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AI Product Management

Choosing the architecture that will carry your AI system

An AI system rests as much on the choice of model and hosting as on the code around it. This mission sets the architecture before development starts: which model, which hosting, which data flows where, and what keeps the system from derailing in front of a user.

  • Model and hosting chosen with full knowledge
  • Server side guardrails
  • Reversibility and vendor independence

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Key challenges

An AI architecture gets decided before the first line of code, not along the way. The choice of model, its hosting and the corpus it relies on determines what the system will be able to do, what it is allowed to do, and what it will cost to run. Revisiting these choices once the system is in production costs far more than setting them early.

  • The model and its hosting

    A proprietary model reached through an API, a model hosted on a cloud in France or Europe, or an open source model run locally: each option commits the data, the dependency and the control of the system differently.

  • RAG and closed corpus

    A model that answers from a closed, verified documentary corpus states what the organisation actually knows, not what the model believes it knows. The quality of the corpus matters as much as the choice of model.

  • Engaging data kept out of the model

    A price, an availability, a contractual commitment should never be phrased by a generative model: they get computed server side, from a source of truth, and the model only relays them.

  • Observability and reversibility

    Without tracking the tokens consumed, the cost, the latency and the footprint of the deployment, nobody knows what the system actually consumes. Without an exit plan, the organisation stays captive to the first vendor chosen.

What your PO / PM does

  • 1

    Choose the model and the hosting

    Compare the available options, proprietary API, sovereign cloud, or local open source model, against the sensitivity of the data and the client's regulatory constraints.
  • 2

    Design the RAG and the closed corpus

    Define which documentary sources feed the system, how they are indexed and kept current, and how the model is constrained to answer only from that corpus.
  • 3

    Set the guardrails and the SI integration

    Keep engaging data out of the model, connect the system to the tools and databases already in place, and write down what the system is not allowed to produce.
  • 4

    Put observability in place

    Instrument the system to track tokens consumed, cost, latency and the footprint of the deployment, and document the exit path towards another model or another host.

What stays in your hands

  • Architecture diagram

    The diagram of the system: model, hosting, corpus, guardrail layer and connection points to the existing information system.

  • Documented corpus and RAG pipeline

    The list of sources retained, the indexing and update method, and the rules that keep the model within that corpus.

  • Specified server side guardrails

    What the system is not allowed to produce, and the server side mechanism that enforces it before an answer reaches the screen.

  • Observability dashboard

    Tracking of tokens consumed, cost, latency and the footprint of the deployment, along with the reversibility plan towards another vendor.

Expected results

  • A system that does not depend on a single vendor

    The call to the model stays abstracted in the code, which allows switching vendor or host without rebuilding the system.

  • Data that stays where it should stay

    The corpus, the conversations and the sensitive data are hosted according to the client's constraints, with verifiable traceability.

  • Visible, manageable consumption

    Tokens, cost, latency and the footprint of the deployment are measured continuously, rather than discovered on the bill.

How we move forward

  1. 1

    Framing the need and the constraints

    Map the data involved, the existing information system and the regulatory constraints before talking about any model.
  2. 2

    Choosing the model and the hosting

    Weigh proprietary API against sovereign cloud and local open source model, in light of what the framing revealed.
  3. 3

    Designing the RAG, the guardrails and the integration

    Build the closed corpus, keep engaging data out of the model, and connect the system to the tools already in place.
  4. 4

    Setting up observability and the reversibility plan

    Instrument the system's consumption and write down the exit path towards another vendor should conditions change.

Who it is for

This mission is for technical leadership and project owners who need to set an AI system's architecture before building it, or who want to check that an existing architecture does not lock them into a single vendor.

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Frequently asked questions

Should you choose a proprietary model or a local open source model?It depends on the sensitivity of the data and the project's regulatory constraints. A local open source model keeps the data on site, a proprietary model reached through an API brings other capabilities: the trade off is made case by case, not on principle.
What is RAG and a closed corpus?RAG means having a model answer from a verified documentary corpus rather than from its general knowledge alone. A closed corpus limits that corpus to the sources the organisation has retained, so the system states what it actually knows.
Why compute engaging data server side?A price or a contractual commitment phrased directly by a generative model stays unpredictable. Computing it server side, from a source of truth, guarantees the figure shown is correct before the model even relays it.
How do you stay independent from a model vendor?By abstracting the call to the model in the code rather than locking it to a single API, and by planning a fallback mode. That is what allows switching model or host without rebuilding the system.
What is observability for an AI system?It is the continuous tracking of tokens consumed, cost, latency and the footprint of the deployment. Without that measurement, nobody knows what the system actually consumes once it is in production.

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