AI inside your SaaS product.
Copilots, autosuggest, intelligent search, generation - embedded directly in your user interface.
You Don't Have an AI Strategy Problem. You Have an AI Integration Problem.
Logiciel ships AI into the products and platforms your customers and employees already use - not next to them.
After 70+ enterprise AI engagements, we see the same three failure patterns:
AI integration services exist to close all three. Strategy and model selection are commodities now. Integration is where the program either ships or doesn't.
When Logiciel delivers AI integration services, four things change about how your product behaves:
The AI lives where users already work. Inside Salesforce, inside Workday, inside your own platform, inside Microsoft 365 - wherever the workflow already happens. Zero context-switching.
The data is fresh, governed, and contextual. Real-time integration with your systems of record, with row-level permissions intact and audit trails preserved.
The model is replaceable. We integrate against an abstraction layer, not a single vendor. When the next foundation model is better or cheaper, you swap it without rewriting the integration.
The workflow has guardrails. Every consequential AI action runs through a policy layer your security team approves once - not per-feature.
That's the desired state. It's also the only state where AI shows up in your retention and revenue numbers instead of your "future roadmap" slide.
We see the same eight integration patterns across enterprise engagements. These are the ones that move metrics - not demos.
Copilots, autosuggest, intelligent search, generation - embedded directly in your user interface.
Salesforce, HubSpot, Microsoft Dynamics - lead scoring, next-best-action, account research, call summaries.
SAP, Oracle, NetSuite - invoice processing, vendor matching, anomaly detection, intelligent approvals.
ServiceNow, Zendesk, Jira Service Management - ticket triage, summarization, agent assist, deflection.
Snowflake, Databricks, BigQuery - natural-language query, semantic layer, automated insight surfacing.
Document intelligence, meeting summaries, structured outputs from unstructured inputs.
Property management, EHR, construction PM, loan origination - domain-specific integrations.
Multi-step automations that span the systems above, with governance built in.
(Reuse two case studies from the existing carousel, reframed for product/integration outcomes. Suggested copy for the design team:)
If product-integration-specific stories aren't yet published, default to the 7-Figure ARR AI augmentation case study with the framing: "Integration patterns transferable to enterprise B2B products."
Not a methodology. A method. Five concrete steps that take 8 to 16 weeks for a first integration.
We catalog the systems, data sources, user surfaces, and policy constraints in scope. Output is a written integration architecture, not a slideware diagram.
We stand up the integration plane - auth, data access, model abstraction, policy enforcement, audit. This is the work that makes every subsequent AI feature cheaper to ship.
We pick one user surface and ship a fully integrated AI feature into it. Usable, governed, instrumented.
Production observability, eval harness tied to the user outcome, and a staged rollout with feature flags.
Every subsequent integration costs a fraction of the first one because the platform layer is in place. This is where the program ROI compounds.
These two get confused. They're different engagements with different ROI shapes.
Most enterprise AI budgets in 2026 are being spent on the second one. The reason is simple: revenue lives in the products customers already use, and an AI integration program lifts that revenue without a new product launch.
AI integration services are engineering engagements that embed AI capabilities - large language models, classifiers, agents, recommendation systems - inside applications, platforms, and workflows your business already operates. Integration covers the data plumbing, identity, governance, user surface, and operational layer required for AI to behave as a first-class feature rather than a standalone tool.
An AI platform sells you the infrastructure to build with. AI integration services do the building inside your environment. Most enterprises end up needing both - a platform decision (model, vendor, hosting) and an integration program (how the AI shows up inside Salesforce, your product, your ERP, etc.). Logiciel typically integrates against whatever platform your team has already chosen.
A first integrated feature on a brand-new integration platform takes 8 to 16 weeks. Subsequent features cost a fraction of that because the platform layer is in place. Multi-system enterprise integrations - for example, AI inside Salesforce, SAP, and a customer-facing product - typically run as a 6 to 12 month program with multiple feature releases.
That's the actual point. Logiciel's integration pattern centralizes identity, data access, policy enforcement, and audit logging into a single layer that your security and compliance teams approve once. After that, every new AI feature inherits the same posture. We've shipped integrations under SOC 2, HIPAA, PCI, and FedRAMP-aligned constraints.
Yes. Logiciel's most common integration surfaces include Salesforce, SAP, Oracle, NetSuite, ServiceNow, Microsoft Dynamics, Workday, Snowflake, Databricks, mainframe-fronted APIs, and homegrown vertical platforms. Where modern APIs don't exist, we design integration patterns that respect the constraints of the legacy system instead of forcing them into a modern pattern.
A first integration typically runs in the low-to-mid six figures depending on the number of systems in scope. The cost is sensitive to integration depth - number of systems, data sensitivity, identity model - more than to the AI itself. We scope and price after a 30-minute discovery call.
You do. Every Logiciel engagement includes full IP assignment, source-code ownership from the first commit, and complete documentation. We don't operate a black-box integration layer that locks you in.
You don't need another strategy session. You need an integration plan: the systems, the data flows, the governance, and the timeline. Thirty minutes with a Logiciel integration engineer and you have one.