AI agents & agentic workflows
Agents that do the work — with the controls to trust them.
We build multi-agent systems on OpenAI and Anthropic models that take a trigger — an email, a document, a form — and complete the workflow across your systems. Evaluation, guardrails, a human hand-off and monitoring are part of the build, not an afterthought.
What we build
The kinds of systems we're asked for.
Not chatbots. Agents with a job to do, wired into the systems where the job lives.
- 01
Multi-agent pipelines
Specialised agents chained into a pipeline with a defined contract between each step, so a failure is caught where it happens instead of at the end.
- 02
Email- and document-triggered agents
An agent watches a mailbox or a document drop, understands what arrived, and completes the work across the systems involved — raising what needs a decision.
- 03
Personalised plans from sensitive data
Pipelines that turn sensitive personal data into an individual plan or recommendation — built with data minimisation and strict access boundaries, because the data is the kind you cannot afford to leak.
- 04
Lead capture & qualification
Agents that answer inbound interest, ask the qualifying questions, enrich what they learn and hand a warm, structured lead to your team or CRM — with the whole conversation on the record.
- 05
Agents inside your existing tools
Integration is most of the work. We connect agents to your CRM, ERP, ticketing, document stores and in-house systems through their APIs, with least-privilege credentials per agent and per action.
- 06
Document understanding & extraction
Structured data out of unstructured inputs — submissions, invoices, contracts, forms, scans — validated against your schema and your business rules before it goes anywhere.
How an agentic workflow runs
Trigger, understand, act, hand off — every time, on the record.
Whatever the use case, the shape is the same. Designing it this way is what makes an agent dependable enough to be given real work.
- 01
A trigger
An email, a document, a form submission, a webhook, a scheduled check. The agent never has to be asked — it starts when the work shows up.
- 02
Understanding
The agent works out what is being asked, extracts the entities involved and resolves them against your systems. Low-confidence reads are flagged, not guessed.
- 03
Planning & tool use
It decides which steps are needed and calls the tools you've allowed — reads first, writes only through validated actions with a defined scope.
- 04
Action across systems
Records updated, documents filed, messages drafted, downstream systems notified. Every write is validated the way your own code would validate it.
- 05
Hand-off & record
Anything outside policy, over a threshold or uncertain goes to a person with the context attached. Everything — including what was skipped — is logged and traceable.
The specifics — which triggers, which tools, which thresholds — are designed with your team during discovery.
How we build them responsibly
The controls are the product.
An agent is only useful if your team can trust what it did while they weren't looking. These are the practices we build in on every engagement.
- 01
Evaluation before trust
We build an evaluation set from your real cases before the first agent runs, and re-run it on every prompt, tool or model change. An agent that can't be measured doesn't ship.
- 02
Guardrails around every action
Agents can only call the tools you've allowed, with inputs validated and outputs checked against your rules. Writes are scoped, idempotent and reversible where the system allows it.
- 03
A human hand-off that actually works
Confidence thresholds and policy rules decide what goes to a person. The hand-off carries the agent's context and reasoning, so a reviewer sees what the agent saw and can act without re-investigating.
- 04
Privacy for sensitive data
Sensitive fields are minimised or redacted before they reach a model; we use API access where your data is not used for training, can keep processing inside your cloud and region, and give each agent least-privilege access.
- 05
Monitoring in production
Every run is traced — inputs, tool calls, outputs, cost and latency — with alerts on drift, error rates and spend. You can see what the agents did and why, and so can we.
- 06
Model choice without lock-in
We build on OpenAI and Anthropic models behind one interface, pick per task on measured quality, cost and latency, and keep the evaluation set — so switching models later is a re-run, not a rewrite.
How an engagement runs
Start with one workflow that matters. Prove it. Then widen.
The fastest way to find out whether agents work for you is a narrow, real workflow with real volume — not a demo.
- 01
Discovery & the first workflow
A short call, then a look at the workflows, the systems behind them and the data involved. We pick one with real volume and a clear definition of done, and write down how we'll measure it.
- 02
Evaluation set & design
Before building, we assemble a test set from your real cases and design the agent's tools, boundaries and hand-off rules with your team.
- 03
Build, pilot, measure
The agents run on real work in a controlled roll-out, with a person reviewing at first. We tune against the evaluation set until the numbers hold.
- 04
Run & extend
Monitoring, alerting, cost control and a named engineer. Then the next workflow, with the harness already in place.
FAQ
Frequently Asked Questions
What teams ask before they hand an agent real work.
Which models do you use?
OpenAI and Anthropic models, chosen per task on measured quality, cost and latency rather than by default. We build behind one interface and keep your evaluation set, so switching models later is a re-run, not a rewrite. Where a deployment needs it, we can work with models hosted inside your own cloud.
How do you handle sensitive personal data?
By design, not by policy document. Sensitive fields are minimised or redacted before they reach a model, each agent gets least-privilege access to your systems, and we use API access where your data is not used to train the provider's models. High-stakes outputs can be routed to a person first.
What if the agent gets it wrong?
It should never get it wrong silently. Low-confidence or out-of-policy cases go to a person with the context attached, every action is logged and reversible where the system allows it, and the evaluation set grows with every miss so the same mistake doesn't ship twice.
Do we need our own AI team to run this?
No. We build, run and monitor it with you, and hand over runbooks, traces and the evaluation harness. If you have an ML or platform team, they get the keys from day one; if you don't, we stay the team.
Start a project
Tell us what you're building. We'll tell you whether we can help.
One 45-minute call, no slide deck, no pressure. If we're the wrong fit we'll say so — and point you toward someone who isn't.
- Response time
- Within 24 hrs
- Discovery call
- No cost, no deck
- Where we're based
- Belgrade · Remote-first