AI integrated into the way your product actually runs. No new tools. Just more leverage.
AI is no longer optional: it is now part of how modern products operate. I build systems that improve workflows — sometimes visible to users, often running quietly in the background.
Turn admin flows and dashboards into places where work gets summarized, classified, and acted on faster.
Send the right alert, context, and next action to the channel your team already watches.
Move repetitive decisions out of queues and into reliable AI-assisted pipelines.
Give teams focused internal tools that answer, draft, search, and route using your real data.
These systems don’t replace teams — they increase their output and decision quality.
Incoming legal requests need to be understood, qualified, and assigned to the right lawyer quickly.
AI analyzes the client’s case, extracts the legal context, evaluates urgency and complexity, and matches the request to the most relevant lawyer based on expertise and past cases.
Clients are routed faster, assignments are more relevant, and legal teams spend less time on manual triage.
Rental application files contain multiple documents that take time to review manually.
AI reads the dossier, extracts key financial and profile information, flags inconsistencies or risk signals, and suggests the next action, including whether to accept or refuse the file and how to draft the client follow-up.
Teams review applications faster, with clearer signals, more consistent decisions, and less manual back-and-forth after the review.
Sales teams waste time reviewing weak inbound requests while high-value leads wait too long.
AI qualifies the lead, scores urgency and fit, surfaces the key signals, and suggests the next best action before anyone opens the CRM.
Teams respond faster to the right opportunities and spend less time sorting inbound demand.
User-generated content is hard to search, organize, and reuse when it stays unstructured.
AI turns a simple food image into structured product content: title, description, ingredients, tags, and a ready-to-save recipe entry.
Platforms gain cleaner content, better discovery, and more reusable data without manual formatting.
The answer exists somewhere, but teams still interrupt each other to find it.
A retrieval layer searches your knowledge base and returns the most relevant answer with source context.
Teams find answers faster. Onboarding and support depend less on tribal knowledge.
Support teams lose time reading history before every reply.
AI reads the thread, account history, and open tickets, then drafts the next response with the right context.
Faster replies. Less context switching. Agents review instead of starting from zero.
Most AI projects fail because they start from tools instead of workflows. The work starts with the process, then becomes a stable system your team can use.
The goal is not to add AI — but to improve how the system already works.
Map the product logic, data flows, and handoffs before choosing any model or tool.
Find the few touchpoints where AI removes work, surfaces signal, or improves decision quality.
Define inputs, outputs, fallbacks, and the exact UX your team or users will rely on.
Connect AI to your real data, APIs, and infrastructure so it runs inside the product, not beside it.
Validate on real examples and edge cases, because AI systems fail in specific, testable ways.
Ship a documented, maintainable system your team can operate without rebuilding it in three months.
Most teams are already integrating AI into their workflows. See where it fits in yours — and what to build to create leverage.
Send your workflow → Direct email: isaure.lohest@gmail.com