Nine ways to start, one way of working.
Each line below has its own page, with what we build, what stays human, indicative pricing, and the questions clients actually ask us. If you are not sure where to start, the selector below takes you to the right page. For companies looking for the term used correctly, AI consulting for companies means engineers who write and operate code inside your business, not consultants who just recommend someone else’s technology.
What type of AI project fits you?
Two questions, no score. The result is the page worth starting from.
What we build, concretely.
AI implementation in a company and AI process automation start from the same place: a discovery that maps your real processes before any technical decision. If you are not sure where to start, what an AI implementation in a company looks like, step by step is explained separately, with discovery, pilot and acceptance criteria written down before you start.
Agents that act, not just respond
An AI agent takes a task, carries it through, and asks for approval only where it matters — it doesn't just generate text in response to a question. We design agents around real processes: filings, procurement, contracts, quality checks.
The service pageThe repetitive processes, taken off people's plate
Bank reconciliation, cross-checks, resource allocation — processes that eat hours of manual work today can run automatically, with a human in the loop only for exceptions. We've automated workflows like this from scratch, with verified tests.
The service pageFrom idea to a running system
Implementing AI at a company isn't a ChatGPT subscription — it's a discovery, a pilot with acceptance criteria written beforehand, then rollout. We explain the steps, with no unfounded promises.
The service pageYour team, ready to work with AI
We took 28 employees at a manufacturing company from zero to daily use of Claude, in two days per group. The curriculum is built on Andrej Karpathy's mental models, not slogans.
The service pageAn assistant that reads your company, not the internet
We build assistants connected to your company's real data — contracts, accounting, internal documentation — that answer with a source, not a guess. The model stays a copilot: the human signs and files.
The service pageWhen your system has no API
Many systems used daily in Romania (accounting, older ERPs) don't have and won't get an API. We build agents that read the data directly or operate the existing interface, safely and with human validation.
The service pageAI connected to SAP and SAGA, without touching the core
We integrate AI on top of your existing ERP through side-by-side extensions (Clean Core) or governed MCP servers, not by modifying the core. Raw data never reaches the model directly.
The service pagePrivate AI infrastructure, on-premise or in the EU cloud.
For companies that want AI on their own data without sending it to APIs outside the EU: a platform with open-weight models running on your own servers or on a dedicated GPU at EU providers, with an interface for employees, RAG on documents, and agents connected to your ERP. We size the server to your real use cases and hand over the code and infrastructure.
The service pageProduction planning, optimized under real constraints
Production Brain combines demand simulation with mathematical optimization under constraints (capacity, shelf life, allergen changes) for a plan the human approves, not blindly executes. We pilot on one line before scaling up.
The service pageClassic automation, with an agent that decides when it's needed
Classic RPA executes fixed steps; we add an agent layer that recognizes exceptions and handles them differently, instead of letting the workflow break. Result: fewer broken workflows, fewer ignored alerts.
The service pagePaid discovery, a pilot with written criteria, then rollout.
We do not sell „digital transformation”. We sell a short discovery, a pilot measured against criteria written before we start, and a go/no-go decision made on data. Only then rollout and a retainer. A forward-deployed AI engineer works directly inside your team, on your real data and processes — unlike classic consulting, where you get a report and implementation stays your job. The four projects above are part of the full case study with FOX, with context, real problems and what concretely changed at each stage.
- 01A one-day discovery: processes, available data, friction points, an opportunity map with estimates.
- 02A 2–6 week pilot on 1–2 cases, built and tested on real data.
- 03Go/no-go decision, based on the criteria written at the start.
- 04Rollout across multiple departments, training for the team, monthly maintenance.
The same client, four projects.
An industrial manufacturer in Romania started with AI training for 28 employees, continued with a redemption platform for a campaign with 250 winners, then moved to a discovery for production digitalization on SAP and a proposal for a sovereign AI platform hosted in the EU. Not marketing figures: projects, in the order they actually happened. For the company that also asked for a sovereign AI platform, we explain separately what a sovereign AI platform, hosted in the EU, actually means, and when it actually makes sense compared with a regular external cloud.
28 employees, two groups
Fundamentals and prompt engineering, then Claude Code, Skills and MCP. Half the time is practice on their own cases.
Redemption for a campaign
Code validation, anti-fraud, fiscal data for winners, admin panel.
SAP discovery
Side-by-side production and logistics digitalization (Clean Core), with a "Production Brain" as the flagship project.
Three different ways to buy AI for your company
When you compare vendors, the biggest difference is not the price, it is who writes the code and who keeps it at the end. A forward-deployed AI engineer works inside your team, on your real processes, and the resulting code belongs entirely to the company, not to a third-party platform you rent monthly. The table below shows, briefly, the difference from the other two common ways of buying AI today: a generic chatbot vendor, or traditional strategy consulting with no code delivered. We apply that same rigorous build discipline to our own site as well, in the visibility in AI search engines chapter, not just to the systems we build for clients.
| TNS — forward-deployed engineer | Generic chatbot vendor | Traditional consulting | |
|---|---|---|---|
| Who writes the code | The engineers who run discovery, directly on your processes | An already-configured platform, not code dedicated to you | Usually no one — the deliverable is a report |
| Who owns the code at the end | The client company, entirely | The platform vendor; you stay a tenant | Not applicable, no code is delivered |
| Pricing model | Fixed price, set after a short discovery | Monthly subscription, usually per user | Hourly fee, or a fee per strategy project |
| What remains after the project | A running system, documented, and yours | Dependence on keeping the subscription active | Written recommendations; you handle implementation separately |
A fixed price, after a short discovery call.
- process and data audit
- opportunity map
- report with prioritization
- 1-2 use cases
- written acceptance criteria
- handover + documentation
- complete architecture
- team training
- monthly retainer
Every project has a different context, workflows and infrastructure, so the price is set after a short, paid discovery and does not change along the way.
Frequently asked questions
What does "agentic AI" mean?
Agentic AI means a system that doesn't just answer a question, but takes on a task, plans the steps, uses tools (search, calculation, calls to other systems), and carries the job through, asking for human approval where it matters. The difference from a regular chatbot is that the agent acts, rather than just talking.
How is AI implemented in a company?
In practice, in three steps: a one-day discovery that maps processes and data, a pilot on 1-2 use cases with acceptance criteria written before the start, then a rollout with team training and long-term maintenance. Skipping discovery is the most common reason AI projects never make it to production.
What does AI implementation actually mean?
It's the process by which an AI model or agent ends up connected to a company's real data and processes — not just used ad hoc by a few employees, but integrated with rules, permissions, and an audit trail. It includes both the technical side (connectors, agents, ERP integration) and the human side (training, internal usage policy).
How do I implement AI in my own business if I don't have a technical team?
The most realistic path is an external partner who does the discovery, builds the pilot, and hands over a documented system, with training included for your in-house team. You don't need an in-house AI department from day one — you need a first use case taken all the way through.
How much do AI agents cost for a company?
The exact figures are set after discovery.
What are AI agents?
They're software systems built on top of a language model, given tools (search, calculation, database access, API calls) and the ability to decide on their own what steps to take to carry a task through, within permissions set in advance. A good agent stops and asks for a human when it goes beyond those limits.
Who handles AI implementation at a company in Romania?
In Romania, the market is still young: most firms offering "AI consulting" actually do training or presentations, not implementation with code that reaches production. The Niche Society works as forward-deployed AI engineering: a small team that moves temporarily inside the client company, builds the system, and hands it over documented.
What does forward-deployed AI engineer mean?
A forward-deployed AI engineer is an engineer who works directly inside the client company, writing and operating code on its real processes — unlike a classic consultant, who delivers only recommendations, or a chatbot vendor, who delivers a generic, configurable product.
What is AI engineering, as a discipline, actually?
AI engineering means building and operating real AI systems — agents, automations, integrations — directly in production, not just recommending them on paper. It differs from consulting in that it delivers code that runs, not just an implementation plan.
Let's see what can be automated in your business.
A free 30-minute session: we'll tell you what can be automated, how long it takes and what it costs, with a fixed price after discovery.