A lead on your hours
One point of accountability for scope, risk and budget. Works Eastern Time, sits in your steering meetings, owns the go / no-go calls.
AI engineering · US-facing · Engineering in France
AI, delivered
in production.
Plenty of firms will sell you a deck about artificial intelligence. We write the agents, prepare the data underneath them and run them inside your systems — with a senior team, part of it in France, at a lower blended cost than a US-only bench.
- ProductionWhat runs reliably and securely in your environment is our only measure of success.
- Your stackYour cloud, your code, your models. Open, documented components — no lock-in.
- Short phasesEach phase delivers something you can verify before the next one is committed.
The reality check
Most enterprise AI never leaves the pilot.
Endless proofs of concept, data that is not ready, and nobody accountable for running the thing on Monday morning. The model was never the hard part.
We start from one business problem, connect to the systems you already run, and stay until your team can operate it without us. Code, not slides.
The delivery model
One accountable team. Two time zones.
You deal with one engagement lead on your hours. Behind them, a senior engineering team — part of it in France — that keeps building while New York and Miami sleep.
Engineering in France
Senior engineers from France's leading engineering and business schools. Three hours of live overlap with ET every working day, and progress waiting for you each morning.
A lower blended rate
Senior delivery without a US-only cost structure. Fixed-scope phases, priced before they start, so finance knows the number before engineering spends it.
- 03:00 ETFrance starts building on yesterday's decisions
- 09:00 ETLive overlap — review, decide, unblock
- 12:00 ETYour lead carries the afternoon with your teams
- Next dayShipped work, logged and testable, in your environment
The fmetrik method
Every step produces structured data — not a document.
An agent is a model given memory, skills and the means to act. We wire it into your tools, your rules and your data, and every step it takes leaves a record you can query, audit and measure.
- 01SourcesEmails, PDFs, ERP and CRM exports, databases
- 02Clean & connectRaw sources become data you can rely on
- 03AgentMemory, skills and tools exposed through MCP
- 04Human approvalYour people sign off before anything moves
- 05Action & recordThe task is done, and logged as structured data
The model reasons.And can change provider without rebuilding the system.
Memory gives context.History, rules, internal knowledge.
Skills define the work.Business methods, scripts, controls.
Connectors act.Inside your tools — under human control.
Where we work
Three domains where AI pays for itself.
Spend · procurement · finance
See where spend leaks, and recover it.
- Agents that read contracts and invoices and flag every discrepancy
- Supplier, contract and spend data reconciled in one base
- Alerts your finance team can act on, with the source attached
Operations
Agents that run real workflows.
- Document intake, support, reporting and back-office follow-up
- A request in, a checkable result out — approved by a human
- Connected to the tools your teams already use
Data
Data your AI can trust.
- Files, CRM, ERP and databases gathered, cleaned and connected
- Target architecture and data governance, written down
- Deployed in your cloud, on open and documented components
Selected work · anonymised
Systems in production, not demos.
Our clients trust us with their data and their critical processes. We do not name them here.
A European grocery retailer
A data and AI cockpit for indirect procurement
Contracts, suppliers, spend and alerts reconciled in a single base, so buyers see where money goes and what to renegotiate.
A European real-estate investment manager
Target data architecture and governance
The data model and governance for the property portfolio, then a mapped and prioritised set of AI use cases built on top of it.
A real-estate asset manager
An agent for inbound deal flow
Opportunity files sent by brokers are captured from email, extracted and structured into the CRM — no more manual re-keying.
How an engagement runs
Short decisions, each one backed by proof.
- 01
Frame
Business problem, data sources, users, risks and the metric that defines success. You leave with a roadmap, a target architecture and a prioritised backlog.
- 02
Prove
A prototype wired into a real scope, to measure the value before scaling it. Ends in a go / no-go decision.
- 03
Deploy
Connectors, security, testing, observability and change management — inside your environment, under your identity provider.
- 04
Evolve
Usage measured, skills sharpened, new workflows added, and a handover to your teams. We leave when you no longer need us.
Inside your stack
No vendor lock-in.
We build on the cloud, models and systems you already run. The reasoning layer can change provider without rebuilding anything.
OpenAI
Anthropic
Mistral
Google Cloud
Azure
AWS
Snowflake
Databricks
Salesforce
SAP
PostgreSQL
Python
Next step
Ready to go to production?
Tell us about one workflow that costs you time or money. We come back with a scoped assessment: what to automate, what it takes, and what it is worth.
Thank you — we'll get back to you.
Meetings in New York and Miami on request.