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/04 Service

AI & data engineering

We build data pipelines and AI features. Before launch, each feature gets an evaluation set, drift monitoring, an audit trail, and a cost budget.

/01 Problems and fixes

Where this work often fails, and how we prevent it.

  1. /01
    The problem
    Two reports show different numbers, because each team copies and cleans the data its own way.
    What we do
    We build one tested pipeline with written data contracts. Every report and model reads from the same validated source.
  2. /02
    The problem
    The demo looks good, but nobody can say how often the feature gives a wrong answer.
    What we do
    We build an evaluation set from real cases before launch. After launch, we monitor for drift and alert the team when quality falls.
  3. /03
    The problem
    A model makes a decision that affects a customer, and nobody can explain it later.
    What we do
    We log each input, output, and model version in an audit trail. Where the risk is high, a person approves the result before it takes effect.
  4. /04
    The problem
    Usage grows, and the bill for model calls grows faster than the revenue.
    What we do
    We measure the cost of each request and set budgets with alerts. We use caching and smaller models where their evaluation scores stay high enough.

/02 Deliverables

What this service gives you.

You receive

  • Tested data pipelines with written data contracts
  • An evaluation set and a baseline score for each AI feature
  • Quality and drift dashboards with alerts
  • An audit trail of inputs, outputs, and model versions
  • A human review queue for high-risk results
  • A cost report for each feature, with budgets and alerts

Typical work

  1. 01Document intake that extracts fields and sends unclear cases to a person
  2. 02Search over internal documents, with a link to each source
  3. 03A data warehouse that joins sales, product, and finance data
  4. 04A demand forecast with a weekly accuracy report
  5. 05Automatic routing of support tickets to the right team

/03 Ways to engage

Three engagement models.

  • /01

    Do you have a defined project?

    Project delivery

    • One senior team from the first call to launch
    • A signed definition of done for every milestone
    • Working software to review every week
    • Handover to your team, or Ongoing ownership by ours
  • /02

    Do you need more senior engineers?

    Dedicated team

    • Engineers picked for your stack and your industry
    • Daily work inside your tools and meetings
    • A monthly check on fit and results
    • Monthly changes to team size, with notice
  • /03

    Do you have a system that must not stop?

    Ongoing ownership

    • An assessment of the system before we accept it
    • A fixed monthly budget and agreed response times
    • Security patches, monitoring, and on-call coverage
    • A written report every month

/04 FAQ

Questions clients ask first.

Do you have a different question? Ask us directly

How do you pick a model?

We choose a model for each task, from hosted models to open models in your own cloud. The evaluation set decides, not the vendor. You can change the model later without a rebuild.

Does our data leave our environment?

Only with your approval. We can keep the data and the models inside your cloud account. We document each data flow and each third party that receives data.

How do you handle wrong answers from the model?

Every model is wrong sometimes. We measure how often, design the feature for that rate, and add human review where an error is expensive.

/05 Related

/ Next step

Talk to us about AI & data engineering.

Send a short note about your system and your constraints. We reply within one working day.

Start a project hello@aeontechnologies.io