Redefining Technology

Full-lifecycle AI delivery

Enterprise AI services

Ten ways to put AI into production.

Atomic Loops offers ten enterprise AI services covering the full lifecycle of a production system: data infrastructure, analytics and forecasting, computer vision, document automation, conversational and outreach systems, MLOps, and AI product development. One team delivers all of them on your cloud, with a first production release inside 90 days as the default goal.

Browse the service catalogBook a consultation

The AI services in numbers

  • 10AI service lines in the catalogAtomic Loops service catalog
  • 6Practice areas, from data to productAtomic Loops service catalog
  • 5Delivery phases in every engagementAtomic Loops delivery method
  • 90Days to a first production releaseAtomic Loops delivery records

The catalog

The AI service catalog, in full

The catalog runs from data foundation to deployed product: ten services, each with what we build, what you keep, the system blueprint, and an honest delivery window. Pick a service on the left; every service links to a full page with its methodology, delivery plan, and FAQs.

Full Stack AI

End-to-End AI System

End-to-end AI system engineering is the design and delivery of a complete production AI stack — data ingestion, model development, serving infrastructure, and monitoring — built as one integrated system rather than a chain of disconnected tools.

What we build

  • Architecture assessment and target-state system design
  • Data pipelines feeding versioned feature and training stores
  • Model development with reproducible training and evaluation harnesses
  • Serving APIs with autoscaling, canary releases, and rollback
  • Full-stack observability: latency, cost, accuracy, and drift

What you keep

  • System architecture blueprint your team can review before build
  • A deployed production stack on your cloud account
  • CI/CD pipelines covering both code and models
  • Runbooks and a structured handover to your engineers

System blueprint

Operational sourcesERP, MES, and sensor data captured where the operation already runs
Ingestion pipelinesBatch and streaming loads with schema checks on every run
Feature & training storeVersioned features so every training run is reproducible
Model trainingTraining jobs pass evaluation gates before any promotion
Serving APIsAutoscaling inference with canary releases and instant rollback
MonitoringDrift, cost, and accuracy watched on every deployed model
End-to-End AI System — data flow, left to right. Feedback loops: Monitoring → Model training.

Effort split

Data engineering35%
ML modelling25%
Platform & MLOps25%
Enablement & handover15%
Typical pod allocation across the engagement. Source: Atomic Loops delivery records

Typical stack

  • Python
  • PyTorch
  • FastAPI
  • Kafka
  • PostgreSQL
  • Docker
  • Kubernetes
  • Terraform
Timeline
Architecture blueprint in 2–3 weeks; first production release inside 90 days.
Engagement
Fixed-scope discovery, then a build retainer with monthly production releases.
Industries
Manufacturing · Logistics · Energy · Enterprise SaaS
Explore End-to-End AI System

Time to production

Estimate your time to production

The 90-day default is a median, not a promise. Set the four sliders to match your situation, leave your details, and submit — your expected window, computed from the same delivery records, appears immediately.

3 / 5
3 / 8
1 / 4
2 / 4

Estimates derive from Atomic Loops delivery records. The number a two-week discovery produces is the one we commit to.

Choosing

How to choose an AI service

Choose by your starting point, not by technology: teams arrive with an operational problem, a data asset, or a product idea, and each entry point has a natural first service and a pilot that proves value before scale-up. When in doubt, a two-week discovery settles the question with evidence.

Delivery method

How every service is delivered

Every engagement follows the same five-phase method — discover, design, build, deploy, operate — so a computer-vision pilot and a data-platform build are governed the same way: evidence before build, gates before promotion, handover before exit.

  1. Discover

    Weeks 1–3

    Feasibility and data audit with the people who own the process, ending in a written verdict: build, fix the data first, or stop.

  2. Design

    Weeks 2–5

    Architecture blueprint, acceptance criteria, and a milestone plan your engineers review before anything is built.

  3. Build

    Weeks 4–10

    Short sprints behind evaluation gates: every model change passes its tests before promotion, and every sprint ends in something demonstrable.

  4. Deploy

    By week 12

    Production hardening on your cloud — CI/CD, rollback, monitoring — and a structured handover to your team.

  5. Operate

    Ongoing

    Drift and cost observability, retraining, and monthly improvement releases until your team takes over.

From long list to production

Candidate use casesEverything discovery surfaces, scored for feasibility and value
Scoped discoveryWeeks of evidence on the one or two that matter most
Pilot in productionOne line, queue, segment, or document class — with acceptance metrics
Scaled systemRollout of what the pilot proved, under monitoring
How scope narrows on the way to production: prove first, scale what the pilot demonstrated.

Frequently asked

AI services: frequently asked questions

These six questions are the ones buyers ask before a scoping call: what an AI project costs, how long production takes, where to start when the data is not ready, how we work with in-house teams, who owns what, and what happens after go-live. Service-specific questions are answered on each service page.

Ask about your projectBrowse the full FAQ

  • How much does an AI development project cost?

    Cost is set by scope, and scope is fixed before build: every engagement starts with a fixed-price discovery or proof-of-concept, so the first commitment is small and the feasibility verdict is real. Production builds are then quoted per milestone from the architecture blueprint, and the operate phase runs as a monthly retainer sized to the systems under management.

  • How long does an AI project take to reach production?

    A first production release inside 90 days is the default goal across all ten services. Focused builds move faster — a proof-of-concept lands in 3–4 weeks and a first automated document class in 4–6 — while multi-line rollouts, such as computer vision across several production lines, run twelve weeks and beyond.

  • Which service should we start with if our data is not ready?

    Start with data mining and warehousing: models built on scattered or untrusted records fail in production regardless of how good the modelling is. That first engagement delivers the governed warehouse and tested pipelines; analytics, forecasting, and LLM services then compound on one foundation instead of re-cleaning the same data per project.

  • Do you replace our in-house team or work with it?

    We work with your team and hand the system over — the model is enablement, not dependency. Your engineers join from the architecture phase, every deliverable ships with runbooks and documentation, and the operate phase exists to transfer ownership rather than to hold it.

  • Who owns the code, models, and data?

    You do. Code, trained models, prompts, pipelines, and documentation are delivered into your repositories and your cloud accounts, and your data never leaves your environment except through integrations you approve.

  • What happens after go-live?

    Every service ends in an operate phase: monitoring, retraining, and monthly improvement releases under a retainer your team can cancel once it has taken over. Models decay as the world changes — the operate phase exists so drift is caught by dashboards and alerts, not by your users.

Start

Start with a scoping conversation

Every service starts the same way: a scoping conversation about the problem, the data behind it, and what a production release must prove. It is technical, it is free, and it ends in a written recommendation — including, sometimes, the recommendation not to build yet.

Book a consultationEmail info@atomicloops.com

Related

Last updated: