From scattered AI tools to an AI-native operation.
Laava helps companies embed AI structurally into their operation. Not as another chatbot or isolated pilot, but as working systems that speed up processes, scale knowledge, and give teams more room to move.
AI that lands in existing operations • Safe and manageable by design
Built for the messy middle of real operations
Built for operations where documents, knowledge, and handovers still cost too much time.
The teams we work with are usually not missing tools. They are losing time in the gaps between inboxes, files, approvals, systems, and the few people who still carry too much context in their heads.
Where Laava makes the difference
Practical AI for the parts of the operation where time, quality, and coordination still leak away every day.
01
Documents & backoffice
Take friction out of document-heavy flows.
Process invoices, forms, emails, and attachments faster.
Let teams focus on exceptions instead of retyping and checking.
02
Knowledge & teams
Make internal knowledge directly usable.
Get answers faster across SharePoint, manuals, procedures, and project files.
Keep source citations and existing permissions in place.
03
Customer questions & service
Respond faster without lowering quality.
Handle recurring questions, triage requests, and prepare responses.
Escalate edge cases with full context to the right person.
04
Workflows & approvals
Remove handoffs that keep slowing the operation down.
Structure incoming work, route it correctly, and trigger the next step.
Add approvals where control matters and automation where speed matters.
For teams with a real operation behind them
We are at our strongest where work still depends on documents, inboxes, domain knowledge, and coordination across systems and people.
Logistics & supply chain
For operations that need faster handovers, fewer document errors, and more throughput without adding more backoffice friction.
Construction, engineering & production
For companies where project knowledge, technical documentation, and execution need to line up faster and with less dependency on a few key people.
Business services
For service teams that want more output per professional, better consistency, and more room for higher-value work.
Short, concrete, and measurable.
Case studies from the real operation
Not lab demos or isolated pilots, but working applications inside live processes that materially improve speed, quality, and operational handovers.
The 'No-Touch' Document Processor
A 4-week PoC demonstrating how multi-modal AI can read logistics documents - waybills, CMRs, packing lists - extract structured data, and validate it against business rules before ERP entry.
In 4 weeks we built a working extraction pipeline and tested it on ~200 real documents from their archive: Multi-modal extraction via Azure OpenAI: Documents are processed as images, so the model can interpret visual layout, tables, stamps, and handwritten annotations - not just machine-readable text. LangGraph validation workflow: A multi-step agent that cross-references extracted fields (PO numbers, weights, addresses) against a sample order dataset, flagging mismatches for human review instead of silently passing them through. Structured JSON output: Each document produces a standardized JSON payload ready for ERP ingestion. During the PoC we mapped this to their ERP schema but stopped short of live integration - the goal was to prove extraction accuracy first.
SharePoint Knowledge Layer
Permission-aware semantic search across 50,000+ SharePoint documents. Search time dropped from 12 minutes to 45 seconds, with zero permission violations in production.
We built a permission-aware semantic search layer on top of the existing SharePoint environment: SharePoint Graph API integration for document indexing, permission mapping, and metadata extraction Semantic vector search via Qdrant - natural language queries like "Find the contract template we used for government clients in 2023" Permission enforcement at query time - users only see results they are authorized to access, matching SharePoint's department-level access controls exactly Azure OpenAI embedding models for semantic understanding, with query expansion for better recall Built in TypeScript, deployed as a production system within the client's Microsoft ecosystem The permission-aware architecture accounted for roughly 40% of the total project effort - but it was non-negotiable for enterprise deployment.
Multi-Brand AI Customer Support
Multi-tenant AI customer service platform for a Dutch energy retailer operating 20+ whitelabel brands. Each brand gets its own tone, knowledge base, and escalation logic - powered by a single LangGraph pipeline with Azure OpenAI and deep CRM integration.
We built a multi-tenant AI support platform where each brand operates as an isolated tenant with its own knowledge base, system prompt, tone of voice, and escalation rules - all running on one shared LangGraph pipeline backed by Azure OpenAI. Brand-aware LangGraph agent: incoming messages are routed through a stateful graph that loads the correct brand context, retrieves relevant knowledge (contracts, FAQ, policies), and generates responses matching that brand's tone and rules Deep CRM integration: the agent pulls real-time customer data (contracts, payment status, meter readings) to give personalized answers - not generic FAQ responses Multi-channel support: handles both email and live chat, with different response strategies per channel (structured email replies vs. conversational chat) Intelligent escalation: when the AI detects it can't resolve an issue (complaints, complex disputes, edge cases), it routes to a human agent with full conversation context, customer history, and a summary of what was already tried Brand onboarding workflow: new whitelabel brands can be configured with their own knowledge base, tone, and policies without code changes - just content and configuration The architecture follows our three-layer approach: Context (brand-specific knowledge retrieval and customer data), Reasoning (LangGraph agent with Azure OpenAI), and Action (CRM updates, email drafting, escalation routing). This keeps the system modular - we can swap models, update knowledge, or add channels without rebuilding the core.
From first scan to a first working AI application
No endless pre-project. Start with one process, one clear business case, and one working application in weeks.
AI Opportunity Scan
A free working session around one concrete process. We identify where AI does and does not make sense, and what the fastest first step is.
First AI application in 4 weeks
A first working application that proves value in the real operation. Small enough to move fast, serious enough to matter.
Scale with control
Once the first application lands, we expand with the same discipline: approvals where needed, no lock-in, and room to keep building.
Built to run safely in your existing operation
Source citations, approvals, auditability, and no lock-in. So AI can create momentum without becoming a risk.
Understands what comes in
Documents, emails, tickets, and forms are interpreted with context and source citations, so teams can see where answers come from.
Works according to your rules
Classification, routing, drafting, and validation happen inside the guardrails you define, with approvals where they matter.
Acts in your existing systems
AI supports the next action inside the tools you already use, with logging, traceability, and less dependency on brittle workarounds.
Understands what comes in
Documents, emails, tickets, and forms are interpreted with context and source citations, so teams can see where answers come from.
Works according to your rules
Classification, routing, drafting, and validation happen inside the guardrails you define, with approvals where they matter.
Acts in your existing systems
AI supports the next action inside the tools you already use, with logging, traceability, and less dependency on brittle workarounds.
Curious where AI can make a real difference in your operation first?
In a free AI Opportunity Scan we look at one concrete process, give an honest assessment, and outline the fastest route to a first working application.
Free session • One concrete process • Honest first route