01
Documents that keep work waiting
Quotes, specs, requests, reports, forms, and updates still move too slowly through the operation.
QuotesSpecsRequests
Now booking Q3 pilots//7 LIVE
We make your business
AI-native.
Laava plugs real, production-grade AI into the systems you already run — so your team works faster, smarter, and without the theatre.
4Weeks to live
ProductionGrade, not demos
EUHosted · Utrecht, NL
Proof from real operations
Evidence beats AI theatre
These are not abstract capability claims. They are the kinds of operational gains we use to qualify whether a use case is worth pursuing at all.
20+
brands on one platform
Multi-brand energy customer operations
12 min -> 45 sec
search time reduced
Permission-aware SharePoint knowledge layer
3h -> 45 min
KYC prep time reduced
AI-assisted financial screening workflow
2h -> 15 min
spec review time reduced
Engineering and procurement comparison flow
Where AI makes the first real difference
The first useful AI case is usually not the flashy one. It sits in the operational friction that keeps costing time every week.
This is where we usually start: not with generic prompting, but with the document flows, knowledge gaps, and handovers that quietly slow down throughput, service quality, and execution.
If that sounds familiar, there is usually a strong first AI application closer than teams think.
02
Knowledge trapped in inboxes and key people
Too much context still lives in email threads, folders, and the heads of the few people everyone depends on.
Mail threadsFoldersKey people
03
Handovers between teams and systems that break flow
Information gets retyped, checked twice, or lost between Outlook, ERP, CRM, portals, and internal tools.
OutlookERPCRM
Most visible in
These patterns usually show up first in operations with heavy document flow, coordination work, and recurring decisions.
Logistics & supply chainConstruction, engineering & productionBusiness servicesEnergy, utilities & telecom
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
Faster handovers, fewer document errors, and more throughput without adding backoffice friction.
12 min -> 45 sec search time in logistics knowledge work
View sector
Construction, engineering & production
Project knowledge, technical documentation, and execution aligned with less dependency on key people.
2h -> 15 min for initial spec review
View sector
Business services
More output per professional, better consistency, and more room for higher-value work.
30-45 min saved per contract on first-pass review
View sector
Energy, utilities & telecom
Customer service, tickets, collections, field operations, and compliance handled with more speed and control.
20+ brands supported on one shared customer-ops platform
View sector
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.
ForHigh-volume logistics backoffice
Logistics Document Intake Before ERP Entry
Problem
Document intake workflow for logistics backoffices that reads freight documents, extracts structured fields, and validates them before ERP entry, so teams stop retyping and correcting the same information by hand.
Result
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 validation we mapped this to their ERP schema but stopped short of live integration - the goal was to prove extraction accuracy first.
91%Extraction accuracy
~200Documents tested
23Validation catches
View case
ForProfessional services firm
SharePoint Knowledge Layer
Problem
Permission-aware semantic search across 50,000+ SharePoint documents. Search time dropped from 12 minutes to 45 seconds, with zero permission violations in production.
Result
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.
12min → 45sSearch Time
95%Search Success Rate
ZeroPermission Violations
View case
ForMulti-brand energy operation
Multi-Brand AI Customer Operations Platform
Problem
The customer-facing implementation of a shared AI platform for a multi-brand energy operation. One platform supports voice, chat, L2 ticket handling, debt-related flows, and sales conversations across 20+ brands without duplicating logic, knowledge, or governance.
Result
We implemented the platform as a shared multi-agent layer for customer operations. Instead of building separate systems for every channel and every brand, we used one platform with shared orchestration, retrieval, memory, and governance, then configured role-specific behavior on top of it. Voice agent with ElevenLabs: handles spoken interactions with controlled routing and handoff. Chat agent for high-volume digital support across brands with brand-aware context and tone. L2 ticket agent: auto-triages tickets, retrieves the right source material from pgvector-backed knowledge, and references comparable resolved tickets in escalations. Debit agent: supports debt-related workflows with the right tone, process sequence, and escalation boundaries. Sales agent: helps qualify and steer commercial conversations without losing operational context. The core stack runs on LangGraph orchestration with pgvector for retrieval and reference matching, channel-specific agent behavior, and integrations into the surrounding operational systems. That makes the platform honest to the actual work: not one prompt wrapped in UI, but a controllable multi-agent operating layer.
20+Brands
5Agent Roles
Voice + Chat + TicketsChannels
View case
How we work
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.
We keep the first step commercially serious and operationally small. Enough scope to prove value in the real operation, not so much scope that momentum disappears before anything ships.
What this usually includes
One processClear ownerReal dataGuardrailsWorking output
01 Scan
01
AI Opportunity Scan
A working session around one concrete process. We identify where AI does and does not make sense, and what the fastest first step is.
In practice
IntakeDocumentsInboxesKnowledge
02 Build
02
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.
In practice
Pilot flowReview stepMeasured outputLive usage
03 Expand
03
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.
In practice
ApprovalsLoggingRolloutNext use case
Ongoing capability
Forward Deployed Engineer
A senior AI builder in your team, backed by the full Laava team. For companies that want to keep implementing and scaling without building an entire internal AI team first.
Best suited for teams that already see where the next opportunities are and want implementation capacity that stays commercially sharp and technically senior.
Where this tends to fit
Implementation capacityOperational ownershipSenior AI engineeringScale without hiring a full team
Built to run in your existing operation
AI has to fit the stack you already depend on
The real challenge is usually not model access. It is making AI work inside the channels, systems, approvals, and ownership boundaries that already exist in the business.
Channels and work surfaces
OutlookTeamsEmailWebformsPortalsPhone / voice
Core systems
ERPCRMSharePointJiraServiceNowCustom APIs
Business software in the wild
SAPAFASExactHubSpotDynamicsSalesforce
Integrates where the work already happens
AI should land inside the current workflow, not force the team into a second operating model.
Keeps approvals, routing, and handovers explicit
Operational control matters more than a clever demo. We build flows that can be followed, audited, and improved.
Works with governance instead of around it
Permissions, source grounding, escalation rules, and review steps are part of the system design from day one.
Built for real systems, not AI theatre
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.
This is where AI becomes useful without becoming brittle. We connect incoming work, add the right controls, and let the next step happen inside the systems teams already use every day.
Examples we commonly work around
OutlookJiraWebformsSharePointTeamsAFASExactDynamics 365HubSpotSalesforce
01 Input
01
Understands what comes in
Documents, emails, tickets, and forms are interpreted with context and source citations, so teams can see where answers come from.
Examples
OutlookShared inboxesJiraWebforms
02 Control
02
Works according to your rules
Classification, routing, drafting, and validation happen inside the guardrails you define, with approvals where they matter.
Examples
ApprovalsValidation rulesSource citationsAudit trail
03 Action
03
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.
Examples
ERPCRMSharePointTeams
FAQ
FAQ
The most practical questions that usually come up before a first application actually lands in the operation.
First serious step
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.
Included in the first conversation
Free sessionOne concrete processHonest first route
Start with one process. Leave with a sharper first route.