AI features users trust,
not demos.
We design and build LLM-powered products and AI features that work on real data, inside real systems — with the evaluation, cost control and guardrails that make them safe to ship.
Most AI projects stall between a promising prototype and a feature people trust. The gap is engineering: grounding answers in your data, handling the cases where the model is wrong, keeping latency and token costs predictable, and integrating with the systems your business already runs on.
Our senior engineers close that gap. We have shipped a business intelligence platform where non-technical users ask questions in plain English and get live charts from an ERP, a two-sided advisory SaaS with an AI recommendation layer, and ML-based route optimization for last-mile delivery. We bring the same production discipline to your product.
Assistants, natural-language search and conversational interfaces built into your web or mobile product.
Retrieval pipelines that ground answers in your documents, databases and knowledge bases — with sources users can check.
Let non-technical teams query operational data in plain English and get charts and reports instantly.
Agents and workflows that take actions in your tools — with approvals, audit trails and safe fallbacks.
AI wired into your ERP, CRM, payment or support stack rather than living in a separate silo.
Test sets, monitoring, prompt and model versioning, and token budgets so quality and spend stay under control.
NL→SQL
plain English to live charts
Built a business intelligence platform for an industrial client where users ask questions in plain English and instantly get charts, tables, and trend reports — no SQL, no analyst needed. Deeply integrated with the client's existing ERP to pull real-time operational data across sales, inventory, purchasing, and logistics.
Full case study2-sided
advisor + client platform
Designed and built a two-sided SaaS platform connecting advisors and clients, with an AI layer powering smart recommendations and conversation assistance. Included a proprietary Vue 3 component library (published to a private npm registry) used across both advisor and client-facing frontends, with AWS Cognito handling authentication.
Full case study5K+
daily shipments routed
Built an AI-assisted dispatch platform for a last-mile delivery company. ML-based route optimization cut fuel costs significantly, while the real-time tracking dashboard gave operations teams full visibility over every delivery.
Full case studyYes. Most of our AI work extends products that already have users and data. We integrate LLM features into your current architecture and ship them behind feature flags so you can roll out gradually.
We are model-agnostic and pick per use case — hosted models such as OpenAI, or open-source models from Hugging Face when data residency or cost requires it. The architecture keeps the provider swappable.
We ground responses in your data with retrieval, build evaluation sets from real questions, and add guardrails and human-in-the-loop steps where a wrong answer is costly. Quality is measured before and after every change.
Our whole team is based in Europe and used to building under GDPR. We design data flows so sensitive data is minimised, access-controlled and, where needed, kept out of third-party models entirely.
A focused AI feature or MVP typically takes weeks rather than months. We start with a short discovery phase to validate the use case and data before committing to a full build.
Tell us what you're building. We'll tell you if we're the right team — and what it'll actually take to get there.
