AI retrofitting is the process of adding intelligent capabilities, natural language search, document summarization, automated classification, to existing business software without replacing the underlying system. Most established companies operate on legacy platforms that contain years of valuable data locked behind outdated interfaces. Retrofitting unlocks that data.

We build middleware layers using Python, LangChain, and REST APIs that sit between your current software and modern AI models. Your team accesses new capabilities through their existing dashboard, no retraining, no workflow disruption. A document search that previously took 20 minutes becomes a 3-second natural language query.

A logistics company running a 12-year-old ERP gained automated shipment status summaries, exception alerts, and natural language reporting through our middleware layer. Staff adoption reached 89% within two weeks because the interface was embedded in the tool they already used.

Retrofitting delivers 70–85% of the value of a full rebuild at 15–20% of the cost. For businesses that cannot afford 6–12 months of system replacement, it is the pragmatic path to AI-powered operations.

AI orchestration is the coordination of multiple AI models, tools, and data sources to execute complex, multi-step business processes autonomously. Unlike single-prompt AI interactions, orchestrated agents plan a sequence of actions, execute each step, evaluate results, and adjust course, similar to how an experienced employee works through a procedure.

We build orchestration pipelines using LangChain agents with tool-calling capabilities. A single trigger can initiate a chain: extract data from an uploaded PDF, validate it against your database, generate a summary, update a CRM record, and send a notification, all without human intervention.

Practical orchestration examples:

• Invoice processing: Extract line items from scanned invoices (OCR + LLM), match against purchase orders in the ERP, flag discrepancies, and route approved invoices for payment
• Contract review: Parse legal documents, extract key clauses (termination, liability, renewal), compare against standard terms, and highlight deviations for legal review
• Customer onboarding: Collect documents, verify identity data, create accounts across multiple systems, and trigger welcome sequences

Each pipeline includes error handling, retry logic, and human-in-the-loop checkpoints for high-stakes decisions.

AI integration solves specific operational bottlenecks by automating high-volume, repetitive cognitive tasks that currently consume skilled employee time. The highest-ROI applications share a common pattern: structured input, predictable logic, and measurable output. These are the use cases we deploy most frequently.

1. Automated Data Entry and Document Processing: Invoices, receipts, and forms are converted into structured database entries using OCR and LLM extraction. Accuracy rates exceed 96% on standardized documents, eliminating 15–20 hours of manual entry per week for a mid-sized business.

2. Sentiment Analysis and Ticket Prioritization: Customer emails, support tickets, and reviews are automatically scored for urgency and sentiment. Critical complaints surface immediately instead of sitting in a queue. Response time to high-priority issues drops by 60–75%.

3. Predictive Analytics: Historical sales data, inventory movements, and seasonal patterns feed machine learning models that forecast demand, optimize reorder points, and predict maintenance intervals. Inventory carrying costs typically decrease by 12–18%.

4. Content Generation at Scale: Product descriptions, meta tags, email copy, and report summaries generated from structured data inputs. Human review remains in the loop, but first-draft generation saves 70% of writing time.

5. Knowledge Base Search: RAG-powered internal search across company documents, past projects, and regulatory databases. Employees find answers in seconds instead of searching through file systems for hours.

Data sovereignty in AI means your business data is processed exclusively within infrastructure you control, never shared with third parties, and never used to train public models. For EU-based companies, this is both a competitive advantage and a GDPR legal requirement under Articles 25 and 32.

We implement a retrieval-augmented generation (RAG) architecture where your documents are embedded into a private Pinecone vector database hosted in EU data centers. The AI model receives only the relevant document chunks needed to answer each query, your full dataset is never sent to any external API.

Key data protection measures:

• Model isolation: We use API-based model access (OpenAI, Anthropic) with zero-data-retention agreements. Your prompts and responses are not stored by the model provider.
• Encryption: Data encrypted at rest (AES-256) and in transit (TLS 1.3) at every layer
• Access controls: Role-based permissions determine who can query, modify, or delete knowledge base content
• Audit logging: Every AI interaction is logged with timestamp, user identity, and data accessed, required for NIS2 compliance in critical sectors

For industries with heightened regulatory requirements (healthcare, legal, financial services), we deploy fully on-premise or in dedicated EU cloud environments.

An AI technical consultation is a structured assessment of your existing software infrastructure, data assets, and operational workflows to identify the highest-ROI opportunities for AI integration. It replaces guesswork with a prioritized, costed roadmap that your team can execute immediately.

The consultation covers three phases:

1. Infrastructure audit: We map your current tech stack, databases, APIs, ERPs, CRMs, and assess integration readiness. We identify data quality issues that would undermine AI accuracy if left unaddressed.
2. Opportunity scoring: Each potential AI use case is scored on implementation complexity, expected time savings, and revenue impact. We prioritize based on the ratio of business value to engineering effort.
3. Technical roadmap: You receive a phased implementation plan with specific timelines, cost estimates, and expected ROI. Phase 1 targets are selected for fastest time-to-value, typically under 6 weeks from kickoff to production.

We operate from Croatia within the EU regulatory framework, ensuring every recommendation accounts for GDPR, data residency, and industry-specific compliance. The output is a document your team can execute with us or independently, no vendor lock-in.

AI integration delivers the fastest measurable returns when applied to high-volume, repetitive cognitive processes, tasks where skilled employees spend hours on work that follows predictable patterns. These are the industries where our clients see production-ready results within the first 90 days.

Tourism and Hospitality: Automated guest communication, booking confirmations, check-in instructions, local recommendations, review responses, reduces front-desk workload by 60–80%. AI-powered revenue management adjusts pricing dynamically based on occupancy patterns, competitor rates, and seasonal demand signals.

Real Estate and Property Management: AI-driven CRM enrichment scores inbound leads automatically, prioritizing agent time on the highest-intent buyers. Document extraction pulls key terms from purchase agreements and lease contracts for instant review, cutting administrative processing time by 50%.

Professional Services (Legal, Accounting, Consulting): RAG-powered assistants search internal knowledge bases, past case files, and regulatory documents instantly. Junior staff resolve complex client questions in seconds rather than hours. Billable time increases because research time decreases.

E-Commerce and Retail: Automated product description generation at scale, intelligent inventory reordering based on demand forecasting, and returns prediction models reduce operational overhead across the supply chain.

Healthcare and Clinics: Appointment scheduling automation, patient intake forms processed by AI extraction, and after-care follow-up sequences handled without staff involvement, all built with GDPR-compliant data handling for sensitive medical information.

Every engagement begins with a technical audit. We map your existing tools, identify the highest-ROI automation candidates, and deliver a phased roadmap before any development starts.

Custom AI integration, ChatGPT plugins, and no-code AI platforms serve fundamentally different purposes. Custom integration delivers full data ownership, domain-specific accuracy, and direct connection to your existing systems. Off-the-shelf tools offer faster initial setup but impose vendor dependencies, limited customization, and data sovereignty concerns, particularly relevant for EU businesses under GDPR and NIS2.