Custom AI solutions
We embed with your team and build AI systems for your workflows: a working prototype on your real data first, then production.
Trusted by
Forward-deployed engineering
We work inside your team, your tools, and your stack, not from the other side of a spec document.
We work inside your team
Your repos, your tickets, your standups. Decisions get made in days because the people building are in the room where the workflow lives.
We build in your infrastructure
Under your security and data constraints, so what we ship is something your team can run, audit, and extend after we leave.
We deliver in production
A demo is not the deliverable. We stay until the system runs in production, with evaluation, monitoring, and a clean handover.
Multi-agent systems, RAG, and workflow automation
Scoped to your workflow and your data, built to run in production.
Multi-agent systems
Specialized agents that plan, retrieve, calculate, and validate, coordinated into workflows too complex for a single prompt.
Assistants on your data
Retrieval systems (RAG) that answer from your own documents and databases, with citations and controlled responses.
Workflow automation
AI wired into your CRM, ERP, and internal tools, so structured work happens without manual entry.
AI products end to end
Full applications with AI at the core: backend, pipeline, guardrails, observability, and the app around it.
Built and shipped
A sample of public projects. Much of our work runs under NDA.
Multi-agent systemFinancial analysis agents for an enterprise planning platform
Pigment’s users needed to run complex, multi-step financial analyses that a single-agent chatbot couldn’t handle reliably. We designed and shipped a production multi-agent system where planning, retrieval, calculation, and validation agents coordinate each analysis, so enterprise teams reason over financial data reliably at scale.
Healthcare appA secure medical speech-analysis app for a national research institute
The CNRS TALanT research project needed to collect and process speech data from patient consultations under strict French healthcare-data rules. We built the iOS and Android app end to end: consultation recording, automatic transcription, OCR for clinical questionnaires, and report generation, on HDS-certified hosting with encrypted storage and GDPR-compliant audit logging.
An end-to-end AI pipeline inside a consumer app
Iris needed its first production AI service, from safety to generation. We shipped a four-block pipeline: guardrail classification, vision-based card analysis, user-context compilation, and structured reading generation. It runs with async orchestration, tracing, and prompt management, plus a back office where the team tests and debugs every block.
Workflow automationA CRM assistant the investment team talks to
Deal-flow data entry into the CRM was manual and easy to skip. We built a natural-language assistant connected to Attio through Telegram, so ISAI’s investors create, update, and route opportunities from where they already work. Structured data lands in the CRM automatically.
RAG & SEOAI-powered SEO for machine translation at scale
Automated translation had to stay accurate on technical terms without hurting search rankings. We prototyped and validated AI-driven SEO features: keyword extraction, SEO-aware translation that preserves domain terminology, and vision-generated alt text, with tracing on every LLM output.
NLP in productionThe RAG architecture behind a writing assistant
Generic grammar checking couldn’t cover the product’s needs. We designed the full RAG architecture for MerciApp’s conversational writing features: chunking strategies, embeddings, PGVector retrieval, reranking, and observability. The team shipped this blueprint into the live product.
From workflow to production
Discovery
Your workflow, data, and constraints analyzed to define the system worth building, and what it must prove.
Embedded build
A working prototype on your real data within weeks, built alongside your team and iterated in the open.
Production & handover
Shipped to production with evaluation, monitoring, and documentation. Your team keeps the keys; we stay as long as needed.
Questions from teams
What does “forward-deployed” actually mean?
We work inside your team for the length of the build: your repos, your tools, your meetings. You are not sending specs to a dev shop; the people building sit where the workflow lives.
How fast do we see something working?
A working prototype on your real data typically lands within 2 to 4 weeks. Production timelines depend on integrations and compliance, and we set them together at the scoping call.
Which stack and models do you use?
Yours first. We build in your infrastructure and pick models per task, cost, and data constraints: commercial APIs or self-hosted. We are not tied to any vendor.
Who owns the code and the system?
You do. Everything ships with documentation, evaluation, and handover. Nothing depends on us staying.
Can you work under strict data constraints?
Yes. We have shipped under French healthcare-data rules (HDS-certified hosting) and GDPR: encryption, audit logging, data residency, and deletion procedures are part of the build, not an afterthought.
What happens after delivery?
Your call. Some teams take the keys and run; others keep us on support and maintenance, or move straight to the next workflow.
Start with one workflow
A 30-minute build call: your workflow, your data, and what we would ship first. No obligation.
- 30 minutes
- You leave knowing what to build
You'll talk directly to Augustin or Robin, the co-founders.
Let's talk about your project
Pick a slot below. 30 minutes with a founder, no obligation.
Rather write than talk?
Send us a message. It lands in our inbox and we answer personally.