What does Veles Digital build?

Custom AI tools, automation pipelines, conversational chatbots, and analytics dashboards. We engineer practical AI systems that eliminate waste, accelerate growth, and ship. Not demos. Every project is shaped around the specific workflows of a client's business.

How long does a custom AI project take?

A typical first-version delivery is two to eight weeks. Discovery and specification take about a week, a focused MVP runs two to four weeks, and larger integrations or analytics dashboards extend to six to eight weeks. We ship working software in short cycles rather than long-lead waterfalls.

Do you work with non-technical clients?

Yes. Most of our clients are not engineers. We translate business problems into AI solutions, handle the technical complexity end-to-end, and deliver tools your team can actually use. Onboarding includes training and clear documentation.

What industries does Veles Digital serve?

We're industry-agnostic. The common thread across our clients is teams who want concrete AI-driven outcomes like automation, better analytics, and customer support efficiency, rather than experimental AI projects. Each engagement is shaped around your specific workflows and operational reality.

How does a project start?

Book a free 30-minute discovery call via calendly.com/velesdigitalsolutions/30min, message us on WhatsApp at +381 69 251 1196, or email contact@veles.digital. We discuss your business, identify which workflows AI can improve, and propose a scoped first project with a fixed price and timeline. We respond within 24 hours.

Where is Veles Digital based and who do you serve?

Veles Digital is headquartered in Serbia and serves clients across Europe and worldwide. We operate fully remotely with English and Serbian language support, and most engagements happen via async tooling and scheduled video calls.

How much does custom AI development cost?

We don't publish fixed prices because every project differs in scope, integrations, and timeline. The discovery call is free, runs 30 minutes, and ends with a scoped quote based on your specific needs. Typical engagements range from focused two-to-four-week MVPs to six-to-eight-week multi-system integrations, with fixed scope and fixed price for the agreed phase.

What's the difference between Veles Digital and a ChatGPT subscription?

ChatGPT is a general-purpose assistant; Veles Digital builds production AI shaped around your specific workflows, integrated with your data, deployed on your own stack. ChatGPT helps individuals draft emails. We build systems that automate your support inbox, qualify your inbound leads, surface insights from your sales calls, or pull together a board-ready dashboard from your real-time data. Owned by you, not rented per seat from OpenAI.

Which AI platforms and models do you build on?

We're model-agnostic. We work with OpenAI (GPT-4o, GPT-4.1), Anthropic Claude (Sonnet, Opus, Haiku), Google Gemini, Mistral, and open-source models (Llama, Qwen) depending on the use case. Selection is driven by cost, latency, accuracy, and data-residency requirements, not vendor loyalty. We also use vector databases (Qdrant, pgvector, Pinecone) and orchestration frameworks (LangChain, custom agentic patterns) where they make sense.

Can you integrate AI with our existing CRM, ERP, or data warehouse?

Yes. Integration is the core of every project. We connect AI capabilities to Salesforce, HubSpot, Pipedrive, Notion, Airtable, Postgres, MySQL, BigQuery, Snowflake, and most modern SaaS APIs. If your stack has a documented API or webhook surface, we can wire AI into it. We don't ask you to migrate your data to a new platform.

Do you provide ongoing AI maintenance and updates?

Yes. After delivery, we offer optional retainers covering production monitoring, model upgrades, prompt tuning, and feature evolution. Most clients move to retainer after the initial build to keep their AI current as models and APIs evolve. What worked on GPT-4o today may be obsolete or expensive six months from now, and a small ongoing engagement keeps the system competitive.

How do you measure AI ROI for our business?

Every project starts with a measurable target like hours saved per week, response time reduction, conversion lift, support ticket deflection, or qualified leads per month. We instrument the AI system to report on those metrics from day one. If we can't measure the win, we don't ship it. Discovery includes a baseline measurement so the impact is provable, not anecdotal.

AI agents for real estate: qualify the 80%, escalate the 20%

AI agents for real estate that qualify the 80%, escalate the 20%.

Most property inquiries are bottom-of-funnel browsers asking a question they could find on the listing. A few are real buyers who will not wait fifteen minutes for a callback. A custom AI agent qualifies the routine, books the viewings, and escalates the real ones with full context. Your agents stop chasing tire-kickers and start closing.

Why generic real-estate chatbots fail

Most real-estate websites have a "contact us" form, a phone number, and maybe a chat widget that asks for an email before saying hello. Property portals add an inquiry button that drops a notification in the agent's phone. The result is the same: every inquiry, regardless of intent, lands on one human, who handles them in order received.

The math falls apart at any meaningful volume. Eighty percent of inquiries are browsers comparing prices, asking about features already in the listing, or fishing for "what if" pricing. Twenty percent are real buyers, and they will not wait for the agent to finish answering the first eighteen "is this still available" questions. Lead-response research is unambiguous: firms that contacted prospects within 5 minutes were 100 times more likely to make contact than those that waited 30 minutes (Oldroyd, McElheran & Elkington, Harvard Business Review). In real estate the gap is even sharper because the buyer is comparing properties in real time.

A generic chatbot makes this worse, not better. It asks a flowchart of questions, fails to answer the actual property question, and either dumps the conversation on an agent or annoys the buyer into leaving. The tool that should triage the inbox creates work instead of absorbing it.

How we build it differently

We ship a vertical AI real-estate agent wired to your actual property database, your CRM, and your agents' calendars. The agent answers questions about the property from the listing data (price, size, features, availability, photos), qualifies inquiry intent (browsing, ready to view, ready to offer, not a match), and either books a viewing or escalates with a structured summary.

For routine inquiries on standard properties at listing price, the agent owns the conversation end to end and books viewings without agent intervention. For high-intent buyers, offer negotiations, or any property-specific edge case (financing question, multi-property comparison, listing change), the agent escalates with the conversation history, the candidate buyer profile, and a suggested next action.

Tool use over pure prompting is non-negotiable. The agent never invents property details. It calls a function that returns the current listing data, the current availability, the agent's calendar slots. Quotes are deterministic. Schedule conflicts are impossible. The architectural shape we ship for Mobilni Market in B2B retail (agent reads inbound, classifies intent, looks up data via functions, escalates the unusual) maps directly to the inquiry-heavy workflow real estate runs on.

Multilingual support matters in any cross-border market. Frontier language models handle EU languages natively, including code-switching mid-conversation when a buyer from Germany messages a Belgrade listing in mixed German and English.

What we ship for a real-estate client

Inquiry classifier: agent labels every inbound message (browsing, viewing request, offer interest, off-topic) with confidence scores
Property data lookup: live integration with your listing database for price, specs, availability, photo URLs
Buyer memory: every inquirer recognized across inquiries, with prior properties viewed and preference signals
Viewing scheduler: agent calendar integration with timezone handling, rescheduling, and reminder logic
High-intent escalation: structured handoff to the agent with conversation history, buyer profile, and recommended next step
Multi-channel: web chat on the listing site, WhatsApp where dominant, portal-inquiry integrations (where the portal allows)
Agent dashboard: every active conversation visible in real time, one-tap takeover, one-tap return to the agent
Multilingual: native EU-language handling with mid-conversation code-switching

Frequently asked

Can it integrate with our MLS / property portal feeds?

Yes, where the portal exposes an API or a structured feed (most major EU and US portals do). We integrate as a read-only consumer for listing data plus a write-back for inquiry-status updates where the portal supports it. If your agency runs a custom property CRM, we integrate via API or direct DB connection. The agent never serves stale property data.

How does this handle GDPR / client privacy for inquiry data?

Inquiry conversations contain personal data, so we treat them as such from day one. Data Processing Addendum signed before any production data flows. For sensitive workloads we can run inference on-premise or via privacy-preserving providers so candidate data never trains an external model. Full audit trail of every conversation. The compliance shape is the same we use for our recruitment work, where the data sensitivity is similar.

How does it integrate with our CRM (Salesforce, HubSpot, Pipedrive, etc.)?

Through your CRM's API. The agent reads buyer/contact records to recognize returning inquirers, writes new contacts on first inquiry, updates pipeline stage when viewings are booked or offers come in. Status writes are explicit and logged, not silent. The CRM stays the source of truth for buyer data.

Can the agent handle offers and negotiations?

No, and this is intentional. Offers and price negotiations are exactly the human-in-the-loop moments your agents should own. The AI agent recognizes when a conversation is escalating from inquiry to offer (vocabulary cues, explicit offer mentions, financing questions), packages the conversation context, and hands off to the human agent immediately. Anything below that threshold (availability, viewing logistics, property specs) the agent handles.

Does this work for a multi-property portfolio or only single agencies?

Both. The architecture is the same; multi-property agencies just have a larger listing database and more agents on the dashboard. We have shipped systems that handle hundreds of active SKUs in B2B retail and the pattern maps cleanly. Boutique agencies with 20-50 active listings work the same way, just with less data plumbing.

How long until I see results?

A focused real-estate agent ships in 4-6 weeks from kickoff to production, depending on portal integration complexity. The methodology and milestone breakdown lives on our process page. First 1-2 weeks post-launch we run close monitoring on real inquiries, tune the qualifier thresholds, and adjust escalation rules. Inquiry-to-viewing conversion typically lifts meaningfully in month two as the agent learns your specific buyer language and listing types.

AI agents for real estate that qualify the 80%, escalate the 20%.

Why generic real-estate chatbots fail

How we build it differently

What we ship for a real-estate client

Frequently asked

How we estimate the work

Ready to talk about a real estate project?