AI Consulting

AI retrofitting is the process of adding AI capabilities (natural language search, document summarisation, 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 Most established companies operate on legacy platforms that contain years of valuable data locked behind outdated interfaces.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 TypeScript, LangChain.js, 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. Finding an answer becomes a question in plain language instead of a search through folders.

For example, a logistics company running a 12-year-old ERP can gain automated shipment status summaries, exception alerts, and natural language reporting through our middleware layer. Staff adopt it quickly because the interface lives inside the tool they already use.

When the system underneath still does its job, retrofitting is usually far cheaper than a rebuild, and we tell you when it isn't. For businesses that cannot afford 6–12 months of system replacement, it is the pragmatic path to AI-powered operations.

Four things set the price: how many systems we connect and whether they have an API, the state of their data, how many steps need a person's approval, and the request volume. There is a one-off build cost and a running cost for the AI model and hosting. You get the amount in writing, as a fixed price per phase.

What moves the build price:

• Number and type of systems. CRMs such as HubSpot or Pipedrive have documented APIs. An older ERP without an API needs exports, imports or database access, which is more work.
• Data quality. Duplicates, incomplete records and different names for the same thing have to be sorted out before AI works on them. Sometimes that is the biggest part of the job.
• Approval steps. Every step where a person approves (sending to a customer, posting, a change in the ERP) needs a review screen and a record of who approved what.
• Test set. Before launch we build tests from your real messages and documents. The more cases, and the more varied, the more preparation.
• Personal data and GDPR. A data processing agreement and rules on who can see what are part of the scope from day one.

Running costs: AI model usage depends on the number and length of requests and the model chosen; hosting depends on where the workflow runs. Every request has a cost limit, so more traffic never becomes a surprise on the invoice. An optional monthly plan covers monitoring, fixes and re-testing after model changes.

How you get a number: describe the systems and the process in the enquiry form, and the engineer who will build it replies in writing with a scope assessment. Starting prices by project type are on the pricing page.

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, with a person approving the steps that carry risk.

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. On standardised documents accuracy is high enough that manual entry becomes a spot check instead of a daily job.

2. Sentiment Analysis and Ticket Prioritisation: Customer emails, support tickets, and reviews are automatically scored for urgency and sentiment. Critical complaints surface immediately instead of sitting in a queue. High-priority issues reach the right person faster.

3. AI Features Inside Your Product: Recommendations, smart search, summaries and drafting built into your SaaS, so users get an answer instead of a filter panel. For PGAdmit, our AI recommendation engine lifted search usage 40% and signups 25%.

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 removes most of the writing time.

5. Knowledge Base Search: RAG-powered internal search across company documents, past projects, and regulatory databases. Employees ask in plain language and get an answer from your own documents, with the source shown. The assistant on this site is a live example: it answers only from what we have published.

Data sovereignty in AI means your business data stays in systems you control, the model only sees what it needs to answer a question, and nothing is used to train public models. For EU-based companies, this is both a competitive advantage and part of what GDPR expects: privacy by design and appropriate security under Articles 25 and 32.

We implement a retrieval-augmented generation (RAG) architecture where your documents are embedded into a private vector database (pgvector on Supabase, or Pinecone) hosted in EU data centres. 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 access to the models (OpenAI, Anthropic), where your data is not used for training by default. When data has to stay in the EU, we use EU-hosted models (Claude on AWS Bedrock in Frankfurt, Azure OpenAI in the EU).
• 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

For industries with heightened regulatory requirements (healthcare, legal, financial services), we deploy in a dedicated EU cloud environment.

Before launch, we build a test set from your real questions and documents and check that the AI answers correctly, cites the right source, or says it doesn't know. Assistants show where each answer came from, so your team can verify it. Where a wrong answer would be costly, a person approves before anything reaches a customer. Every request has a cost limit, so a spike in usage never turns into a surprise invoice. After every model update, the same tests run again before the change goes live.

An AI feature is not finished at launch: models change, your documents change, and users ask questions nobody planned for. After launch, we can look after it on a monthly plan: we review real conversations and failed answers, update the knowledge base, rerun the tests after model updates, watch the cost per request and fix what breaks. New features are separate projects with their own fixed price, so the monthly plan stays predictable.

AI consulting starts with 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 prioritised, 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 prioritise based on the ratio of business value to engineering effort.
3. Technical roadmap: You receive a phased implementation plan with cost estimates and expected ROI. Phase 1 targets are selected for fastest time-to-value. The roadmap itself is what confirms your real timeline, not a guess upfront.

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 AI tends to pay back fastest.

Tourism and Hospitality: Automated guest communication (booking confirmations, check-in instructions, local recommendations, review responses) takes most routine questions off the front desk. 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, prioritising 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.

Professional Services (Legal, Accounting, Consulting): RAG-powered assistants search internal knowledge bases, past case files, and regulatory documents instantly. Junior staff find the relevant rule or precedent without waiting for a senior colleague, and every answer shows its source so it can be checked. Less time goes into searching, more into billable work.

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 with staff reviewing anything clinical, 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, custom GPTs 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 customisation, and data sovereignty concerns, particularly relevant for EU businesses under GDPR.