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 Automation · Recruiting

AI automation for recruiting: one workstation, not five tabs.

Recruiters spend more than half their day moving data between sourcing platforms, research subscriptions, CRMs, email tools, and scheduling apps. The relationships live in their heads; the admin lives across five browser tabs. We replace the stack with one AI-driven workflow that keeps the data consistent and the recruiter focused on conversations that matter.

Why most "recruiting tech stacks" cost more than they save

The modern recruitment stack is a graveyard of point tools. A LinkedIn Recruiter seat. A scraping platform for sourcing. A research subscription for company intel. An ATS for pipeline. A CRM for client relationships. An email tool for outreach. A scheduler for screens. A mock-interview tool for pre-screening. Each tool solves one slice of the workflow. None of them talk to each other in a way that meaningfully reduces work.

The recruiter sits in the middle, copying data between tabs. Sourced profile gets typed into the ATS. ATS notes get pasted into the CRM. Mock-interview results get summarized in an email. Scheduling links get sent in another email. Every transition costs minutes and introduces inconsistency. The "automation" promise of the stack lives in the marketing, not the day.

SHRM benchmarks the US average cost-per-hire at $4,129 per non-executive role (SHRM Benchmarking Report). Industry analyses of LinkedIn Talent Solutions data show AI-enabled workflows cutting that figure by roughly 27% through automated sourcing, screening, scheduling, and pipeline updates (Senseloaf AI on LinkedIn-research cost reductions). The savings come from collapsing the stack, not adding to it.

How we build it differently

We architect the workflow first, then build the AI automation around it. Not the other way around. The first session with a recruitment client is a workflow map: every step from "open role" to "candidate accepted offer", every tool that touches each step, every place data flows or gets re-entered. Most stacks have 40-60 of these steps. The map shows you which 20 you can collapse.

Then we build one workstation. AI sources candidates from professional networks and directories matching ICP. Scoring engine ranks profiles against the role. CV parser extracts structured skills. Mock interview pre-screens before recruiter time gets booked. Email sequencer handles outreach with timing tuned per market. ATS writes happen automatically as pipeline stages change. The recruiter opens one app, sees the pipeline, and works the conversations the AI surfaces.

Tool use over pure prompting is a hard rule everywhere data integrity matters. The AI does not "decide" who is qualified, it calls a scoring function we wire to your ICP. Same for CV skill extraction, ATS writes, calendar bookings, email send. Every decision the AI takes is auditable and reversible.

Built as a desktop-grade workstation, not a browser tab. Recruiters work from one app across a full day, not in a Slack-notification-driven attention shred. Shared cloud database keeps the team in sync across regions and recruiters.

What we ship for a recruitment client

Sourcing engine: configurable scraping profiles for the major professional networks and business directories, tuned to your ICP and geography
ICP scoring function: structured candidate-fit scoring with deterministic output the recruiter can audit
CV parser: skill extraction and structured profile data, written into the unified workstation
Real-time research engine: live AI enrichment of candidate and company profiles before the recruiter opens the candidate
Mock-interview module: async pre-screening conversation that runs before human recruiter time gets booked
Email sequencer: outreach campaigns with timing tuned per market, threaded back into the candidate timeline
ATS write-back: pipeline stage changes, candidate notes, status updates sync into your existing ATS automatically
Recruiter dashboard: every active candidate visible in one place, AI-surfaced gaps, strengths, and suggested next steps
Shared cloud database: real-time pipeline state across multiple recruiters and multiple regions

// Proof from production

Already running in production

Recruitment OS

People Up

This is exactly what we built for People Up, a recruitment firm placing candidates across the EU and Canada. Their team was running five separate tools; we replaced them with one workstation that sources, researches, scores, mock-interviews, and preps every recruiter call. Live in production at peopleup.llc. Same workflow patterns map to any recruitment team whose stack has outgrown its usefulness.

Case study

// FAQ

Frequently asked

How is this different from "AI agents for recruiting"?

AI agents own conversations with candidates. AI automation owns the workflow around those conversations. Most recruitment teams need both, and the right entry point depends on where the pain is sharpest. If recruiters are drowning in candidate messages, start with AI agents. If they are drowning in admin between five tools, start here. Often we build both in phased waves.

Does it integrate with our existing ATS?

Yes. The workstation reads from and writes to your ATS via API. Pipeline stage transitions, status updates, and candidate notes flow automatically. Your ATS remains the source of truth for legal hire records; the workstation is the day-to-day surface recruiters actually work in.

What about GDPR and candidate-data privacy?

We treat candidate data as personal data from day one. Data Processing Addendum signed before any production data flows. Encrypted local cache on every recruiter machine. API credentials managed at OS-keyring level. For sensitive workloads we can run inference on-premise or via privacy-preserving providers so candidate data never trains an external model.

How long does the first version take?

A focused first-phase deployment ships in 6-10 weeks from kickoff, depending on stack complexity and ATS integration scope. Full scoping and milestone breakdown lives on our process page. First milestone is always self-contained and shippable: a sourcing-plus-scoring engine that real recruiters use in week 6, even if the full workstation comes later.

What is the ROI versus the existing stack?

The honest answer is "it depends on the stack". Most clients we have shipped to had been paying for 5-8 SaaS seats per recruiter. The workstation replaces a meaningful slice of those, plus the time savings on data-shuffling work. Most teams see the build cost amortized inside 12-18 months purely on seat consolidation, before counting recruiter-productivity gains.

Is this a multi-tenant SaaS we can subscribe to?

No. Each recruitment workstation we ship is custom-built for the team that commissions it. The architecture is shippable, so the second build is faster than the first, but every team gets their own deployment with their own ICP definition, scoring rules, integrations, and data isolation.

How we estimate the work

Pricing follows scope, not a fixed rate card. The full breakdown of how we scope, ship in milestones, and what each phase includes lives on the process page.

See our process →

Ready to talk about a recruiting project?

A discovery call is free and runs about 30 minutes. We map the problem, tell you if we are the right fit, and walk out with a first-milestone outline.

book a call Email us directly