Founder-led AI consulting and engineering from France
Practical AI consulting and systems for document workflows, internal knowledge, and operational delivery
INOVAI CONSULTING LAB helps startups, SaaS companies, and operations teams improve document-heavy work, internal knowledge access, and repeatable operations with practical AI systems scoped for real use.
Go directly to the AI agents and agentic workflows and RAG systems and knowledge assistants service pages when you already know the workflow category you need.
Make internal knowledge easier to use
Source-backed assistants and retrieval flows for teams that need faster answers from real documentation.
Turn documents and transcripts into review-ready outputs
Extraction, summarization, and reporting workflows shaped around human review and operating constraints.
Ship useful AI inside a product or internal workflow
Focused delivery for assistants, automation steps, and AI-enabled features that need to work beyond a demo.
France-based registered business serving clients in Europe and beyond. Remote collaboration worldwide. SIREN 102 107 836.
Founder-led delivery backed by NLP research, applied work in health-tech and legal AI, and hands-on implementation across retrieval, evaluation, document processing, and human-review workflows.
Representative work
Each example follows the same structure: problem, approach, operating constraint, and outcome.
Knowledge assistant for internal documentation
Internal teams need faster answers from policies and reference material without losing traceability.
- Approach: Source-backed retrieval, answer citations, and content structure tuned for verification.
- Constraint: Answers needed to stay verifiable against real internal source material.
- Outcome: Faster self-serve answers with a clear evidence trail.
Document, transcription, and reporting workflow
Operations teams lose time reading long files or listening back to calls before they can write a report.
- Approach: Extraction, summarization, and report preparation with human review where judgment matters.
- Constraint: Outputs had to stay structured, review-ready, and easy to check.
- Outcome: Less repetitive manual work and faster preparation of case summaries or reports.
Operations assistant with human review
Recurring internal tasks often need speed, but not fully autonomous decisions.
- Approach: Retrieval, routing logic, and bounded workflow steps with explicit review checkpoints.
- Constraint: Automation had to stay bounded, observable, and easy to escalate.
- Outcome: Faster handling of repeated requests without removing human control.
How engagements stay practical
The work is shaped around the workflow, the operating constraints, and the level of support the team actually needs.
Engagement formats
Scoping and decision support
Short engagements for teams that need clarity on the workflow, architecture, model choice, or delivery path before building.
Embedded AI engineering support
Hands-on work inside an existing product or operations team with practical ownership over the selected workflow, feature, or system slice.
Fractional support
Part-time support for product and technical teams that need continuity on roadmap, iteration, and AI decision-making without a full-time hire.
Project-based delivery
Defined scopes for prototypes, internal tools, or production systems with clear milestones and a usable deliverable at the end.
Delivery principles
Practical scoping
Start from the workflow, user need, data, and constraints so the system fits a real business problem.
Architecture and implementation
Choose models, retrieval patterns, tooling, and infrastructure based on reliability, latency, maintainability, privacy, and budget.
Iterative delivery
Ship in small validated steps, improve with real feedback, and keep product, engineering, and operations stakeholders aligned.
Reliability and deployment
Build evaluation, observability, and human review into the work instead of treating them as a late add-on.
Where we usually help
Three common entry points are shown here. The full service detail lives on the dedicated pages.
Strategy
Scope the right AI opportunity
Clarify where AI will actually help, what should stay human-reviewed, and what the first practical build should be.
Explore AI consultingWorkflow systems
Build document and knowledge workflows
Design assistants, search, extraction, and review flows for teams working across internal documents, PDFs, transcripts, or structured outputs.
Explore RAG systemsProduction delivery
Ship applied AI into production
Move from pilot logic to a usable product or internal workflow with evaluation, latency, guardrails, and iteration built in.
Explore applied AI engineeringWho We Work With
The practice is a strong fit for teams that need senior AI support tied to product delivery, operations execution, and decision-making, not just experimentation.
Startups building AI features
Startups that need help shaping, implementing, and shipping AI capabilities inside a product without overbuilding the stack.
SaaS and product teams integrating LLMs or RAG
Teams adding assistants, search, document workflows, or automation into existing user experiences or internal platforms.
Innovation and AI startup teams validating use cases
Teams exploring a concrete Generative AI or applied AI opportunity and needing pragmatic scoping, prototyping, and technical direction.
Businesses improving internal workflows
Organizations that want custom internal AI tools for document-heavy, research-heavy, operations-heavy, or audio-heavy processes.
Where teams usually need help
Typical client needs are less about chasing the newest model and more about making the system useful, reliable, and deployable.
Scoping the right system
Clarifying what should be automated, what should stay human-reviewed, and where AI creates enough value to justify implementation.
Getting quality and grounding right
Improving retrieval, prompts, evaluation, and review loops so outputs are useful in real work, not just demos.
Integrating and deploying cleanly
Connecting the solution to product or internal tooling while managing privacy, multilingual data, performance, and maintainability.
About
Founded by Ozgur Agrali, INOVAI CONSULTING LAB is a founder-led independent AI consulting and delivery practice based in France. The work is remote-first and focused on workflows that need clearer scoping, solid implementation, and business-ready delivery.
The background combines NLP research publications, open-source AI work, and applied delivery in areas such as health-tech and legal AI. The emphasis stays practical: make document, knowledge, and operational workflows easier to run with systems that can hold up outside a demo.
Why teams trust the engagement
Request the scoping call
A concise note about the workflow, documents, transcripts, or product surface you want to improve is enough to begin. The first exchange does not need a polished specification.
- Short workflow scoping conversations with a written follow-up
- Consulting sprints to clarify the right architecture and scope
- Embedded delivery inside a product or operations team
- Scoped builds for document, knowledge, or automation workflows
Include the current workflow, source material, constraints, timeline, and where the team is getting stuck so the first conversation can stay concrete.
Professional links
LinkedIn for business contact, plus founder technical profiles on GitHub, Hugging Face, and Google Scholar.