AI Chatbot Design & Development
Bots built around
actual behavior,
not assumptions
Haagse Kennis designs and develops AI chatbots grounded in real interaction data - systems that handle edge cases, maintain context across multi-turn conversations, and integrate cleanly into existing workflows without months of configuration overhead.
What sets this apart
Generic chatbot builders solve the easy 80%
Most platforms handle straightforward FAQ routing well. Where they fail is in the remaining cases - ambiguous queries, domain-specific terminology, handoff logic, and situations where a wrong answer has real consequences. Haagse Kennis approaches these as engineering problems, not configuration tasks.
The difference shows up in how the system behaves at message 7 of a conversation, not message 1.
Context that persists
Conversation state is tracked across turns so users never repeat themselves. The bot understands what was said 4 exchanges ago and adjusts its responses accordingly.
Defined failure behavior
Every bot is built with explicit fallback paths - what happens when confidence is low, when the query is out of scope, or when a human agent needs to take over. These aren't afterthoughts.
Signals of standing in a field that moves fast
Industries served
From logistics and legal to healthcare intake and e-commerce support - each deployment required domain-specific training and intent architecture, not a generic template.
Production deployments
Bots currently handling live traffic across client systems worldwide. Each one is monitored, iterated, and maintained beyond launch.
LLM architectures tested
No single model fits every use case. Haagse Kennis evaluates latency, cost, and accuracy against the specific task before recommending an architecture.
Founded in 2024, the lab has moved quickly because the team came in with prior experience in production NLP systems - not as learners figuring it out in public. The work is peer-reviewed internally before any client sees it.
Working relationship
What the process actually looks like
A project typically spans 8–12 weeks from scoping to first production traffic. The process is iterative - clients review working prototypes at week 3 and week 6, not just a final handoff. Feedback at each stage shapes what gets built next.
Conversation audit
Existing support logs or user transcripts are analyzed to identify the real distribution of queries - not assumptions about what users ask.
Intent architecture
Intent trees and entity schemas are designed before any model training begins. Structure first, then implementation.
Prototype review
A working bot handles a defined subset of flows. Clients interact with it directly and provide structured feedback across at least 2 review cycles.
Deployment & monitoring
Post-launch, conversation logs are reviewed weekly for the first 30 days. Drift in intent recognition triggers retraining, not just a support ticket.
Yuki Harasawa
AI Systems Lead
The audit phase consistently surfaces queries that nobody on the client team anticipated. That's where the real design work begins.
Marta Veilberg
Conversation Design
Most bots fail at turn 4 or 5. We test specifically for that range because it's where context management breaks down in practice.
Patterns that repeat across different clients
These figures come from deployments measured at the 90-day mark - enough time for initial novelty effects to settle and for usage patterns to stabilize into something representative.
Containment rate at 90 days
Across 12 measured deployments, an average of 91% of conversations reached resolution without escalation to a human agent - measured after the initial stabilization period.
Median response latency
Across production systems, median response time sits at 4.1 seconds including retrieval and generation. Latency targets are set per project based on the interaction context.
Clients return for iteration
Of clients with a bot in production for more than 6 months, 83% have commissioned at least one subsequent iteration - adding flows, new languages, or integrations.
