Automated evaluation in CI
Regression tests for prompts and extraction against golden sets.
How we engage
We treat AI as one layer in your operating system: first we stabilize the data flow and ownership, then add models where they cut processing time or reduce errors - with checkpoints, logs and rollback you can defend in a review.
Often paired with: Analytics · Web development · Content & CMS.
- Discovery - we map reality, not the org chart; prioritized by ROI.
- Integration - APIs, webhooks and queues that don’t fail silently.
- AI where it adds value - extraction, routing, drafts - with human checkpoints.
From messy reality to monitored automation
We don’t patch a broken process with an LLM - integrations and rules first, AI after.
Discovery Map & ROI
Connect APIs & queues
Automate Rules & bots
AI layer Assist & extract
Govern Logs & review
Process and systems discovery
We trace how the work actually gets done: who enters the data, which SaaS holds the source of truth and where Slack turns into a database - before we automate the wrong part.
We produce swimlane diagrams, a trigger inventory and a ranked backlog: quick wins versus structural fixes. AI is proposed only where a model can beat a rule today.
ROI is stated plainly - hours saved, fewer errors or faster collections - so finance understands why the build order isn’t alphabetical.
Integrations and data pipes
REST, GraphQL, webhooks, queues and file drops - designed with retry logic, idempotency and monitoring for when HubSpot fails at 2 a.m.
We document authentication (OAuth, API keys, mutual TLS), rate limits and backoff between retries. Payloads are versioned so downstream automations don’t break when a field is renamed.
When data needs to go to a warehouse or lakehouse, we align schemas with the team that runs BI, so operational automations and analytics don’t drift apart.
Workflow automation
Make, n8n, Zapier or lightweight workers in your cloud - conditional logic, human approvals and error paths operators can follow.
We pick tools your team can maintain, with naming conventions and folder structure. Every workflow is monitored - not “it worked on my machine.”
When volume or regulation calls for workers in code, we clearly separate what lives in the vendor UI from what lives in the repository - and how changes get reviewed.
AI layer: agents, RAG and extraction
Models grounded in approved sources, structured output and evaluation sets - plus human review for anything client-facing or legally sensitive.
Use cases we ship: document classification, entity extraction, ticket summarization, lead routing suggestions and internal Q&A over handbooks and policy PDFs (RAG).
We document model choice, temperature limits, red-team prompts and what happens when the API goes down - fallback to rules, not silence.
Governance, security and handoff
PII boundaries, retention, access control and change management, so security and legal can sign off without blocking every iteration.
We map which fields never leave the region, which automations need dual control and how to roll back a bad model deployment. Vendor SLAs and subprocessors are summarized for the review package.
Handoff includes training and a clear owner for updating the glossary - whether that’s ops, IT or a shared group.
Client voice
“Incoming requests no longer live in three inboxes - routing is clear, and the team trusts the logs when something breaks.”
Roadmap
In pilot
Exploring
Coming soon
Exploring
On the horizon
We invest where client roadmaps are heading - carefully piloted, not as slideware.
Tool-calling agents
Bounded agents that open tickets and update the CRM behind human-approval gates.
Private model hosting
On-VPC inference patterns for stricter data residency.
Voice-of-customer clustering
Thematic grouping of support transcripts with review queues.
What a typical two weeks looks like
A typical slice for a first workflow or AI pilot; longer programs stack these rhythms.
Week 1
- Access to in-scope systems (sandbox preferred)
- Process workshop + draft swimlane
- Integration spike on riskiest API
Week 2
- v1 workflow build in a staging / dev workspace
- Monitoring + alert wiring
- Operator walkthrough & failure drills
What we need from you
- A named ops owner for approvals
- API credentials or vendor admin
- Sample messages and tickets (no confidential data)
- Security reviewer contact (if required)
You might be better off starting elsewhere
If the real problem is frontend performance, design clarity or editorial chaos, automation won’t fix the underlying bottleneck.
FAQ
AI & business automation
Stack choices, security and what comes after the first workflow ships.
Ask a specific questionNo. We choose the vendor based on policy, latency and language - hosted models, Azure/OpenAI, Anthropic or smaller open-weights models when needed. The architecture stays portable where it matters.
Structured outputs, retrieval grounding, confidence thresholds, human review queues for risky classes and evaluation sets we rerun whenever prompts change - not vibe-based QA.
Yes, when volume and compliance allow. We document the limits honestly; when the logic outgrows the UI, we propose a thin code runner or a split of responsibilities so you don’t get stuck.
We always hand off with playbooks. You can run day-to-day changes yourselves, keep us on a monthly hygiene slot, or blend the two - the RACI is explicit in the SOW.
By default we go with minimization, redaction and regional routing. The legal text is yours - we provide the technical controls and data flow diagrams for reviewers.
AI & business automation - all of Serbia
Our studio is in Novi Sad, and we work with companies across Serbia - Belgrade, Niš, Kragujevac, Subotica, Novi Pazar, Čačak, Pančevo and beyond. The way we work is the same whatever the city: video calls for the weekly rhythm, in-person visits when they make sense.
Tell us what you’re shipping
Describe the workflow that hurts most - intake, routing, renewals, reporting - and we’ll propose the smallest shippable automation with a clear AI boundary.