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Expertise · Scale with AI

Put AI to work on a defined problem.

A model demonstration is not yet an operating capability. We start with the decision or workflow, the available data and a baseline against which the result can be assessed.

Scale with AI in short

Scaling AI in production means starting from a defined decision or workflow, then engineering the model, its evaluation and its oversight around it. ARCHITEKT covers applied AI, agentic systems and AI engineering: serving, monitoring, change controls and failure responses are planned with the use, not added after a demonstration.

Specialties

Applied AI

Examine where a model can add useful capability, test the output against the existing approach and define the evaluation and oversight the use requires. The work connects product, data and engineering rather than treating the model as the whole solution.

  • Generative AI in product
  • Applied machine learning and predictive models
  • Big data platforms for AI workloads
  • MLOps and model lifecycle
  • RAG and knowledge retrieval
  • Copilots and workflow automation
  • Responsible AI and model governance

With partner specialists: Fine-tuning and model evaluation; and AI due diligence and asset valuation.

Agentic Systems

Design what an agent may read, decide and do, and when it must stop or involve a person. Tool permissions, evaluation, traceability and failure responses are part of the system alongside the workflow it supports.

We distinguish client references from practice developed in our own ventures. The relevant experience and its limits are identified for the proposed work.

  • Conversational agents and dialog design
  • Agent orchestration and multi-agent workflows
  • Tool use, function calling and MCP integration
  • Retrieval, memory and knowledge graphs
  • AI gateway and single-chokepoint architecture
  • Guardrails, prompt-injection defence and PII masking
  • Agent evaluation, golden sets and LLM-as-judge
  • Lineage, observability and AI audit trail
  • Human-in-the-loop and escalation design
  • Autonomy tiering and blast-radius control

With partner specialists: Agentic FinOps and model routing.

AI Engineering

Engineer model integration, evaluation and operation for a defined use. Serving, monitoring, change controls and failure responses belong in the delivery plan.

  • Model gateway and provider abstraction
  • Context and retrieval pipelines
  • Evaluation harness and release gating
  • Data platforms behind AI workloads
  • Model lifecycle and deployment
  • Conversational and assistant engineering
  • AI observability and lineage

With partner specialists: Cost, latency and model routing.

Related work

Work by the operators that used this expertise, before ARCHITEKT and in an ongoing mandate.

Our own platforms

ARCHITEKT builds these platforms on its own account, with Erwan Deschamps as Chief Technology Officer (CTO) and Anthony Chevalier as Chief Product Officer (CPO). They are not client work.

Engagement forms

Discuss the workflow you want AI to support

When the destination is a measured effect on business performance across several decisions, From Intelligence to Performance owns that outcome and applies this capability to each decision within it.

When the data a use depends on is not yet owned, governed or served, Data builds that foundation first.

Questions

Do you start from a model?
No. We start from the decision or workflow, the data available and a baseline for the current result. A model is chosen once the use, its evaluation and its oversight are defined.
Do you cover fine-tuning or AI due diligence?
With partner specialists. Fine-tuning and AI due diligence are brought in through partners when the work requires them, alongside our applied AI, agentic systems and AI engineering practice.
How does Scale with AI differ from Data?
Scale with AI engineers a model or an agent into one decision or workflow, with its evaluation, oversight and failure responses. Data sets the strategy and governance, builds the platform and pipelines and gives decisions trusted indicators. Where the data a use needs is not yet owned or served, that work comes first.