AI for companies with a real operation behind them

Construction, Manufacturing & Engineering

In construction, manufacturing, and engineering, the critical knowledge sits in specs, drawings, and manuals, and in the heads of people close to retirement. Meanwhile projects get more complex and margins thinner. On the Laava Platform, agents read specs and datasheets, check them against requirements, answer technical questions from your own documentation, and prepare the paperwork that keeps projects moving. For production lines and installed equipment, we build predictive maintenance models that learn from sensor and maintenance data and flag failures before they stop the line. Experience stops walking out the door, and engineers spend their time engineering.

Public & Social Sector

The public sector has to deliver more service with fewer people, while every decision must be explainable. That combination breaks on manual work: case files, objections, and information requests queueing up behind scarce specialists. On the Laava Platform, agents prepare case work from approved sources, with source citations and human approval built into the workflow. Civil servants keep the judgment; residents get answers faster.

Business Services

In business services, the product is knowledge and the constraint is time. Hours still disappear into reading documents, drafting proposals, and checking contracts, work that follows patterns an agent can learn. On the Laava Platform, agents read, draft, and check inside your own systems and sources, while a professional makes the calls that matter. The hours go back to strategy, clients, and the work you actually bill for.

Transportation & Logistics

Logistics runs on trucks and ships, and on PDFs, scans, portals, and mail threads. The delay is rarely on the road; it sits in the back office, where orders, customs documents, and status questions wait for someone with time. On the Laava Platform, agents read and structure documents, answer status questions from your own systems, and prepare the next step in the chain. Fewer errors, faster handling, and a back office that keeps up with the fleet.

Retail & Consumer

Retail wins on speed: answering customers, keeping product data right, and keeping promises during peaks. On the Laava Platform, agents handle recurring customer questions across channels, keep product and order information consistent, and prepare the follow-up in your own systems. The team keeps the exceptions; customers stop waiting.

Energy, Utilities & Telecom

Energy, utility and telecom operators combine customer contact with billing, payment follow-up and field operations. At OpenSource Energie, we are building a fully agentic front office for customer contact, support and follow-up. AI agents work with existing systems to retrieve context, prepare replies and carry out agreed steps, with the team in control. On the asset side, we build predictive maintenance models on sensor and maintenance data, so field teams get ahead of failures instead of chasing them.

Healthcare & Life Sciences

Healthcare runs on data that is hard to reach: archives of scans, reports and records that only a few people know how to search. We build AI that works with that data within strict privacy constraints: computer vision and self-learning models that make medical images searchable without labelling them first, and agents that take repetitive document and intake work off clinical and support teams. Specialists keep the judgement; the data finally works for them.

Financial Services

Financial services carry a double load: customers expect instant answers, regulators expect every step to be documented. On the Laava Platform, agents prepare client files, screen documents against policy, and draft responses, grounded in your approved sources, with review and audit built in. Compliance stops being the reason work is slow.

Other Industries

Not listed? The patterns matter more than the sector: documents that keep work waiting, knowledge locked in inboxes, handovers that break flow. If your operation recognises those, there is usually a strong first workflow, and the approach is the same: one workflow live, then expand on the same foundation.