01Featured
Customer-support AI agents
Reduce support load and speed up response times. Our agents resolve up to 40% of routine enquiries on their own and escalate the harder cases to your team with full context.
Most-shipped piece
Service · AI Solutions
Your data already has the answers.
We make it talk.
Your customer data, orders and documentation are already sitting in your stack. We turn them into live AI workflows — pilots that grow into operations, not the other way around.
What we deliver
Six example AI agents.
Far from the only ones.
No theoretical slides, no empty promises. These are real, live solutions we operate and develop for Bauhof and Aatrium today.
02
Demand-forecasting agents
Eliminate stockouts and optimise working capital. AI-driven demand forecasting at SKU level lets you anticipate stock running out and automate replenishment planning.
03
Autonomous content generation
Scale your assortment without a content bottleneck. AI agents generate technically accurate product copy and metadata, strictly following your brand voice and quality standards.
04
Strategic decision tooling
Make data-driven business decisions in real time. An AI copilot analyses sales data and market conditions, offering precise recommendations for pricing and campaign planning — replacing gut feel with facts.
05
Omnichannel integration
A single layer of intelligence across all your channels. Connect AI agents to web chat, email, Slack or Teams, ensuring consistent, high-quality information at every touchpoint.
06
Observability & security
Ensure full control and regulatory compliance. Every AI-agent action and conversation is logged, analysed and auditable, giving you confidence that data is handled securely.
How we work
4 stages. No surprises.
A single statement of work, weekly demos, and the same engineers in every meeting. Re-scopes happen in writing.
- 01Week 1
Use-case analysis & metrics
We pick the critical business process with the highest automation potential. We set the baseline and agree on concrete success metrics (KPIs).
- 02Week 2 — 4
Pilot build
We build a working AI agent against an isolated copy of live data. Initial testing runs with internal users in a controlled environment.
- 03Week 4 — 6
Evaluation, tuning & validation
We measure answer quality, reliability and business impact, then tune the system until it clears the agreed production threshold.
- 04Week 6 — ∞
Operations & monitoring
We move the agent into day-to-day operation with telemetry, rate limiting, audit trails and ongoing performance monitoring.
Who this is for
Three perspectives.
Same fixed-deadline contract.
Risk-reversal in plain English — what your board, your marketers, and your engineers each get out of the same project.
01
For business leaders
Model-agnostic systems on top-tier foundation models. Cost-to-performance ratio held in writing.
- Pilot to production in 4–6 weeks
- Vendor independence (OpenAI · Claude · Llama 3)
- Measurable lift, not demo-stage promises
02
For support & sales
Answer a large share of inbound questions instantly — 24/7 coverage in your tone of voice, with exceptions escalated to a human with full context.
- Semantic search over your knowledge base
- Citations, not hallucinations
- Escalates to a human with full context
03
For IT & security
Your data stays yours. Hardened security controls, private model option, audit trails on every call.
- Never enters vendor training pipelines
- PII redaction at the audit step
- Containerised endpoints, rate-limited, cached
Our toolchain
Boring tech.
On purpose.
Boring infrastructure with sharp AI on top. A standard, production-grade stack keeps your agents portable, observable and supportable as models, teams and traffic change.
01Tooling
Claude 4
02Tooling
GPT-5
03Tooling
Gemini 3
04AI
Letta
05Tooling
Pgvector
06Tooling
BM25
07Tooling
Apache AGE
08Workflow
Temporal
09Tooling
Langfuse
10Observability
Sentry
10 pieces · one runbook
Plays nice with
The systems
you already run.
Integrates with your existing stack — Magento, your CMS, payments, ERP, analytics. We connect what you already run, not replace it.
OpenAI
Claude
Llama 3
Mistral
Pgvector
Pinecone
Letta
Langfuse
Case spotlight
Zaproo internal · 2024
A four-week pilot deflecting 38% of inbound tickets.
A Claude-based agent answering tier-1 product questions on the back of our knowledge base. The result came from a scoped use case, a controlled rollout, retrieval grounded on a real knowledge base, and continuous monitoring after launch.
Tickets deflected
38%
Time to live
4 wks
Common questions
The questions
we answer most.
If yours is here, you have your answer. If not, send a brief — it takes two minutes.
How do you keep our data out of vendor model training?
We use OpenAI and Claude API tiers with training opt-out by default. For data that cannot leave at all, we run Llama 3 or Mistral in an isolated environment inside your own cloud — customer data never crosses an external boundary.
Why model-agnostic instead of locked to one vendor?
Models change every six months, and locking to one vendor costs you the next jump. We benchmark Claude, GPT and Llama on your data, pick the best cost-to-performance ratio, and re-evaluate quarterly.
How is a production-ready agent different from a chatbot demo?
A demo answers three prepared questions nicely. Behind a production-ready agent sit the semantic search pipeline, the eval harness, the regression tests and the monitoring — that is what separates a demo that looks good in a meeting from a system on call at 02:00.
How long does a pilot take?
A pilot runs 4–6 weeks: it starts by fixing the use case and the success metrics, continues with building the agent against your data, and ends with evaluation and validation before it goes to production. The goal is not a demo but a measurable operational outcome.
A 30-minute call · No deck · A clear next step
Ready for AI
that ships?
Book a call
or just hit the phone+372 656 0066