Make internal knowledge easier to use
Source-backed assistants and retrieval flows for teams that need faster answers from real documentation.
Founder-led AI consulting and engineering from France
Practical AI. Built around your business.
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.
France-based registered business serving clients in Europe and beyond. Remote collaboration worldwide. SIREN 102 107 836.
Practical AI consulting and systems for document workflows, internal knowledge, and operational delivery
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.
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.
INOVAI / Products
Business AI products, with a clear scope
Explore local-first business products and request one or several modules, consulting, a PoC, pilot, MVP, or direct installation proposal. We agree scope, readiness, pricing, and delivery for your target environment.
Pilot available
Invoice Intelligence
Purchasing and finance teams need traceable invoice information, not disconnected spreadsheets.
Scope
The accepted RC9 pilot covers supported invoice XML, an invoice register, original-source access, supplier and price analytics, and explicit archive/restore with retained originals.
Availability and acceptance
Pilot Available applies only to the accepted RC9 artifact. The latest Invoice Starter assistant, history and read-only query API require new package acceptance; they are not certified by RC9. Customer-format and installation review are required.
One clear next step for your team.
Explore a focused product, shape a custom workflow, or discuss a controlled pilot. Start with the path that fits your need.
Explore the products
See what each product does, its current availability, and the boundaries of the offer.
Browse all five productsBuild around your workflow
Get founder-led consulting and engineering for your documents, knowledge, and operational processes.
Explore consulting servicesScope a pilot
Agree on the data, access, deployment, and acceptance criteria before requesting an installation.
How pilot access worksLocal-first intelligence. Built for useful work.
Turn authorized business information into searchable evidence and structured workflows. Discuss one product or a coordinated installation of several, from a focused proof of concept to a contracted deployment.
Small models, purposeful architecture
Prefer appropriately sized local models, structured retrieval, filters, and deterministic calculations where useful. External model APIs are optional only where the agreed solution supports them; any external processing requires explicit authorization.
Your information, controlled access
Design for customer-controlled processing, role-based access, authorized sources, and traceable actions. Privacy depends on the deployed configuration, permissions, backups, and operator practices—not on a blanket security promise.
Find the evidence, not just an answer
Search agreed documents, folders, and records with product-specific filters. Where supported, answers cite authorized source records; missing or contradictory evidence should trigger an explicit limitation or human review. No system can guarantee zero hallucinations.
Efficiency before model size
Use indexing, caching, focused pipelines, and smaller models to reduce unnecessary computation. Speed, power consumption, and environmental impact depend on hardware, workload, and measurements; no unmeasured energy savings are claimed.
One team, one or several products
Scope single-user or multi-user access, shared administration, and one or several modules. Combined installations require compatibility checks, permissions, resource planning, and an agreed integration contract.
Capacity matched to the job
CPU, GPU, and compact or portable-device requirements can be assessed. Large collections—including million-record targets—need representative benchmarks, supported-format checks, storage sizing, and concurrency tests. Not every file type or device is supported.
Commercial inquiries are open for every product. Installed features, supported formats, release readiness, pricing, capacity, and delivery dates are confirmed in your proposal—not guaranteed by a request.
Request a commercial proposalRepresentative 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.
- Timeline
- 30-45 minute call, followed by a short written recommendation.
- Deliverable
- Recommended scope, likely system shape, main risks, and the best next engagement format.
- What to bring
- A short description of the workflow, source material, current blocker, and what success looks like.
- Next step
- A consulting sprint, embedded delivery, or a tightly scoped build.
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
Workflow-first scoping before the build starts
Architecture choices made with latency, privacy, and budget in mind
Document, knowledge, and reporting systems designed for real users
Evaluation, observability, and human review built in early
Direct collaboration with founders, product leads, and operations teams
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.