Healthcare
PHI workflows, EHR integrations (Epic, Cerner, Athena), and clinical data pipelines.
Move From AI Experiments to Measurable Production Wins.
Logiciel helps enterprises operationalize AI - turning pilots, models, and LLMs into reliable production systems with real ROI.
For ambitious Head of AIs, the cost of slow ai implementation compounds fast - across reliability, hiring, and competitive position.
Our AI engineers ship production-grade implementations - covering data, models, evals, agents, observability, and the human-in-the-loop systems that make AI safe to scale.
A senior AI squad that owns architecture, evals, and deployment end-to-end.
Reference architectures for retrieval, agents, and fine-tuning that cut build time in half.
Production guardrails - eval pipelines, prompt versioning, observability, and cost dashboards.
Outcome-aligned engagements with milestones tied to model accuracy, latency, and cost.
We bring patterns and people who already understand the data, workflows, and compliance that shape outcomes in your sector.
PHI workflows, EHR integrations (Epic, Cerner, Athena), and clinical data pipelines.
MLS feeds, property data, transaction systems, and broker workflows.
SCADA, smart-meter data, grid telemetry, and outage management.
ledger-grade reliability, payment rails, KYC/AML, and audit-ready financial reporting.
Multi-tenant data isolation, usage-based billing, and enterprise SSO/SCIM.
Dedicated AI Pod: An embedded squad of ML, prompt, and platform engineers aligned to your roadmap.
AI Staff Augmentation: Plug specialist talent - agent engineers, MLOps, eval leads - into your existing team.
Outcome-Based Build: Fixed-scope delivery for a model, agent, or AI feature with success metrics defined upfront.
We map candidate use cases to value, feasibility, and risk.
We define data flows, model choices, and evaluation criteria before code ships.
Agile delivery with weekly demos, eval reports, and cost dashboards.
Shadow mode → limited rollout → full release, gated by quality and cost guardrails.
We run the system, train your team, or transition cleanly to in-house ownership.
AI Strategy & Roadmap: Use-case discovery, ROI modeling, and a 90-day execution plan.
LLM & RAG Engineering: Retrieval, fine-tuning, and prompt systems built for accuracy at production scale.
Agentic AI Development: Multi-step, tool-using agents with planning, memory, and safe execution.
MLOps & Eval Pipelines: CI/CD for models, automated evals, drift detection, and rollback workflows.
AI Governance & Safety: Policy, red-teaming, audit logs, and compliance scaffolding for regulated workloads.
AI Cost Optimization: Model routing, caching, batching, and FinOps dashboards to keep inference costs predictable.
Real frameworks from Logiciel teams shipping AI initiatives in production:
Evaluation Differentiator Framework: How leading teams turn AI evals into a competitive moat.
Agent to Agent Future Report: A field guide to multi-agent architectures heading into 2026.
We benchmark frontier and open-weight models against your evals - accuracy, latency, and cost - and recommend a portfolio rather than a single model.
Yes. We integrate with Snowflake, Databricks, AWS, Azure, GCP, and on-prem stacks. We do not require you to rip and replace.
Most pilots reach a usable prototype within 4–6 weeks and production rollout within 10–14 weeks.
Every shipped feature has an eval suite, retrieval grounding where applicable, and a human-in-the-loop fallback path.
Yes - once scope and evals are clear, we offer milestone-based fixed pricing for many builds.
You own everything we build for you, including prompts, fine-tunes, eval data, and infrastructure code.
We instrument every workload with token, latency, and dollar dashboards, and apply caching, routing, and batching to keep unit costs flat as usage grows.
Hire engineers who move fast, care about quality, and integrate like they are in-house.