code35
code35

ai integrations

We put AI to work — not on decoration. From integrating with your existing systems to designing custom agents, advisory to fine-tuning — end to end.

44
AI agents running
4
Service lines
10×
Productivity lift
01what we build

We work across four lines.

Each line stands alone or combines with the others. Most projects activate two or three of them. We figure out the right combination for you on the first call.

01integration

AI Integration

We add AI to your existing systems. Not a chatbot glued to your ERP — actual working agents that automate approvals, data entry, and customer communication. They slot naturally into your Hangfire queue, RabbitMQ, and the rest of your infrastructure.

TypicalAutonomous agent inside ERP/CRM, OCR pipelines, intelligent document reading
02advisory

AI Advisory

Strategic advisory before you spend on AI. Which processes can be automated, which models fit your needs, how much to invest, how to calculate ROI. Includes executive workshops and team training.

TypicalAI strategy report, process mapping, ROI model, team training
03custom agent

Custom Agent Design

Not a generic "helper" — autonomous agents trained for a specific role, equipped with their own tools, able to talk to other agents. Think of them as new members of your team. Inspired by our Tengri / Ragnar architecture, built for you.

TypicalSales assistant, code review agent, content moderator, lead generator
04fine-tuning

Model Fine-Tuning

Custom training to make general-purpose models work better in your domain. Industry-specific terminology, company voice, patterns in repetitive tasks. Open-source models or API-based fine-tuning.

TypicalDomain-specific LLM, RAG system, embedding model, eval pipeline
02industries

Sectors where we've put AI to work.

We work in every sector where AI pays off — with deep expertise in some. If you don't see your sector, talk to us anyway — most of the logic carries over.

Travel & TourismVisa ServicesSaaSFinTechE-CommerceHealthcareLogisticsEducationReal EstateManufacturingHospitalityMediaLegal TechAgricultureInsuranceEventsRetailConstruction
NoteIn AI projects, process knowledge beats sector knowledge — and that's where we specialise.
03stack

Our AI stack.

No vendor lock-in — we pick the best model, framework, and infrastructure per need. From open source to commercial APIs, small models to frontier ones.

Foundation Models
ClaudeGPTGeminiLlamaMistral
Agent Frameworks
LangChainLangGraphCrewAIAutoGenCustom orchestration
Vector & RAG
pgvectorQdrantPineconeWeaviateEmbedding models
Fine-Tuning
LoRAPEFTHugging FaceEval pipelinesCustom datasets
Infra & Ops
vLLMOllamaGPU CloudLangSmithHetzner GPU
MCP & Protocols
MCP ServerCustom MCP toolsTool callingFunction schemasA2A protocols
Safety & Eval
Prompt safetyHallucination checksOutput validationHuman-in-the-loop
04our workflow

How we run AI projects.

Different from classic software work: a small proof first (POC), then integration, then scale. Measurable results at every step — no question marks left open.

01

Discovery

On the first call we identify which processes AI can automate. We map data sources, ROI potential, technical constraints. Not everything is AI — part of our job is occasionally telling you "AI isn't needed here".

~ 1 week·Free
02

Proof of Concept

We test a single scenario with your real data at a small budget. Does the AI actually work, is quality sufficient, which model should we use — you leave with concrete answers. Scale decisions happen after POC.

2-4 weeks·Fixed price
03

Integration

If the POC lands, we integrate with your production system. ERP, CRM, work queue, webhooks, UI. The AI sticks into your existing stack; user experience stays intact.

4-8 weeks·Modular delivery
04

Fine-Tune

We train the model on your data. Your sector terminology, your company voice, the patterns in your recurring tasks. 30-60% better performance than a general model — fewer errors, lower cost.

2-6 weeks·Measured with evals
05

Deploy & Monitor

Production rollout, monitoring dashboard, alert systems. AI outputs are measured continuously — hallucination, latency, cost, user satisfaction. Any drift triggers an alert immediately.

1 week·Live + observability
06

Evolve

AI isn't a one-off install — every month brings new data, new patterns, new uses. A monthly iteration loop keeps model performance climbing. New features, new agents, new use cases get added.

Monthly loop·Retainer model
next step

Let's put AI to work.

First call is free. You describe your process, we tell you where AI actually moves the needle. Instead of promises we prove it with a concrete POC.