Built for real enterprise workflows
AI for domains where a wrong answer costs more.
Loss you cannot locate. Gaps you cannot map. Anomalies no team can review. We research the domain, engineer the platform, and ship software that runs in production. Not another pilot.
Innopas IP behind smarter decisions
DeepTech, Data Spine, AI Engine, Cloud Mesh
Builders, researchers and engineers
Five layers, bottom to top: DTRIHub research; the lalla.ai platform core with six capabilities; the utility knowledge graph and concept graph; the EUNIQ and TopSyllabus products; and the surfaces — Lalla Chat, dashboards and APIs, field and operations. Four solutions orbit the core: DeepTech, Data Spine, AI Engine and Cloud Mesh.
Five layers, bottom to top. The bottom two are built once and shared; only the top layers change per product. Four solutions orbit the core, and each is a way in.
From a hard problem to a working product.
Enterprise AI stalls when generic models meet complex reality. A grid has topology. A syllabus has structure. A business has rules, evidence, risk and decisions. We start with the domain, not the demo.
Research the domain.
Our DeepTech Research & Intelligence Hub develops the methods that make generic AI useful in complex environments, and with university research groups and PhD advisors.
Never start from scratch.
lalla.ai is the platform underneath everything we build — agents, retrieval, evidence, guardrails, evaluation and deployment, already done. You bring the domain and the data. License it and build on it with your own team, or have us build on it for you.
Build the product.
The method becomes software we own: EUNIQ for energy and utility networks, TopSyllabus for education. Each adds only its domain: the platform beneath it is already built.
Bring one problem to a 30-minute call.
The engineers who would do the work, not a sales team. You leave with a straight read on whether it is solvable today, whether it needs research first, and roughly what a first build would take. Including when the answer is that you should not build it.
Choose the right path to value.
Not every business problem needs a custom build. Start with a solution when the question is open, a product when the need repeats, or a partner platform when proven technology already exists.
Start with a solution.
For open questions that need research, architecture and a practical first build, and DeepTech, Data Spine, AI Engine and Cloud Mesh.
Explore solutions ↗Start with a product.
For recurring problems where Innopas already has a deployable answer — EUNIQ, TopSyllabus, and the lalla.ai platform beneath them.
Explore products ↗Start with a partner.
For mature capabilities where buying and implementing is smarter than rebuilding: cybersecurity and GRC, and supply chain execution.
Explore partners ↗Three ways in, depending on what you already have.
If the problem is still an open question, start with a solution. If the answer is a system you can license, start with a product. If someone has already built it well, start with a partner platform.
We work on your problem.
Applied AI programmes, scoped and gated. Each is anchored to research we own and a platform already in production.
You license what we built.
Finished things with a name, an owner and a price. Two products, and the platform beneath them both.
We bring what we did not build.
Mature platforms in domains where rebuilding would serve nobody. We implement and support them; the products stay our partners'.
Talk to the engineers who would build it.
Thirty minutes. No deck. No discovery invoice. Just a straight read on feasibility, sequence and what a first build would take, even if the answer is not to build.