Belgrade · Global · AI-First
Intelligent systems that perceive, reason, plan, and act — continuously, autonomously, at scale.
The paradigm has shifted. We are past the era of reactive software — systems that wait, that answer, that execute only what they're told. The next generation of intelligent systems is agentic: perceiving context, forming hypotheses, orchestrating tools, and driving outcomes through continuous closed-loop reasoning. Not chatbots. Not automation scripts. Cognitive architectures that work.
Every agent we build is grounded in a layered cognitive architecture — from low-level perception and tokenization through multi-step reasoning, long-term memory consolidation, and calibrated action execution. Nothing is a black box.
Our agents implement the ReAct paradigm (Reasoning + Acting) extended with Tree-of-Thoughts exploration via Monte Carlo Tree Search. Long-horizon tasks are decomposed through chain-of-thought scratchpads, with self-critique loops enforcing Constitutional AI alignment at every decision boundary.
Memory is not a context window — it is a multi-tier system spanning working memory (KV-cache, 128k context), episodic memory (HNSW-indexed vector stores with cross-encoder re-ranking), and semantic memory (knowledge graphs with entity-level relation extraction). Agents remember what matters. They forget what doesn't.
We design and build systems where AI is the core architecture — not a feature layer bolted on top of existing software.
AI agents that operate independently through continuous perception-reasoning-action loops. They decompose ambiguous objectives into executable subplans, invoke tools, handle failure modes through retry-and-replan heuristics, and converge on correct outcomes without human intervention. Built on the ReAct paradigm with MCTS-based lookahead for long-horizon task completion.
ReAct / MCTS / Tool UseDirected acyclic task graphs (DAGs) of specialized subagents — each fine-tuned or prompted for a narrow cognitive role — coordinated by an orchestrator through structured message-passing protocols. Collective intelligence emerges through specialization and delegation, not monolithic models. Reduces token overhead by 60–80% vs. naive chain architectures while improving correctness on compositional tasks.
DAG Orchestration / Message Passing / SpecializationProduction-grade pipeline architectures chaining foundation models (GPT-5.4, Claude Opus 4.6, Gemini 3.1 Pro, Llama 4 Maverick) with external APIs, persistent memory systems, code interpreters, and retrieval engines. Streaming inference with latency budgeting, parallel fan-out with result aggregation, and observable execution traces for debugging and audit.
LangGraph / Streaming / ObservabilityEnterprise-grade RAG systems with hybrid retrieval (BM25 sparse + dense embedding, cosine similarity), cross-encoder re-ranking for precision, and agentic query decomposition. Indexes any corpus — documents, codebases, databases, wikis — making institutional knowledge instantly queryable by agents with sub-100ms P95 retrieval latency.
HNSW / Hybrid Retrieval / Cross-Encoder Re-rankingFull-stack products where AI is the foundation, not a feature. Applications with persistent agent sessions, multi-turn context management, user-delegated task execution, and real-time streaming interfaces. From architecture to deployment — containerized, observable, and horizontally scalable on AWS Bedrock, Vercel Edge, or on-premise Kubernetes clusters.
Full-Stack / Persistent Sessions / K8sAgent independence is dynamically calibrated to task criticality and epistemic uncertainty. Below a configurable confidence threshold ε, agents execute fully autonomously. Above it, they surface decision points with full reasoning traces, confidence intervals, and recommended actions — preserving human judgment at high-stakes nodes.
Every agent decision is explainable: a logged sequence of retrieved contexts, tool invocations, intermediate scratchpad reasoning steps, and calibrated posterior confidence scores. Humans maintain epistemic oversight without cognitive overhead — you see exactly why the agent did what it did, at any granularity.
Cognitive load redistribution through structured task delegation. Humans define objectives and constraint boundaries; agents handle the full combinatorial search space of subtask execution. The result is a distributed cognition system — human judgment at the strategic layer, machine execution at the tactical layer.
Agent behavior is shaped by RLAIF (Reinforcement Learning from AI Feedback) and direct human preference signals. Constitutional constraints are enforced at inference time via reflection loops. Reward model ensembles prevent Goodhart's Law pathologies — agents optimize for genuine outcomes, not proxy metrics.
Agents don't replace human cognition — they amplify it. The human brings judgment, values, domain intuition, and moral agency. The agent brings tireless execution, perfect recall, infinite parallelism, and combinatorial search across possibility spaces no human could traverse in a lifetime.
This is not automation. Automation executes predefined procedures. Agentic AI reasons about novel situations, selects appropriate tools dynamically, recovers from unexpected failures, and improves its own strategy through in-context learning — all within a single task session.
We engineer the interface between these two cognitive regimes — calibrating the autonomy gradient, designing the feedback loops, and building the oversight infrastructure that makes human-agent collaboration trustworthy at scale.
Every engagement begins with one question: where can an agent take cognitive ownership? We design systems with autonomy as the default — AI is not bolted onto existing software, it is the foundation from which everything is built. This inversion changes what's possible.
Agent performance is measured, not assumed. We instrument every system with structured evals, latency budgets, and precision/recall metrics specific to your task distribution. Improvement is empirical and continuous — not a one-time deployment.
We don't build demos. Every prototype is architected for production: observability, error handling, graceful degradation, and horizontal scalability are requirements, not afterthoughts. You get a system you can run, monitor, and trust — in weeks, not quarters.
Every client works directly with the founder. No account managers. No briefing chains. You get a technical partner who understands your business model, questions your assumptions, and builds systems that serve your actual goals — not a requirements document.