Agentic AI - Autonomous Workflows for Production
Most teams hit an efficiency ceiling: every new tool means another person copy-pasting between tabs. Agentic AI is digital labor - autonomous workflows that span your tools, run on your rules, and scale without hiring.
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When it fits
- The workflow spans more than one system and has a clear, instrumentable success metric
- You can name the 5–10 actions the agent should be allowed to take
- Failure has a recoverable cost - humans can review or undo within a reasonable window
- You want to remove repetitive labor, not eliminate human judgment from a high-stakes decision
When it doesn't
- The 'agent' is really a chatbot - single-turn Q&A doesn't need agency
- The decisions are irreversible and high-stakes (medical diagnosis, legal judgment) without human sign-off
- Your tools don't have APIs and there's no appetite to build the integration layer
Process
We start by mapping the workflow and writing the tool contracts before any code. Sprint 1 ships a narrow vertical slice with full observability. Sprints 2–4 expand the action set, tighten guardrails, and roll out behind a feature flag. Evaluation is continuous - every release is gated on the eval harness, not on whether it 'looks right'.
Full delivery processPricing
Typically a fixed-price 8–14 week build, or an AI Pod (4–7 specialists) on a quarterly engagement. Outcome-based pricing where the metric (tickets resolved, documents processed, hours saved) is instrumentable.
See engagement modelsCase studies
Residential Access Control System
A beautifully simple portal to interconnected secure environments, delivering ease of use and peace of mind for residential communities.
AI-Powered Applicant Tracking System
Comprehensive ATS solution with AI-driven candidate matching, automated resume parsing, and real-time recruiter-candidate communication serving 10K+ monthly candidates.
How we'd build it
Blueprints: example projects with the plan, the architecture and the targets we'd start from.
Dental Booking Platform With an AI Receptionist
Online booking across clinics and chairs, reminders and recalls to cut no-shows, and an AI phone receptionist that hands off to your team.
AI Claims Intake for Insurance Brokers & MGAs
AI that turns FNOL emails, forms and calls into structured claim files, reads the documents and routes each claim; handlers make every decision.
FAQ
- How is this different from just using a chatbot?
- Chatbots answer questions. Agents take actions: filing tickets, updating records, calling APIs, kicking off workflows. The engineering challenge is making those actions reliable and reversible at scale, which is where most agent projects fall over.
- Which frameworks do you use - LangChain, CrewAI, AutoGen?
- Whichever fits the problem, but the framework choice rarely matters as much as the architecture decisions around it: tool boundaries, eval design, and observability. We're framework-agnostic and have shipped on all of the above plus pure orchestration code where the framework was the bottleneck.
- How do you stop an agent from doing something stupid?
- Three layers. First, scoped permissions - agents only see the tools they need. Second, action gates - high-impact actions require human approval until the eval harness shows they're safe. Third, a kill switch and rollback runbook in production from day one.
- How do you measure whether an agent is actually working?
- Workflow-specific success rate, cost per successful action, escalation rate to humans, and end-to-end latency. We define these with you in discovery and instrument them in production from sprint one - not after launch.
Who builds it
Our own team, working from our office in East Sarajevo, Bosnia and Herzegovina. The engineers on your scoping call are the ones who build it.
Meet the team