AI agent development and deployment for business
From a pilot scenario to an agent system in your perimeter: long-running orchestration, CRM/ERP/messenger integrations, quality gates, and ops. Not a weekend bot — infrastructure you can run under SLA.
Walk through a scenarioCoverage
Task-specific agents
Support, sales, document flows, knowledge search, 1C and e-commerce API scenarios — with measurable pilot KPIs.
Agent infrastructure
Orchestration (Temporal), queues, RAG, tool-calling, MCP connectors, prompt and schema versioning.
Security & operations
On-prem/hybrid, secrets in your vault, logging, alerts, human-in-the-loop, runbooks for L2 support.
How we work
- 1.
Workshop: one process with clear impact and autonomy boundaries.
- 2.
Architecture: models, tools, integrations, access policies, fallbacks.
- 3.
Pilot in staging; regression and load tests when needed.
- 4.
Production rollout, monitoring, iterations, white-label for partners.
Deliverables
- Working agents and Git-hosted code — no no-code vendor lock-in.
- System diagram: agents, tools, data flows, control points.
- Quality, LLM cost, and scenario SLA dashboards.
- Docs for DevOps and business process owners.
Estimates
Single-scenario pilots from a short sprint; corporate agent platforms are longer engagements. Quote after workshop.
Pairs well with automation, high-load, and on-prem when the perimeter is closed or regulated.
AI agents FAQ
An agent runs action chains: calls APIs, waits for events, decides within policy. Chatbots are mostly dialog-bound.
Matched to task and perimeter: GigaChat, YandexGPT, local LLMs, foreign APIs where security policy allows.
Yes — on-prem or hybrid: local model for routine tasks, cloud for complex ones.
RAG on your data, strict tool schemas, human-in-the-loop on critical steps, eval sets before release.
Yes — delivery under your brand, NDA, fixed support SLAs.
One-scenario pilot — typically 2–4 weeks after scope sign-off.
