Customer-support AI agents
Reduce support load and speed up response times. A well-scoped use case can automate a significant share of routine enquiries: our own solution reached 38%. Harder cases go to your team with full context.
Your data already has the answers.
We make it talk.
The data and the knowledge AI can work from are already in your systems. We build agents on top of them that handle real tasks, and take the pilots that work safely into everyday use.
Six example AI agents.
Far from the only ones.
No theoretical slides, no empty promises. These solutions are running today, and we operate and develop them for our clients.
Demand & inventory forecasting
Reduce stockouts and optimise working capital. AI-driven demand forecasting at SKU level lets you anticipate stock running out and automate replenishment planning.
Automated content production
Scale your assortment without a content bottleneck. AI drafts product copy and metadata from your product data, tone of voice and quality rules, and the validated result goes live.
AI-assisted business analysis
Surface what you need from sales, customer and market data faster, so pricing and campaign decisions rest more on data and less on manual analysis.
AI across every customer channel
Connect the same AI agent to web chat, email, Slack or Teams. The agent uses the same knowledge base, the same rules and the same customer context in every channel.
Observability & security
What the agent does has to be reviewable and auditable. We log the calls, answers, tool use and decisions that matter, and apply the security rules that fit the data and the use case.
4 stages. No surprises.
A clear statement of work, weekly demos and the same accountable team for the whole project. Changes of scope are agreed in writing.
- 01
Use-case analysis & metrics
We pick the use case where AI brings a measurable business gain and the risk stays controllable. We measure the baseline and agree concrete success metrics (KPIs).
- 02
Pilot build
We build the pilot in a controlled environment on a dataset that suits the use case and is prepared securely. Initial testing runs with internal users.
- 03
Evaluation, tuning & validation
We measure answer quality, reliability, cost and business impact, then improve the system until it meets the quality and safety requirements agreed before the project started.
- 04
Production use & operations
We move the agent into day-to-day use with monitoring, cost limits, an audit trail and continuous quality tracking.
Three perspectives.
Same fixed-deadline contract.
What management, the support team and the IT team each get out of the same solution.
For business leaders
The solution does not depend on a single AI model or vendor. We pick the model for each use case on price, quality, speed and data requirements.
- Pilot to production in 4–6 weeks
- No lock-in to a single model vendor
- Measurable lift, not demo-stage promises
For support & sales
Automate routine enquiries and give customers an answer outside office hours too. Harder cases are handed to a person together with the conversation so far and the context they need.
- Semantic search over your knowledge base
- Citations, not hallucinations
- Escalates to a human with full context
For IT & security
Your data stays yours: clear access and security rules, a private or self-hosted model where that is needed, and an audit trail on every call.
- Enterprise API tiers with training opt-out
- PII redaction at the audit step
- Containerised endpoints, rate-limited, cached
Boring tech.
On purpose.
Stable infrastructure. The AI may change. The build does not depend on one model: a standard technical foundation lets you swap models, track costs and operate agents even as the technology moves quickly.
Claude
GPT
Gemini
Letta
Pgvector
BM25
Apache AGE
Temporal
Langfuse
Sentry
The systems
you already run.
Integrates with your existing stack: Magento, your CMS, payments, ERP, analytics. We connect what you already run — rather than forcing you to replace the systems you have.
OpenAI
Claude
Open-weight models
Mistral
Pgvector
Pinecone
Letta
Langfuse
A four-week pilot deflecting 38% of inbound tickets.
An AI agent answering tier-1 product questions from our own knowledge base. The result came from a tightly scoped use case, a controlled rollout, answers grounded in the company’s own knowledge base, and continuous monitoring after launch.
Tickets deflected
38%
Time to live
4 wks
Questions
we get asked most often.
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?
Your data is not used to train AI models.
We use enterprise AI services and APIs where customer inputs and outputs are not used to train models by default. Where needed, we apply additional data-protection and retention limits.
For particularly sensitive data we can also run open-weight models inside your own cloud environment. In that case the data never has to leave your organisation at all.
Why not build the solution on a single AI model?
Because the best model today may not be the best choice in six months.
AI models are moving fast, and different models suit different tasks. We build so the model in use can be swapped without rebuilding the whole system.
We test the candidates on your own use case and data, and choose on quality, speed, cost and data-protection requirements. Where it matters, we re-evaluate the choice regularly.
How is a production-ready AI agent different from an ordinary chatbot?
A production-ready AI agent is part of a business process, not just a chat window.
An ordinary demo or chatbot can answer prepared questions well. A production-ready agent also has to cope with incomplete data, errors and situations nobody planned for.
For that we use controlled knowledge sources, automated quality tests, monitoring, security rules and a clear process for handing a case to a person.
The difference is between a demo that looks good in a meeting and a system you can actually rely on at 02:00.
How long does an AI pilot take?
A typical pilot runs 4–6 weeks.
We start by agreeing the use case, the source data and the metrics. We then build the solution against your real data and working processes, and test its quality on real cases.
The pilot ends with evaluation and validation, which is what the decision to go to production rests on.
The goal is not to produce an AI demo, but to prove measurable impact in a real business process.
Ready for AI
that actually works?
Book an audit
or just hit the phone+372 656 0066