02

AI products and features

AI-native product development

Design and ship a product whose core workflow depends on models, tools, data, and accountable user decisions.

Primary buyer: Technical founders and product leaders
01Primary buyerTechnical founders and product leaders
02Target outcomeA usable AI product with production architecture and measurable behavior
03Useful starting pointA product thesis, target workflow, representative users, and the riskiest technical assumption.
01

The real constraint

The feature must create user value while containing model latency, cost, failure, and product risk.

02

How we approach it

We design the user decision, model boundary, evaluation, application surface, and rollout as one product system.

03

How we prove it

A paid prototype measures user completion, model quality, latency, cost, and recovery before production scope.

Production system

AI products and features

Intelligence embedded in a useful product workflow, not isolated behind a chat box.

  • 01Product and workflow discovery
  • 02Model and tool orchestration
  • 03Evaluation and failure handling
  • 04Application UX and permissions
  • 05Deployment, telemetry, and rollout

Typical fit

A usable AI product with production architecture and measurable behavior

IndustriesSoftware / SaaSEnterprise teams
TechnologyLLMAgentsProduct engineeringAzure

FAQ

Questions before a pilot

These answers describe how we normally work. Only a signed agreement creates commitments. Website enquiry terms

01Can you work with a product that already exists?

Yes, and that is the normal case for this kind of work. Adding a model to a product with real users is a different problem from starting clean: there is an existing data model, an existing permission system, and users who will notice a regression immediately. We treat those as constraints from the first week rather than things to refactor around later.

02How do we know the model is good enough before committing to a build?

We build an evaluation set from your real cases and measure against it before the production work starts. That produces a number attached to a decision, good enough to ship, good enough with human review, or not good enough yet, instead of a demo that happened to work three times in a row.

03What if the model turns out not to be the answer?

Then we say so, and discovery has done its job. Some of the most useful outcomes of this phase are finding that a rules engine, a better interface, or a fixed data problem gets you further than a model would. You will have paid for a bounded piece of work instead of a year of production build on a bad premise.

Service focus

When the model is the product, not a feature

You have a thesis and a workflow that only works if the model behaves. We build the whole thing: the interface people actually use, the evaluation that tells you whether it is working, and the architecture that survives the first thousand users. The uncomfortable questions get answered early, not after launch.

Built for
Technical founders and product leaders
Designed to achieve
A usable AI product with production architecture and measurable behavior

What you get

  • Product and workflow definition with acceptance criteria
  • Evaluation set and measurable model behaviour
  • Production architecture with cost and latency budgets
  • A deployed, documented application you own

Need a different system?

Not quite your solution?

Explore nearby systems or bring the outcome, workflow, and constraints that do not fit a predefined category. Tandemora can scope a custom solution around the real operating problem.

Your version will have different constraints.

Bring the workflow, current system, available evidence, and the assumption you trust least. We will identify the smallest useful next step.

Discuss this system