineering

AI-assisted coding with engineering discipline: Claude Code, Cursor, and similar tools to ship faster without dropping reviews, tests, or observability. We work with cloud-hosted or on-premises LLMs as policy requires, and we use the same toolchain when we deliver for you.

  • Claude Code, Cursor, and comparable assistants
  • Cloud and on-premises LLMs — latency, privacy, compliance
  • Shared context, specs, and review gates teams reuse
  • Hands-on enablement on your stack

TYPICAL SCENARIOS

The shapes of work we keep seeing.

Challenge & how we help

SCENARIO / 01

Legacy integration and strangler modernization

Challenge

Core systems are locked behind brittle point-to-point integrations; every change is a project.

How we help

API facades, event-driven seams, and incremental extraction with contracts your teams can evolve — co-developed with your platform owners.

SCENARIO / 02

Internal copilots and assistive workflows

Challenge

Support and operations drown in tickets, runbooks, and tribal knowledge spread across Confluence and chat.

How we help

LLM assistants grounded in your approved knowledge, retrieval you can audit, and human-in-the-loop for actions that need approval.

SCENARIO / 03

Shipping faster with AI-assisted coding

Challenge

Engineering toil — boilerplate, test gaps, repetitive reviews — eats the sprint.

How we help

Claude Code, Cursor, and similar tools with lightweight conventions, review gates, and lead-time metrics — so gains stick past the first sprint.

NEXT STEP

Ready to move faster? Let’s build it together.

Flip the switch