A Swiss climate-tech B2B platform was hitting ceilings. We embedded as the architecture voice, rebuilt the service boundary incrementally, and shipped a retrieval layer that made their AI roadmap viable in weeks.

Rebuilt a scaling platform's architecture without pausing the business.

From scope doc to production. Editorial platform with AI-assisted content workflows. One designer, two engineers, six weeks, live.

MVP shipped in six weeks.

From ICP definition to campaign-ready outreach in 48 hours. 98% verified email coverage, 100x lower copywriting cost, 15-20 hrs/week of manual work reduced to under one.

AI-powered B2B GTM pipeline.

Let's talk about the ((next one)).

Shipped. In production.
[[Making money.]]

If you ever need an inline link inside copy, ping engineering. We'll add a {link:slug} marker.

Let's start with a [[conversation]].

A team that
[[ships the right thing]].

Built around senior expertise,
[[shaped to your goal]].

Small enough to ((know each other)),
[[senior]] enough to ship.

Djuradj Ivanić

Todor

The bench

Four ((non-negotiables))
[[we hire and ship by]].

Every engineer on the project has shipped this before. No 'learning on your dime,' no junior pyramid hidden behind a senior frontman.

Senior, always

We push back hard on scope and architecture before we write code. Once we've agreed, we ship without drama.

Argue early, ship calmly

We treat models like services that need testing, monitoring, and fallbacks. The boring parts are the ones that make AI work in production.

AI is plumbing, not magic

We're allergic to lock-in. Docs match the code, evals match the prompts, the team can run it without us. That's how engagements end well.

The product is yours

[[Send us your CV]] anyway.

Build software you'd
[[want to brag about]].

Roles ((currently open)).

Three conversations,
[[no take-homes]].

30 minutes with Djuradj. We talk about your work, you ask anything. No technical screening here.

Intro call

90 minutes pairing on a small problem from one of our actual codebases. We'll debug, talk through trade-offs, see how we work together.

Pair session

We reach out to a couple of references you choose. If both directions feel right, we send an offer within a week.

Reference + offer

What you actually get,
((not the brochure)) version.

Paid in EUR. Based on what you'd command at a senior product company in your timezone, not what local 'market rate' says.

Competitive salary

Not 'hybrid.' Most of the team works from home most of the week. Belgrade office exists for those who want it.

Genuine remote

Everyone you work with has shipped real systems. PR review is a conversation between equals.

Senior peers

Macbook Pro of your choice, plus whatever else you actually need to do the work well.

Equipment

Books, courses, conferences. We don't itemise it; we just trust you to spend it well.

Education budget

Core overlap with EU timezone for a few hours a day. Otherwise: ship the work, we don't care when.

Flexible hours

What's the [[shape]]?

Have a problem worth
((solving))?

Have a problem worth ((solving))?

Senior team.
AI in the workflow.
[[Solutions shipped at scale.]]

From first call to ((shipped system)) in under four moves.

One working session. We map the constraint, name the trade-offs, and leave with a written plan. Not a sales doc.

Discovery

The smallest useful system. Clear boundaries, honest timelines, the data model the business actually needs.

Architecture

Weekly drops. Working software on Fridays. AI assists the build loop, so you see progress without the long dark window.

Ship

Docs, evals, and a team that can run it without us. We stay on call as long as it's useful.

Handoff

Where senior engineering meets [[AI as a daily tool]].

For founders and operators turning an idea into a shipped product. Architecture, engineering, QA, release. Built by a senior team that uses AI in the build loop and ships AI-native patterns where they earn their place.

Custom Product Development

Where AI is the product, not the decoration. LLM workflows, retrieval pipelines, agentic GTM and ops automation, with the eval, observability, and fallback work that separates a system from a demo.

AI Integration & Automation

Senior engineers embedded in your team, fluent in AI-heavy stacks from day one. Same timezone, same Slack, same standups. Useful on agentic, RAG, and eval work without a ramp-up quarter.

Staff Augmentation

Climate-Tech SaaS

Editorial Platform

TechCompanyNews

B2B GTM System

Climate Ops Co.

B2B Platform X

From scope doc to production. Editorial platform with AI-assisted content workflows.

From ICP definition to campaign-ready outreach in 48 hours. 98% verified email coverage at 100x lower copywriting cost.

How we collect, use, and disclose personal information when you use danacious.com.

Privacy Policy

Boring infrastructure,
[[reliable AI]].

Where does AI add value, and where would it just add latency? We separate the genuine wins from the resume-padding.

Map the workflow

Eval datasets, retrieval indices, prompt registry, structured outputs, failure handlers. The infra you'd have built six months from now, built first.

Build the rails

Real users, low-risk path, eval scoring on every response. Confidence comes from data, not from vibes.

Ship behind a flag

Dashboards your team actually checks. Runbooks for when things drift. We leave when you can answer 'is the AI working?' without paging us.

Observability + handoff

Let's talk about ((what it would take))
to put it in front of users.

AI that ((actually))
[[earns its place]] in production.

AI features that
((survive Mondays)).

Your AI proof-of-concept worked.
((Then what?))

Models

Patterns

Infra

Observability

Smallest useful system,
[[shipped weekly]].

We sit with the team, map the constraint, and leave with a written plan. Not a sales doc. The actual scope, with trade-offs called out.

Discovery: one working session

Service boundaries, data model, observable interfaces. We design for the next two engineers, not just the demo.

Architecture: clear boundaries

Working software on Fridays. You see progress every week, course-correct early, and never sit in a 'big reveal' meeting that goes sideways.

Build: weekly drops

Docs that match the code. Evals where they matter. We stay on-call as long as you need; the product was always yours.

Handoff: your team owns it

Let's talk about ((what you'd ship))
in the next twelve weeks.

Custom software
built for the [[long haul]].

Software that earns its [[runtime]].

Off-the-shelf SaaS got you ((this far)). What now?

Frontend

Backend

AI layer

Senior engineers,
[[no junior tax]].

We propose 1–2 engineers from our bench whose skills match your stack. You meet them. If the fit isn't right, we keep matching.

Match in days

They join your Slack, your standups, your repos. PRs in week one. No 'context discovery phase' billed to you.

Embedded onboarding

EU-based, 09:00–18:00 CET. Pairing happens live, code review is synchronous when it needs to be.

Same timezone, same hours

No long contracts. Scale up for a launch, scale down after. Notice period in weeks, not quarters.

Month-to-month

Tell us the gap.
We'll tell you who [[fits]].

Senior engineers,
((on your team)) by Monday.

Throughput, ((without))
[[the recruiting tax]].

Hiring is ((slow)).
The roadmap [[isn't waiting]].

Data + ML

Mobile

Tell us the ((problem)). We'll tell you the shape.

Three engagements,
one [[senior team]] behind them.

Web and mobile products built from first principles. Architecture, engineering, QA, release. We start where the spec ends, ask the hard questions early, and ship working software on Fridays. AI patterns built in where they earn their place.

LLM-powered workflows, retrieval pipelines, internal tools. We build the unglamorous plumbing (evals, observability, fallbacks) so the model layer stops being a demo and starts being a system you can trust on Mondays.

Senior engineers embedded in your team, onboarded in days. Same timezone, same Slack, same standups. Scale throughput without the recruiting overhead, and skip the junior-to-lead training tax.

A senior engineering team building custom software and AI systems. We use AI as a daily tool, shipping at a cadence that lets a focused team deliver what a much larger one would.

danacious. Senior team. Solutions shipped at scale.

danacious

The terms governing your use of danacious.com.

Terms & Conditions

Rebuild the bones,
[[ship every Friday]].

Two weeks of discovery: actual workflows, actual pain points, actual data flow. We left with a written architecture diff.

Map the seams

Reshape one boundary at a time. Old code keeps running. New code gets observable interfaces. No big-bang rewrite.

Service boundary first

Stand up the retrieval layer the AI roadmap needed. Once it existed, three planned features became weeks of work, not quarters.

Retrieval as foundation

By month four, the client's team was making architecture decisions independently. We stayed on retainer for the hard ones.

Hand the keys back

Three years in,
the platform was ((fighting back)).

Tell us the [[constraint]].
We'll tell you the shape.

Rebuilt a scaling platform.
[[Without pausing]] the business.

A Swiss climate-tech B2B platform was hitting ceilings: slow releases, fragile data model, AI features that kept slipping. We embedded as the architecture voice and shipped the rebuild incrementally.

AI features in production,
((and trusted)).

Cut scope.
[[Ship the spine]].

Four user flows, picked from a list of seventeen. Everything else got 'phase 2' on a Notion doc.

Spine, not skeleton

Auth, payments, email: buy. Editor, AI workflows, the differentiated bits: build.

Buy where possible

Real users, real content, real data. Flag-gated, measured, iterated.

Ship Friday week 3

Weeks 4–6: only changes the data demanded. Resisted every 'one more thing.'

Bake the feedback in

Six weeks to validate
or ((kill the idea)).

Let's talk about
((what fits)) in six weeks.

MVP shipped in
((six)) [[weeks]].

From scope doc to production: an editorial platform with AI-assisted content workflows. One designer, two engineers, a tight scope, no detours.

AI-powered GTM pipeline for German logistics clients.

Live, measured,
((and validated)).

Bought

Seven stages,
[[fully automated]].

Structured filters applied to a B2B database. 262 companies matched across eight markets.

ICP definition and filtering

Automated role and seniority matching. 3,060 contacts identified: CTOs, CIOs, VPs of Engineering.

Decision-maker discovery

Smart multi-provider waterfall for email finding, then triple-layer verification. 98% coverage at 60% lower cost than single-provider.

Email waterfall and verification

0-100 scoring across nine weighted categories. Then Claude-powered personalized outreach using proven copywriter frameworks. $1.80-$2.20 per 500 leads, versus $50-$250 with market tools.

Lead scoring and AI copywriting

Reaching the right people
at scale was ((broken)).

Tell us your ICP.
We'll show you the [[pipeline]].

AI-powered GTM pipeline.
[[98% coverage]], 100x cheaper.

A German logistics company needed to reach CTOs and CIOs across eight European markets. Manual prospecting consumed 15-20 hours a week and produced inconsistent results. We built an end-to-end AI pipeline that takes an ICP definition and produces ranked, campaign-ready outreach in 48 hours.

Shipped. In production.
Making money.

Rebuilt a scaling platform's architecture without pausing the business.

MVP shipped in six weeks.

AI-powered B2B GTM pipeline.

Let's talk about the next one.

Shipped. In production.Making money.

Rebuilt a scaling platform's architecture without pausing the business.

MVP shipped in six weeks.

AI-powered B2B GTM pipeline.

Let's talk about the next one.

Shipped. In production.
Making money.