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Mayrian
AI Transformation

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AI Product Engineering

Once a use case is chosen, the work is building it to hold up in production: tested against agreed criteria, connected to your systems and monitored once it is live. AI Product Engineering covers that path, from proof of concept to operations.

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Services in this line

From proof to production

Success criteria met

Tests and controls pass

  1. 1Prove

    • AI Proof of Concept & MVP

  2. 2Build

    • Generative AI Development
    • Machine Learning Development
    • Intelligent Process Automation

  3. 3Operate

    • MLOps & AI Operations

Retrain and improve from operations back into the build

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Common challenges

Challenges we solve

  • Demos that don't survive real data

    A prototype works on a curated sample, then fails on the volume and variety of production data.

  • Quality nobody measures

    There is no test set or agreed measure, so nobody can say whether a change made the system better or worse.

  • Systems that drift after launch

    Accuracy falls as the data changes, and nobody notices until users stop trusting it.

  • Automation that breaks on exceptions

    Rules handle the common case, then every exception lands back on a person with no context.

A product team working around their laptops in a bright office

How to get started

Three steps to a plan for AI Product Engineering

  1. 1Tell us what you needUse the project form or book a call. A few sentences about the goal is enough to start.
  2. 2Free technical consultationWe go through your goals, users, existing systems and constraints with you.
  3. 3Your planA detailed plan covering the right tech stack, architecture, timeline and budget. Then you decide.
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Services

Five services, one standard

Every service is built and tested the same way, so a proof of concept can become a production system without starting again.

Will it work, and is it worth building?

AI Proof of Concept & MVP

A small working version with agreed success criteria, to test value and feasibility before a full build.

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What we do

  • Agree the hypothesis and success criteria
  • Build the smallest version that can test them
  • Evaluate it on your own data
  • Recommend go, iterate or stop

You receive

  • A working proof of concept or MVP
  • An evaluation report against the criteria
  • A go, iterate or stop recommendation
  • A production plan and estimate

Can it read, write and act on our content?

Generative AI Development

Assistants, search and agents built on large language models, grounded in your own content and tested before release.

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What we do

  • Index your content with its access rules
  • Build the assistant, search or agent
  • Test for accuracy, grounding and misuse
  • Integrate it with the tools people use

You receive

  • An assistant, search or agent in production
  • An evaluation test set and report
  • Security test results
  • Usage and cost monitoring

Can we predict it from our data?

Machine Learning Development

Prediction, classification and recommendation models trained on your data, validated on data held back and explained.

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What we do

  • Frame the prediction and its success measure
  • Prepare features and a simple baseline
  • Train and validate models on held-out data
  • Explain predictions and deploy the model

You receive

  • A validated model behind an API
  • A model card
  • An explanation with each prediction
  • Monitoring for accuracy and drift

Can the process run with less manual work?

Intelligent Process Automation

Processes redesigned and automated with rules, AI and human approval at the steps that need judgement.

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What we do

  • Map the process and its exceptions
  • Decide which steps are rules, AI or people
  • Build the workflow and its integrations
  • Route exceptions with their full context

You receive

  • An automated workflow in production
  • An exception-handling design
  • An audit trail for every case
  • Process measures before and after

Will it keep working once it is live?

MLOps & AI Operations

Deployment, monitoring, retraining and support for AI in production, with its cost and results tracked.

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What we do

  • Set up release pipelines with rollback
  • Monitor quality, drift, latency and cost
  • Retrain when thresholds are crossed
  • Respond to incidents

You receive

  • Release pipelines for models and prompts
  • Monitoring dashboards and alerts
  • A retraining process
  • Runbooks and regular performance reports

Choose an approach

Start from the problem, not the technology

Choose what you need to do to see the approach that fits and what has to be in place first.

The approach

A retrieval-augmented assistant

It finds the passages the person asking is allowed to see and answers from them, citing each source, so answers can be checked.

Needed first

  • Current, approved content with named owners
  • The access rules that apply to that content
Generative AI Development

Release gates

Before anything goes live

Every system passes the same five gates before release. A gate that doesn't pass stops the release, and the reason is recorded.

  1. Gate 1: Agreed success criteriaAccuracy, time and cost targets set with the business owner before the build starts.
  2. Gate 2: Evaluation on unseen dataTested on data the system hasn't seen, including the hard and unusual cases.
  3. Gate 3: Security testingPrompt injection, data leakage and permission checks for anything that reads content or takes action.
  4. Gate 4: Human approval pointsConsequential actions wait for a person's approval until the results justify otherwise.
  5. Gate 5: Monitoring in placeQuality, drift, latency and cost tracked from the first day, with alerts and owners.

Deliverables

What you receive

Across the five services. Each engagement delivers the parts that apply to it.

  • Proofs of concept and MVPs evaluated against agreed criteria
  • Assistants, search and agents grounded in your content
  • Validated machine learning models with model cards
  • Automated workflows with designed exception handling
  • Evaluation test sets and reports
  • Security test results
  • Release pipelines with rollback
  • Monitoring dashboards, alerts and runbooks

Questions

Questions about
AI Product Engineering

Do you build on our cloud and AI platforms?

Yes, where they fit the use case. We build on the cloud and model providers you already use, and record any change we recommend with the reason for it.

How do you know a generative AI system is accurate?

Each system has an evaluation test set agreed with your experts: real questions with known good answers. It is scored before release and again after every change to prompts, models or content.

What happens when a model gets worse over time?

Monitoring tracks accuracy and the data the model sees. When either crosses an agreed threshold, the model is retrained or rolled back, and the change is recorded.

Can our team take over after launch?

Yes. Code, prompts, evaluation sets and runbooks are handed over in your repositories and accounts, and we can train your team to run them.

How is a project priced?

Well-defined scopes are delivered as fixed-price engagements; when requirements are still evolving, we provide a dedicated team instead. Either way, the free technical consultation ends with a plan covering tech stack, architecture, timeline and budget, so you know the cost before work starts.

Ready to talk about AI Product Engineering?

Start with a free technical consultation: a plan covering the right approach, architecture, timeline and budget for your AI work.

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Two engineers reviewing code on a laptop and a monitor