AI-native engineering
AI agents
for your workflows
We build agents that work with your data, systems and regulations: read documents, answer customers, plan resources and guard quality — with measurable results and human escalation.
From kickoff to pilot in 4–8 weeks. No rip-and-replace.
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Your data stays yours
Agents run inside your perimeter: on-premise, in your cloud, or behind an API.
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Humans in the loop
The agent does not act on uncertain decisions on its own — it escalates to an operator.
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Measured impact
Every pilot starts with metrics: time, cost, share of automated work.
- 6+
- ready-made agent solutions in the kit
- 4–8
- weeks from kickoff to pilot
- 100%
- of solutions can be fine-tuned on your data
- 24/7
- agents work without weekends
The problem
Where teams usually get stuck
We start with one narrow, repetitive and expensive step in your process.
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Manual work eats time
Operators re-read the same documents and re-key the same data by hand.
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Answers get stuck in a queue
Emails, chats and tickets wait hours, while knowledge lives in people's heads.
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Knowledge is hard to find
Policies, wikis and archives are scattered — finding one answer takes days.
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Control comes too late
Customers and auditors find the mistakes, not the system, before it is too late.
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Plans live in spreadsheets
Resources, shifts and deadlines are planned manually and ignore changes.
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Engineering slows down
Reviews, migrations and docs consume the time of your most expensive engineers.
Solutions
Agents built for your processes
Ready-made solutions from our kit, adapted to your regulations and systems — or an agent built from scratch for a unique process.
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Document agent
Documents turned into structured data
Ingests invoices, contracts, waybills and specs, extracts the fields that matter, checks them against your rules and writes the result straight into the accounting system. Anything unreadable or contradictory goes to an operator with a source reference attached.
- Field extraction from scans and PDFs
- Validation against templates and reference data
- Duplicate and mismatch detection
- Manual correction with a full audit trail
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Support agent
The agent runs the first line, not a person
Replies in email, chat and messengers using your knowledge base and ticket history, checks order status in your systems and closes routine requests on its own. Complex or emotional cases are handed to a human together with the collected context.
- Answers grounded in your docs and policies
- Order status lookup in your systems
- Intent classification and routing
- Drafting the reply for an operator
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Sales agent
Leads get handled instead of lost
Triages inbound requests, enriches them with CRM data, scores priority and timing, drafts the proposal and a personalised follow-up using your templates, then keeps the deal moving to the next step.
- Lead qualification and scoring
- Proposal and personalised email drafting
- Data enrichment from open sources
- Reminders and follow-ups inside CRM
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Planning agent
Shift and capacity plans that recalculate themselves
Builds schedules against constraints, staff availability and equipment, recalculates the plan whenever anything changes and shows where you will hit short-staffing, overtime and missed deadlines — before it happens.
- Shift and capacity scheduling
- What-if analysis over constraints
- Overtime and shortage forecasting
- Plan refresh on every change
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Quality agent
Check before sending, not after the complaint
Compares documents and replies against your policies and SLAs, catches inconsistencies, tone and forbidden phrasing, verifies completeness and produces a risk report before the customer sees the result.
- Policy and SLA compliance checks
- Document completeness validation
- Tone and copy-editing of replies
- Risk and deviation reporting
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Developer agent
Reviews, migrations and docs on your own code
Reads the repository, checks changes against your conventions, prepares schema migrations and test drafts from written scenarios, then answers questions across the codebase and project documentation.
- Change review and regression search
- Schema migrations and test drafts
- Search across repo and documentation
- Doc updates driven by code changes
Approach
How we work
A transparent process with a clear result at every stage.
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01
Discovery
We map the process, the data and the metrics, then pick the step with the highest impact.
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02
Prototype
We build the agent on your own examples: real data, real cases, an honest quality report.
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03
Pilot
We run it in one team next to your people and compare metrics and cost.
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04
Run & improve
We integrate it with your systems, add monitoring, evals and retraining on fresh data.
Platform
What is inside the solution
One data layer, a model zoo, quality control and observability.
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Data & knowledge
Connectors to databases, data marts, mail, messengers and file storages. RAG over your knowledge bases.
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Models
Commercial API models, open-source models and domain fine-tuning. We pick the model per task and budget.
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Tools & actions
The agent does more than answer: it calls your API, creates tasks, updates CRM and fills documents.
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Evals & quality
Test sets built on your data, with quality tracked whenever a model is updated.
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Observability
Conversation logs, step-by-step traces, cost per request and alerts on degradation.
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Security
Isolation, access logging, personal-data masking and on-premise deployment.
FAQ
Frequently asked questions
How does a project start?
With discovery: we map the process, the data and the target metric. If the task does not fit AI, we say so at that very first stage.
Do we have to replace our current systems?
No. Agents sit on top of your existing landscape: 1C, CRM, mail, messengers, repositories. Deep integration is optional.
Where does the solution run?
Your server or cloud, our managed setup, or an API integration. For sensitive data — on-premise with nothing leaving your network.
How is quality measured?
Before the pilot we assemble evals from your real cases and record accuracy, speed and cost. Then we keep watching those metrics in production.
Who is responsible for the agent's output?
The architecture keeps a human in the loop: autonomy levels, confidence thresholds and mandatory escalation to an operator.
How much does it cost?
We estimate the pilot after discovery: data volume, integrations, deployment requirements. We calculate the total cost of ownership with you — no hidden fees.
Tell us about the task — we will propose a pilot
Describe the process you want to automate. You get back an impact estimate, a timeline and a pilot plan.
We reply within one business day.
Office
ул. Галерная, д. 20-22, лит. А, помещ. 106-Н, офис 1
Санкт-Петербург, 190098