AI solutions —measurable value
LLM agents, document processing and automation — a measurable result in one process first, then expansion. Because we use AI ourselves every day, we know what works.
Our approach
Most AI projects start with the sentence “we need AI” and end with nothing, because they start from the technology, not from the problem. We go the other way: first we find the process where your team loses the most time — answering customer requests, reading documents, compiling reports — and then we set a measurable target for that process.
In the first tier, one process is automated and the result is measured in numbers: how many times shorter the response time is, how many hours were saved. This approach both reduces risk and builds trust in AI inside the company. When the pilot succeeds, expansion happens on an already proven model.
Our difference: agentic development is our own internal way of working — we work with AI agents every day. The solution we sell you has first been tested on ourselves: where it is strong, where it needs oversight, how costs are managed.
What's included
Internal knowledge assistants
A RAG-based assistant over company documents, policies and history — employees get answers without searching through files.
Customer service agents
Agents that answer most requests automatically and hand complex ones to a human. Response time goes from minutes to seconds.
Document processing
Contracts, invoices, applications — structured data extracted from scanned documents and written into your systems automatically.
Workflow automation
Agent-based automation of repetitive processes (reporting, email handling, data transfer).
Model evaluation and cost control
Which model for which job — the quality/cost balance, continuous measurement of answer quality, management of API costs.
How it changes withscale
The same service at three different depths. We choose together which tier to start from during the scale conversation.
One process, one team, a measurable target. A working agent on simple infrastructure — the result is proven in numbers.
The pilot spreads to other departments. Answer-quality monitoring, usage analytics and cost control are put in place.
AI becomes part of the company's core systems: CRM, ERP, service desk. Multi-agent flows, audit trail and access control.
We use what we sell
At SOFIXAR, code is written with agents, documents are processed by agents, tests are built with agents. The experience that comes to your project is not theory — it is methods checked every day in our own production.
1
process to start — we measure, then expand
Deliverables
- A working agent / automation — in production
- Knowledge base and RAG infrastructure
- Quality measurement dashboard (answer accuracy, usage, cost)
- Human oversight flow — critical decisions go through approval
- API cost limits and alerts
- Usage training for your team
Frequently asked questions
- Does our data go into model training?
- No. We work with business-contract APIs — your data is not used for training. In sensitive areas we add a data anonymization layer, and if you wish we evaluate a local model option.
- What happens if the AI gives a wrong answer?
- Critical decisions never run fully automatically — we build a human approval flow. In addition, answer quality is measured continuously: when accuracy drops, the system raises an alert and we step in.
- How much will the monthly API cost be?
- During the pilot we measure it with real usage and give you a forecast. Cost limits and cheaper-model fallbacks are standard — there are no surprise invoices.
Next step
Let's build your product*together*
In a forty-minute meeting we talk through your scale: which tier it makes sense to start at, how much time and budget it takes, and what we deliberately defer. No obligation.
