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01 · AI NATIVE

AI applications that work with you.

We build software that thinks, remembers, and decides for you in the right processes. It doesn't replace people: it frees up time for what really matters, keeping your data safe and every decision traceable.

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They've used our services

All our projects are covered by £10 million of professional indemnity insurance (verify here)
+ an additional £1 million dedicated to data security (verify here).

Benetton
Beretta
Colgate
Dolce & Gabbana
Diesel
Enel
Eni
FCA
Golden Lady
Kraft
Loro Piana
Peroni
DATA PRIVACY

Your knowledge stays yours.

RAG (Retrieval-Augmented Generation) is the safest way to use AI on company documents. Your contracts, manuals, policies, historical tickets stay in a vector database under your control — private cloud or on-premise.

When someone asks a question, the system retrieves only the relevant excerpts and passes them to the model via enterprise API with zero data retention. No training on your data, no leaks, no compromises.

Internal legal assistant — Answers questions on contracts citing clauses and versions, without documents leaving the company.
Customer support with memory — AI knows past customer tickets and internal policies, cuts resolution time by 60%.
Employee onboarding — Internal chatbot answering HR procedures, benefits, processes, replacing hours of one-to-one training.
RAG FLOW EXAMPLE
AI AGENT EXAMPLE
AUTONOMOUS AGENTS

AI that acts, not just chats.

An AI agent is a system that uses the tools you already have: reads emails, updates the CRM, queries the database, writes to the calendar. Executes multi-step tasks, asks for confirmation when needed, traces every action.

The goal is to free the team from repetitive tasks that consume time without creating value. End customers receive faster responses; internal users gain more time for complex problems.

Sales assistant — Qualifies leads from email/LinkedIn, updates Salesforce, books calendar meetings. The salesperson arrives at the call already prepared.
Support ticket triage — Classifies, prioritizes, suggests responses, escalates to the right team. Average response time from 8 hours to 20 minutes.
Multi-channel operations — Responds on WhatsApp, email, chat and phone keeping the same customer context. Consistent experience, always.
PERSONALIZATION

A model that speaks your language.

Generalist models cover many scenarios but are rarely optimal on a specific domain. With fine-tuning we start from a base model (GPT, Claude, Llama) and specialise it on your domain: industry terminology, communication style, company rules, real cases.

The result is an AI that stays closer to the required tone, reduces the risk of errors and follows specific rules. It typically costs less per query and has lower latency than larger models.

Consistent brand voice — A fine-tuned model writes copy that feels written by your team. Same voice on 1000 messages per day.
Industry compliance — Model fine-tuned on banking, medical or pharmaceutical regulations. Never responds outside defined boundaries.
Technical classification — Small model (7B) trained on your tickets or documents classifies better than GPT-4, at 1/10 of the cost.
FINE-TUNING EXAMPLE
HUMAN-IN-THE-LOOP EXAMPLE
HUMAN CONTROL

AI knows when to ask for a human opinion.

A well-designed AI doesn't fake certainty it doesn't have. Every decision carries a confidence score: above threshold the system acts autonomously, below threshold the output is reviewed by a person. The feedback is fed back into the system as an example for improving future decisions.

This pattern reduces hallucination, lowers error rate and increases team trust: critical decisions aren't made in the dark. The AI works like a colleague that knows when to involve someone more senior.

Approval workflows — AI processes 70% of standard cases; borderline ones go to operator queue with context pre-prepared.
Content moderation — Auto-approves clearly compliant content, flags ambiguous cases for human review, escalates high-risk ones.
Assisted medical diagnosis — Proposes likely diagnoses with confidence level; the doctor decides. Never replacement, always support.
COMPLIANCE & AUDIT

Every decision, traced.

Regulated sectors (finance, health, legal, government) can't use AI without complete audit trail. So every interaction is immutably logged: query, retrieved context, model used, output, final decision.

Ready for GDPR, EU AI Act, ISO 27001, SOC 2. If an inspection arrives tomorrow, we can show what AI did, when, on what data. No black boxes, no surprises.

Banking & insurance — Every risk decision motivated, contextualized, reproducible. Compliance with European banking regulations.
Healthcare — Logs of accessed information, automatic patient data anonymization, role-based access control.
Public administration — Algorithmic transparency: citizens can know how a decision concerning them was made.
AUDIT TRAIL EXAMPLE
VALUE FOR PEOPLE

AI makes sense when it adds value for those who use it.

We don't build AI to impress, but to support everyday work: internal teams reclaim time for complex problems instead of repetitive tasks; end customers get fast, accurate, always-available responses.

Every AI project starts with a simple question: who's better off afterwards? If the answer isn't clear, it's probably better not to build it.

FREQUENTLY ASKED QUESTIONS

What we get asked the most.

Transparency first. If your question isn't here, write to us: we reply within 24h, from a real person.

Does my data really stay private with a RAG system?
Yes. With a well-designed RAG system, your documents are never sent as training data to the LLM. Semantic search happens on a vector database that stays under your control (private cloud or on-premise, AWS Bedrock, Azure OpenAI, Vertex AI). Only relevant excerpts are passed to the model via enterprise API with zero data retention. We always work under NDA and follow ISO 27001 and GDPR by design.
How long to get an AI application in production?
Typically 4 to 12 weeks from kickoff, depending on complexity. Discovery takes 1-3 days, a working prototype is ready within 2 weeks, production deploy within the third sprint. We work with weekly sprints and live demos, so every phase is verifiable and you can change direction without throwing anything away.
How much does an AI application cost?
Depends on scope. An AI PoC starts typically at €15-25k; a complete RAG system or production agent €50-150k. We provide a fixed quote after the discovery phase, no surprises. LLM model costs are estimated separately and monitored in real-time with alerts on threshold overruns.
Which LLM is better: OpenAI, Anthropic or open-source?
There's no universally 'better' model. The right model always depends on purpose: how much reasoning needed, how much latency you tolerate, how sensitive the data, how much you can spend per query. We evaluate current options with benchmarks on your real case and choose what solves that problem best. The AI landscape changes every few months: our commitment is to stay current and switch tools as soon as a better one for you comes out.
How do you monitor an AI application in production?
Structured logging of every interaction (input, output, time, cost, model used), latency and quality tracking with automatic evaluation, alerting on drift, anomalies and cost spikes, continuous A/B tests on prompts and models. Real-time dashboard with technical and business metrics, integrated with your existing tools.
Can I integrate AI into my existing systems?
Yes, it's our daily bread. We integrate LLMs via API into CRM (Salesforce, HubSpot), ERP (SAP, Dynamics), e-commerce (Shopify, Magento), internal management systems, mobile apps and BPM flows. We work with REST, gRPC, GraphQL, webhooks, and the most common enterprise protocols. We don't replace your systems: we empower them.

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