AI opportunity analysis & prototype
We review processes and data sources to select the highest-impact AI use cases, then validate technical fit and business value early with a focused prototype.
Use Case · Data Audit · Proof of Concept
AI Integrations
Connect AI to your workflow.
We add secure AI layers to existing products and operations, making knowledge accessible, repetitive work automated and decision-making faster.
We build more than an AI demo: a measurable system that works inside daily operations. We design the full lifecycle from selecting the right use case to data security and production monitoring.
What we do
Enterprise knowledge & RAG
We develop enterprise search, assistant and copilot experiences that answer from policies, product data, contracts and support content with sources.
OpenAI · Claude · RAG · Vector Search
AI agents & automation
We build controlled agent workflows that work with CRM, support, sales and operations systems, classify tasks, use tools and proceed with human approval.
AI Agents · Tool Calling · Workflow · Human-in-the-loop
Document & data intelligence
We build reliable document systems that extract structured data from invoices, contracts, forms and emails, then classify, summarize and route it.
OCR · Embeddings · Classification · Semantic Search
Vision, voice & predictive models
We productize image analysis, conversational flows, anomaly detection and predictive models for your industry and real operating conditions.
Vision · STT / TTS · Forecasting · Python
Security & AI operations
We make AI manageable in production through authorization, privacy controls, cost limits, output evaluation and model monitoring.
Guardrails · Evaluation · Observability · MLOps
Existing work and related insights.
See this service in a real product and explore existing articles that can support your decisions.
DoctorFollow
A DoctorFollow iOS case study covering interface modernization, Swift/UIKit implementation and AI-powered clinical screens.
Claude Mythos, Project Glasswing and what changes for security teams
A practical look at what new model capabilities mean for enterprise security operations and AI-powered threat detection.
Quantization without the hype: the real cost of running an LLM on your own hardware
A practical look at the trade-offs between model size, latency, accuracy and infrastructure cost.
Frequently asked questions
Do we need a lot of data for an AI project?
Not always. Foundation models and RAG make it possible to start with existing documents and systems in many cases. During discovery, we assess data quality, access and security requirements.
Can AI be integrated into our existing software?
Yes. We can add AI capabilities to CRM, ERP, support systems, mobile apps and internal portals through APIs and secure integration layers.
Will our company data be shared with model providers?
We assess data processing and retention policies based on the selected infrastructure. When needed, we can use on-premise, private cloud or enterprise APIs with no data retention.
How do we start an AI integration?
We start with one high-value, measurable use case. After a focused prototype, we complete security, integration and quality requirements before production.
Together, let's build something that works.
Tell us briefly about your project. We will clarify the opportunities together in our first call.