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An AI team of a new kind
2026 · available

An AI team of a new kind for your products — apps and services in days, not months.

A distributed team of AI agents under unified orchestration: analysts, architects, developers, testers and ops engineers — in one contour. Full cycle from research and architecture to testing, deployment and maintenance — team speed without the overhead.

agent-build · project buildlive
modulesdata flowsR A C V T D · agents01 · research · 0%
Brief to MVP
2 wks
typical
Services in prod
0+
shipped
Agents in the team
0
specialised
Vs a usual team
×0
delivery speed

“We don't hire people for a task — we assemble a team of AI agents for it and run it under unified orchestration. That is why launches take weeks, not quarters.”

§ 01

What we do

4 directions

From a single automation to a turnkey app. We own the task end to end — from framing and prototype to production deployment, testing and maintenance.

01

AI in your workflows

Process audit, finding automation points, a fast pilot on real data and an impact estimate before scaling.

auditpilotROI
02

Turnkey apps & services

Building a product from prototype to production: backend, interface, inference, monitoring and operations.

MVPprodmonitoring
03

Agents & automation

AI agent pipelines for routine: document processing, support, reporting, research, quality control.

multi-agentpipelinesworkflow
04

Integrations & RAG

Connecting LLMs to data and systems: knowledge bases, RAG, voice, documents, vision, internal API links.

RAGLLM-APIvision · voice
§ 02

Agent team

Unified orchestration

Not a “developer with ChatGPT” but a coordinated team of specialised agents. Each owns a stage, while the orchestration keeps the architecture, quality and accountability.

Fig. 1 · Agent pipelineUnified orchestration · 6 stages
00 · coreOrchestratorBriefs every agent, keeps the architecture and quality, and owns the result.
01 · researchResearchRequirements, domain analysis, choosing the approach.
02 · architectArchitectureDesign, stack selection, breakdown into steps.
03 · coderBuildCode generation per module — in parallel.
04 · reviewCode reviewRefactoring, security, standards compliance.
05 · qaTestsScenarios, runs, capturing and reproducing defects.
06 · devopsDeploymentContainerisation, CI/CD, production and maintenance.
Flow: brief → prodStages 01 → 06 run sequentially
§ 03

How a project runs

Brief to prod

A transparent rhythm with short iterations. You see working results in the first week — not a deck a month later.

Day 0
Brief & goal

We unpack the task, success metric and constraints. Scope locked.

Day 1–2
Prototype

A working prototype on your data — we test the hypothesis for real.

Week 1
MVP

A service with the key scenario, interface and basic monitoring.

Week 2
Production

Deployment, load checks, documentation and handover.

Next
Maintenance

Support, improvements and scaling driven by feedback.

§ 04

Cases

Selected

A few projects assembled by our team of agents. The structure and metrics are a reference for your scenario.

RAGscreenshot · 16:10
Case 012 wks

Knowledge-base assistant

Search and answers across 12,000 internal documents with source links and hallucination control.

RAGFastAPIpgvector
−70%search time
94%accuracy
Agentsscreenshot · 16:10
Case 0210 days

Ticket processing pipeline

Classification, enrichment and routing of inbound requests by a fleet of agents with no operator in the loop.

multi-agentqueuewebhook
×6throughput
24/7no pauses
Voicescreenshot · 16:10
Case 033 wks

Voice assistant

A phone bot with speech recognition, scenarios and handover of complex dialogues to a live operator.

STT · TTSrealtimeSIP
62%auto-answered
1.2slatency
§ 05

Stack

Tools

We pick tools for the task, but lean on a proven set for speed and reliability.

Models & agents
LLM-API · open modelsAgent orchestrationRAG · vector searchFunction calling
Backend
Python · FastAPINode.jsPostgreSQL · pgvectorRedis · queues
Frontend
TypeScript · ReactNext.jsTailwindWebSocket · streaming
Data & infra
Docker · CI/CDCloud · serverlessObservabilityVector DBs
§ 06

Let us talk about your project

Reply within a day

Have an AI service idea or a process that is ready to automate?

Describe the task in two sentences — we'll come back with a timeline estimate and an approach. The first prototype usually ships within a week.